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feat(intelligence): Revenue Intelligence OS — Lead Machine complete

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ADDED MODULES:
- intelligence/icp.py: ICP Builder — 34 Saudi industries, Arabic+English queries
- intelligence/discovery.py: Multi-source discovery — web search + 34 seed companies
- intelligence/enrichment.py: Company/person enrichment — website data + news
- intelligence/scoring.py: 5-dimension scoring — Fit/Intent/Access/Value/Urgency
- intelligence/entity_resolution.py: Arabic/English dedup + fuzzy matching
- intelligence/outreach.py: Arabic-first outreach — WhatsApp/Email/LinkedIn
- intelligence/triggers.py: Trigger alerts — funding/hiring/expansion/IPO
- intelligence/pipeline.py: End-to-end orchestrator — ICP→Discovery→Score→Brief
- routes/intelligence.py: 15 REST endpoints + audit chain integration
- DB: 5 new tables — intelligence_leads/runs/watchlist/triggers/entities

ARCHITECTURE:
- Layer 1: Signal collection (web + curated Saudi B2B DB of 34 companies)
- Layer 2: Enrichment (website data, news, tech stack detection)
- Layer 3: 5D scoring — Master = 0.30 Fit + 0.25 Intent + 0.15 Access + 0.20 Value + 0.10 Urgency
- Layer 4: Entity resolution — Arabic/English fuzzy dedup
- Layer 5: Outreach brief — signal-driven WhatsApp/Email/LinkedIn in Arabic

MOTION SUPPORT: B2B sales | partnership | channel | tender
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salesflow-saas/backend/app/api/routes/intelligence.py Normal file
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"""
Revenue Intelligence OS — Lead Machine API
Endpoints for ICP, Discovery, Enrichment, Scoring, Outreach, Triggers
"""
import uuid
import json
import time
from flask import Blueprint, request, jsonify
from app.core.database import db
from app.api.routes.auth import require_auth
from app.core.audit import log as audit_log
from app.intelligence.icp import ICPConfig, DEALIX_DEFAULT_ICP
from app.intelligence.pipeline import run_pipeline
from app.intelligence.triggers import scan_watchlist, scan_company_for_triggers
from app.intelligence.outreach import generate_outreach_brief
from app.intelligence.scoring import score_lead
from app.intelligence.enrichment import enrich_candidate, EnrichedLead
intelligence_bp = Blueprint("intelligence", __name__, url_prefix="/api/intelligence")
def _json(data, status=200):
return jsonify(data), status
# ─── ICP MANAGEMENT ─────────────────────────────────────────────────────────
@intelligence_bp.get("/icp")
@require_auth
def get_icp(user):
"""Get active ICP config for org"""
with db() as conn:
row = conn.execute(
"SELECT * FROM icp_configs WHERE org_id=? AND is_active=1 ORDER BY created_at DESC LIMIT 1",
(user["org_id"],)
).fetchone()
if row:
config = json.loads(row["config"])
return _json({"icp": config, "id": row["id"], "name": row["name"]})
# Return default ICP
return _json({"icp": DEALIX_DEFAULT_ICP.to_dict(), "id": "default", "name": "Dealix Default ICP"})
@intelligence_bp.post("/icp")
@require_auth
def create_icp(user):
if user["role"] not in ("manager", "admin"):
return _json({"error": "Forbidden"}, 403)
"""Create or update ICP config"""
data = request.get_json() or {}
icp_id = str(uuid.uuid4())
# Deactivate existing
with db() as conn:
conn.execute("UPDATE icp_configs SET is_active=0 WHERE org_id=?", (user["org_id"],))
conn.execute("""
INSERT INTO icp_configs (id, org_id, name, config, is_active, created_by)
VALUES (?, ?, ?, ?, 1, ?)
""", (icp_id, user["org_id"], data.get("name", "Custom ICP"), json.dumps(data), user["id"]))
audit_log(user["org_id"], "intelligence", "icp_created", user["id"], icp_id, data)
return _json({"id": icp_id, "message": "ICP saved"}, 201)
# ─── PIPELINE ────────────────────────────────────────────────────────────────
@intelligence_bp.post("/pipeline/run")
@require_auth
def run_lead_pipeline(user):
if user["role"] not in ("manager", "admin"):
return _json({"error": "Forbidden"}, 403)
"""
Trigger full lead intelligence pipeline.
Body (all optional):
custom_queries: list[str]
motion: sales | partnership | channel | tender
max_leads: int (default 30)
enrich: bool (default true)
generate_outreach: bool (default true)
"""
data = request.get_json() or {}
motion = data.get("motion", "sales")
max_leads = min(int(data.get("max_leads", 30)), 100)
enrich = data.get("enrich", True)
gen_outreach = data.get("generate_outreach", True)
custom_queries = data.get("custom_queries", None)
run_id = f"run-{uuid.uuid4().hex[:12]}"
# Load ICP from DB if available
with db() as conn:
icp_row = conn.execute(
"SELECT config FROM icp_configs WHERE org_id=? AND is_active=1 LIMIT 1",
(user["org_id"],)
).fetchone()
icp = None
if icp_row:
try:
cfg = json.loads(icp_row["config"])
icp = ICPConfig(**{k: v for k, v in cfg.items() if k in ICPConfig.__dataclass_fields__})
except Exception:
icp = DEALIX_DEFAULT_ICP
else:
icp = DEALIX_DEFAULT_ICP
# Record run start
with db() as conn:
conn.execute("""
INSERT INTO intelligence_runs (id, org_id, run_mode, motion, status, created_by)
VALUES (?, ?, 'manual', ?, 'running', ?)
""", (run_id, user["org_id"], motion, user["id"]))
try:
result = run_pipeline(
icp=icp,
custom_queries=custom_queries,
motion=motion,
max_leads=max_leads,
enrich=enrich,
generate_outreach=gen_outreach,
)
result["run_id"] = run_id
# Persist scored leads to DB
with db() as conn:
for item in result.get("scored_leads", []):
lead = item["lead"]
score = item["score"]
lid = lead.get("id", str(uuid.uuid4()))
conn.execute("""
INSERT OR REPLACE INTO intelligence_leads (
id, org_id, company_name, domain, industry, region, company_size,
description, website, tech_stack, signals, recent_news,
contact_name, contact_title, contact_email, contact_phone, contact_linkedin,
decision_maker_score, enrichment_source, enrichment_confidence,
source, source_url, raw_snippet, trigger,
score_fit, score_intent, score_access, score_value, score_urgency,
score_master, priority_tier, score_reasons, next_action, next_action_ar,
pipeline_run_id, enriched_at
) VALUES (
?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?
)
""", (
lid, user["org_id"],
lead.get("company_name", ""), lead.get("domain", ""),
lead.get("industry", ""), lead.get("region", ""),
lead.get("company_size", "unknown"),
lead.get("description", ""), lead.get("website", ""),
json.dumps(lead.get("tech_stack", [])),
json.dumps(lead.get("signals", [])),
json.dumps(lead.get("recent_news", [])),
lead.get("contact_name", ""), lead.get("contact_title", ""),
lead.get("contact_email", ""), lead.get("contact_phone", ""),
lead.get("contact_linkedin", ""),
lead.get("decision_maker_score", 0),
lead.get("enrichment_source", "web"),
lead.get("enrichment_confidence", 0.5),
lead.get("source", ""), lead.get("source_url", ""),
lead.get("raw_snippet", ""), lead.get("trigger", ""),
score.get("fit", 0), score.get("intent", 0),
score.get("access", 0), score.get("value", 0),
score.get("urgency", 0), score.get("master", 0),
score.get("tier", "P4"),
json.dumps(score.get("reasons", [])),
score.get("next_action", ""), score.get("next_action_ar", ""),
run_id, lead.get("enriched_at", ""),
))
# Update run record
ts = result.get("tier_summary", {})
conn.execute("""
UPDATE intelligence_runs SET
total_discovered=?, total_deduped=?, total_enriched=?,
tier_p1=?, tier_p2=?, tier_p3=?, tier_p4=?,
duration_sec=?, status='complete'
WHERE id=?
""", (
result.get("total_discovered", 0),
result.get("total_after_dedup", 0),
result.get("total_enriched", 0),
ts.get("P1_outreach_now", 0), ts.get("P2_enrich_more", 0),
ts.get("P3_nurture", 0), ts.get("P4_archive", 0),
result.get("pipeline_duration_sec", 0),
run_id,
))
audit_log(user["org_id"], "intelligence", "pipeline_run", user["id"], run_id,
{"motion": motion, "total": result.get("total_enriched", 0)})
# Return summary (not full scored list — too large)
return _json({
"run_id": run_id,
"total_discovered": result["total_discovered"],
"total_after_dedup": result["total_after_dedup"],
"total_enriched": result["total_enriched"],
"tier_summary": result["tier_summary"],
"pipeline_duration_sec": result["pipeline_duration_sec"],
"p1_leads": result["p1_leads"][:10],
"outreach_briefs": result["outreach_briefs"][:5],
})
except Exception as e:
with db() as conn:
conn.execute(
"UPDATE intelligence_runs SET status='error', error_message=? WHERE id=?",
(str(e)[:500], run_id)
)
return _json({"error": str(e), "run_id": run_id}, 500)
# ─── LEAD MANAGEMENT ─────────────────────────────────────────────────────────
@intelligence_bp.get("/leads")
@require_auth
def list_intelligence_leads(user):
"""List discovered leads with filters"""
tier = request.args.get("tier") # P1|P2|P3|P4
status = request.args.get("status") # discovered|contacted|qualified|archived
sort = request.args.get("sort", "score") # score|date
limit = min(int(request.args.get("limit", 50)), 200)
offset = int(request.args.get("offset", 0))
conditions = ["org_id=?"]
params = [user["org_id"]]
if tier:
conditions.append("priority_tier=?")
params.append(tier)
if status:
conditions.append("status=?")
params.append(status)
order = "score_master DESC" if sort == "score" else "created_at DESC"
where = " AND ".join(conditions)
with db() as conn:
rows = conn.execute(
f"SELECT * FROM intelligence_leads WHERE {where} ORDER BY {order} LIMIT ? OFFSET ?",
params + [limit, offset]
).fetchall()
total = conn.execute(
f"SELECT COUNT(*) FROM intelligence_leads WHERE {where}", params
).fetchone()[0]
leads = []
for row in rows:
lead = dict(row)
for field in ["tech_stack", "signals", "recent_news", "score_reasons"]:
try:
lead[field] = json.loads(lead[field] or "[]")
except Exception:
lead[field] = []
leads.append(lead)
return _json({"leads": leads, "total": total, "limit": limit, "offset": offset})
@intelligence_bp.get("/leads/<lead_id>")
@require_auth
def get_intelligence_lead(user, lead_id):
"""Get a single intelligence lead"""
with db() as conn:
row = conn.execute(
"SELECT * FROM intelligence_leads WHERE id=? AND org_id=?",
(lead_id, user["org_id"])
).fetchone()
if not row:
return _json({"error": "Lead not found"}, 404)
lead = dict(row)
for field in ["tech_stack", "signals", "recent_news", "score_reasons"]:
try:
lead[field] = json.loads(lead[field] or "[]")
except Exception:
lead[field] = []
return _json(lead)
@intelligence_bp.patch("/leads/<lead_id>/status")
@require_auth
def update_lead_status(user, lead_id):
"""Update lead status — contacted | qualified | archived"""
data = request.get_json() or {}
new_status = data.get("status")
if new_status not in ("discovered", "contacted", "qualified", "archived"):
return _json({"error": "Invalid status"}, 400)
with db() as conn:
conn.execute("""
UPDATE intelligence_leads SET status=?, reviewed_by=?, reviewed_at=datetime('now')
WHERE id=? AND org_id=?
""", (new_status, user["id"], lead_id, user["org_id"]))
audit_log(user["org_id"], "intelligence", f"lead_status_{new_status}", user["id"], lead_id)
return _json({"id": lead_id, "status": new_status})
@intelligence_bp.post("/leads/<lead_id>/push-to-crm")
@require_auth
def push_lead_to_crm(user, lead_id):
"""Push an intelligence lead to the CRM leads table"""
with db() as conn:
il = conn.execute(
"SELECT * FROM intelligence_leads WHERE id=? AND org_id=?",
(lead_id, user["org_id"])
).fetchone()
if not il:
return _json({"error": "Lead not found"}, 404)
crm_id = str(uuid.uuid4())
conn.execute("""
INSERT INTO leads (id, org_id, company_name, contact_name, contact_email,
contact_phone, source, industry, company_size, region, status, score,
stage, enriched_data)
VALUES (?, ?, ?, ?, ?, ?, 'intelligence', ?, ?, ?, 'new', ?, 'intake', ?)
""", (
crm_id, user["org_id"],
il["company_name"], il["contact_name"] or "",
il["contact_email"] or "", il["contact_phone"] or "",
il["industry"] or "", il["company_size"] or "",
il["region"] or "", il["score_master"],
json.dumps({
"signals": json.loads(il["signals"] or "[]"),
"domain": il["domain"],
"description": il["description"],
"score_breakdown": {
"fit": il["score_fit"], "intent": il["score_intent"],
"access": il["score_access"], "value": il["score_value"],
"urgency": il["score_urgency"],
}
})
))
conn.execute(
"UPDATE intelligence_leads SET crm_lead_id=?, status='qualified' WHERE id=?",
(crm_id, lead_id)
)
audit_log(user["org_id"], "intelligence", "lead_pushed_to_crm", user["id"], lead_id,
{"crm_lead_id": crm_id})
return _json({"lead_id": lead_id, "crm_lead_id": crm_id, "message": "Pushed to CRM"}, 201)
# ─── OUTREACH ────────────────────────────────────────────────────────────────
@intelligence_bp.post("/outreach/generate")
@require_auth
def generate_outreach(user):
"""
Generate outreach brief for a single lead.
Body: { lead_id, motion? }
"""
data = request.get_json() or {}
lead_id = data.get("lead_id")
motion = data.get("motion", "sales")
with db() as conn:
row = conn.execute(
"SELECT * FROM intelligence_leads WHERE id=? AND org_id=?",
(lead_id, user["org_id"])
).fetchone()
if not row:
return _json({"error": "Lead not found"}, 404)
lead = dict(row)
for field in ["tech_stack", "signals", "recent_news"]:
try:
lead[field] = json.loads(lead[field] or "[]")
except Exception:
lead[field] = []
score_dict = {
"fit": lead.get("score_fit", 0), "intent": lead.get("score_intent", 0),
"access": lead.get("score_access", 0), "value": lead.get("score_value", 0),
"urgency": lead.get("score_urgency", 0), "master": lead.get("score_master", 0),
"tier": lead.get("priority_tier", "P3"),
}
brief = generate_outreach_brief(lead, score_dict, motion)
# Save outreach back to lead
with db() as conn:
conn.execute("""
UPDATE intelligence_leads SET
outreach_whatsapp_ar=?, outreach_email_subject_ar=?,
outreach_email_body_ar=?, outreach_linkedin_ar=?, outreach_angle=?
WHERE id=?
""", (
brief.whatsapp_ar, brief.email_subject_ar, brief.email_body_ar,
brief.linkedin_ar, brief.angle, lead_id
))
audit_log(user["org_id"], "intelligence", "outreach_generated", user["id"], lead_id)
return _json({
"lead_id": lead_id,
"company": brief.company_name,
"angle": brief.angle,
"whatsapp_ar": brief.whatsapp_ar,
"email_subject_ar": brief.email_subject_ar,
"email_body_ar": brief.email_body_ar,
"email_subject_en": brief.email_subject_en,
"email_body_en": brief.email_body_en,
"linkedin_ar": brief.linkedin_ar,
"personalization_score": brief.personalization_score,
})
# ─── WATCHLIST & TRIGGERS ────────────────────────────────────────────────────
@intelligence_bp.get("/watchlist")
@require_auth
def get_watchlist(user):
with db() as conn:
rows = conn.execute(
"SELECT * FROM intelligence_watchlist WHERE org_id=? AND active=1 ORDER BY priority DESC",
(user["org_id"],)
).fetchall()
return _json({"watchlist": [dict(r) for r in rows]})
@intelligence_bp.post("/watchlist")
@require_auth
def add_to_watchlist(user):
data = request.get_json() or {}
wid = str(uuid.uuid4())
with db() as conn:
conn.execute("""
INSERT INTO intelligence_watchlist (id, org_id, company_name, domain, priority, added_by)
VALUES (?, ?, ?, ?, ?, ?)
""", (wid, user["org_id"], data.get("company_name", ""),
data.get("domain", ""), data.get("priority", 0), user["id"]))
return _json({"id": wid, "message": "Added to watchlist"}, 201)
@intelligence_bp.delete("/watchlist/<wid>")
@require_auth
def remove_from_watchlist(user, wid):
with db() as conn:
conn.execute(
"UPDATE intelligence_watchlist SET active=0 WHERE id=? AND org_id=?",
(wid, user["org_id"])
)
return _json({"id": wid, "message": "Removed from watchlist"})
@intelligence_bp.post("/triggers/scan")
@require_auth
def scan_triggers(user):
if user["role"] not in ("manager", "admin"):
return _json({"error": "Forbidden"}, 403)
"""
Scan watchlist companies for trigger events.
Body: { company_names?: list[str] }
"""
data = request.get_json() or {}
company_names = data.get("company_names")
if not company_names:
with db() as conn:
rows = conn.execute(
"SELECT company_name FROM intelligence_watchlist WHERE org_id=? AND active=1",
(user["org_id"],)
).fetchall()
company_names = [r["company_name"] for r in rows]
if not company_names:
return _json({"message": "No companies to scan", "triggers": {}})
# Limit to 5 companies per manual scan
company_names = company_names[:5]
trigger_results = scan_watchlist(company_names)
# Persist triggers
now = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
with db() as conn:
for company, events in trigger_results.items():
for event in events:
tid = str(uuid.uuid4())
conn.execute("""
INSERT INTO intelligence_triggers (
id, org_id, company_name, trigger_type, trigger_label_ar,
signal_strength, evidence, source_url,
recommended_action_ar, recommended_action_en
) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
""", (
tid, user["org_id"], company,
event["type"], event["label_ar"],
event["strength"], event["evidence"][:500],
event["url"][:300],
event["action_ar"], event["action_en"],
))
audit_log(user["org_id"], "intelligence", "triggers_scanned", user["id"],
f"watchlist-{len(company_names)}", {"companies": company_names})
return _json({
"companies_scanned": len(company_names),
"triggers_found": sum(len(v) for v in trigger_results.values()),
"results": trigger_results,
})
@intelligence_bp.get("/triggers")
@require_auth
def list_triggers(user):
with db() as conn:
rows = conn.execute(
"""SELECT * FROM intelligence_triggers WHERE org_id=?
ORDER BY signal_strength DESC, detected_at DESC LIMIT 50""",
(user["org_id"],)
).fetchall()
return _json({"triggers": [dict(r) for r in rows]})
# ─── RUNS HISTORY ────────────────────────────────────────────────────────────
@intelligence_bp.get("/runs")
@require_auth
def list_runs(user):
with db() as conn:
rows = conn.execute(
"SELECT * FROM intelligence_runs WHERE org_id=? ORDER BY created_at DESC LIMIT 20",
(user["org_id"],)
).fetchall()
return _json({"runs": [dict(r) for r in rows]})
# ─── DASHBOARD SUMMARY ───────────────────────────────────────────────────────
@intelligence_bp.get("/dashboard")
@require_auth
def intelligence_dashboard(user):
"""Intelligence OS overview — stats for the frontend dashboard"""
with db() as conn:
total = conn.execute(
"SELECT COUNT(*) FROM intelligence_leads WHERE org_id=?", (user["org_id"],)
).fetchone()[0]
tiers = conn.execute(
"""SELECT priority_tier, COUNT(*) as cnt FROM intelligence_leads
WHERE org_id=? GROUP BY priority_tier""",
(user["org_id"],)
).fetchall()
top_leads = conn.execute(
"""SELECT company_name, score_master, priority_tier, signals,
contact_email, next_action_ar, outreach_angle, status
FROM intelligence_leads WHERE org_id=?
ORDER BY score_master DESC LIMIT 10""",
(user["org_id"],)
).fetchall()
trigger_count = conn.execute(
"SELECT COUNT(*) FROM intelligence_triggers WHERE org_id=? AND is_actioned=0",
(user["org_id"],)
).fetchone()[0]
runs = conn.execute(
"""SELECT COUNT(*) as total, MAX(created_at) as last_run
FROM intelligence_runs WHERE org_id=?""",
(user["org_id"],)
).fetchone()
tier_breakdown = {r["priority_tier"]: r["cnt"] for r in tiers}
top = []
for row in top_leads:
lead = dict(row)
try:
lead["signals"] = json.loads(lead["signals"] or "[]")
except Exception:
lead["signals"] = []
top.append(lead)
return _json({
"total_leads": total,
"tier_breakdown": {
"P1_outreach_now": tier_breakdown.get("P1", 0),
"P2_enrich_more": tier_breakdown.get("P2", 0),
"P3_nurture": tier_breakdown.get("P3", 0),
"P4_archive": tier_breakdown.get("P4", 0),
},
"unactioned_triggers": trigger_count,
"pipeline_runs": runs["total"] if runs else 0,
"last_run": runs["last_run"] if runs else None,
"top_leads": top,
})

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salesflow-saas/backend/app/core/database.py Normal file
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"""Dealix Database Core — SQLite with full schema for 9 OS modules"""
import sqlite3
import hashlib
import json
import time
from contextlib import contextmanager
from pathlib import Path
DB_PATH = Path(__file__).parent.parent.parent / "dealix.db"
def get_connection():
conn = sqlite3.connect(str(DB_PATH), check_same_thread=False)
conn.row_factory = sqlite3.Row
conn.execute("PRAGMA journal_mode=DELETE")
conn.execute("PRAGMA foreign_keys=ON")
return conn
@contextmanager
def db():
conn = get_connection()
try:
yield conn
conn.commit()
except Exception:
conn.rollback()
raise
finally:
conn.close()
def init_db():
with db() as conn:
conn.executescript("""
-- Users & Auth
CREATE TABLE IF NOT EXISTS users (
id TEXT PRIMARY KEY,
email TEXT UNIQUE NOT NULL,
name TEXT NOT NULL,
role TEXT NOT NULL DEFAULT 'sales',
org_id TEXT NOT NULL DEFAULT 'dealix',
password_hash TEXT NOT NULL,
created_at TEXT DEFAULT (datetime('now')),
updated_at TEXT DEFAULT (datetime('now'))
);
-- ============================================================
-- 1. REVENUE OS
-- ============================================================
CREATE TABLE IF NOT EXISTS leads (
id TEXT PRIMARY KEY,
org_id TEXT NOT NULL,
company_name TEXT NOT NULL,
contact_name TEXT,
contact_email TEXT,
contact_phone TEXT,
source TEXT DEFAULT 'website',
industry TEXT,
company_size TEXT,
annual_revenue TEXT,
region TEXT,
status TEXT DEFAULT 'new',
score INTEGER DEFAULT 0,
stage TEXT DEFAULT 'intake',
assigned_to TEXT,
notes TEXT,
enriched_data TEXT,
created_at TEXT DEFAULT (datetime('now')),
updated_at TEXT DEFAULT (datetime('now'))
);
CREATE TABLE IF NOT EXISTS deals (
id TEXT PRIMARY KEY,
org_id TEXT NOT NULL,
lead_id TEXT,
title TEXT NOT NULL,
value REAL DEFAULT 0,
currency TEXT DEFAULT 'SAR',
stage TEXT DEFAULT 'discovery',
probability INTEGER DEFAULT 0,
close_date TEXT,
owner_id TEXT,
account_id TEXT,
notes TEXT,
created_at TEXT DEFAULT (datetime('now')),
updated_at TEXT DEFAULT (datetime('now'))
);
CREATE TABLE IF NOT EXISTS accounts (
id TEXT PRIMARY KEY,
org_id TEXT NOT NULL,
company_name TEXT NOT NULL,
industry TEXT,
tier TEXT DEFAULT 'standard',
arr REAL DEFAULT 0,
health_score INTEGER DEFAULT 75,
csm_id TEXT,
created_at TEXT DEFAULT (datetime('now'))
);
-- ============================================================
-- 2. PRICING & MARGIN CONTROL OS
-- ============================================================
CREATE TABLE IF NOT EXISTS quotes (
id TEXT PRIMARY KEY,
org_id TEXT NOT NULL,
deal_id TEXT,
account_id TEXT,
line_items TEXT,
subtotal REAL DEFAULT 0,
discount_pct REAL DEFAULT 0,
discount_reason TEXT,
final_price REAL DEFAULT 0,
margin_pct REAL DEFAULT 0,
approval_status TEXT DEFAULT 'pending',
approved_by TEXT,
approved_at TEXT,
valid_until TEXT,
created_by TEXT,
created_at TEXT DEFAULT (datetime('now'))
);
CREATE TABLE IF NOT EXISTS discount_policies (
id TEXT PRIMARY KEY,
org_id TEXT NOT NULL,
max_discount_pct REAL NOT NULL,
approver_role TEXT NOT NULL,
deal_value_min REAL DEFAULT 0,
deal_value_max REAL,
active INTEGER DEFAULT 1
);
-- ============================================================
-- 3. PARTNERSHIP & ALLIANCE OS
-- ============================================================
CREATE TABLE IF NOT EXISTS partners (
id TEXT PRIMARY KEY,
org_id TEXT NOT NULL,
company_name TEXT NOT NULL,
partner_type TEXT DEFAULT 'reseller',
status TEXT DEFAULT 'prospect',
fit_score INTEGER DEFAULT 0,
revenue_contribution REAL DEFAULT 0,
health_score INTEGER DEFAULT 75,
contact_name TEXT,
contact_email TEXT,
notes TEXT,
created_at TEXT DEFAULT (datetime('now')),
updated_at TEXT DEFAULT (datetime('now'))
);
CREATE TABLE IF NOT EXISTS alliance_workflows (
id TEXT PRIMARY KEY,
org_id TEXT NOT NULL,
partner_id TEXT NOT NULL,
stage TEXT DEFAULT 'scouting',
economics_model TEXT,
term_sheet TEXT,
approval_status TEXT DEFAULT 'pending',
approved_by TEXT,
activation_date TEXT,
created_at TEXT DEFAULT (datetime('now'))
);
-- ============================================================
-- 4. PROCUREMENT / VENDOR OS
-- ============================================================
CREATE TABLE IF NOT EXISTS vendors (
id TEXT PRIMARY KEY,
org_id TEXT NOT NULL,
vendor_name TEXT NOT NULL,
category TEXT,
risk_level TEXT DEFAULT 'medium',
spend REAL DEFAULT 0,
health_score INTEGER DEFAULT 75,
contract_expiry TEXT,
created_at TEXT DEFAULT (datetime('now'))
);
CREATE TABLE IF NOT EXISTS procurement_requests (
id TEXT PRIMARY KEY,
org_id TEXT NOT NULL,
vendor_id TEXT,
title TEXT NOT NULL,
amount REAL NOT NULL,
justification TEXT,
status TEXT DEFAULT 'draft',
approval_status TEXT DEFAULT 'pending',
approved_by TEXT,
approved_at TEXT,
created_by TEXT,
created_at TEXT DEFAULT (datetime('now'))
);
-- ============================================================
-- 5. RENEWAL & EXPANSION OS
-- ============================================================
CREATE TABLE IF NOT EXISTS renewals (
id TEXT PRIMARY KEY,
org_id TEXT NOT NULL,
account_id TEXT NOT NULL,
current_arr REAL DEFAULT 0,
renewal_date TEXT,
churn_risk_score INTEGER DEFAULT 0,
expansion_score INTEGER DEFAULT 0,
status TEXT DEFAULT 'upcoming',
rescue_play_active INTEGER DEFAULT 0,
assigned_to TEXT,
notes TEXT,
created_at TEXT DEFAULT (datetime('now'))
);
-- ============================================================
-- 6. EXPANSION / MARKET ENTRY OS
-- ============================================================
CREATE TABLE IF NOT EXISTS market_entries (
id TEXT PRIMARY KEY,
org_id TEXT NOT NULL,
market_name TEXT NOT NULL,
segment TEXT,
readiness_score INTEGER DEFAULT 0,
status TEXT DEFAULT 'scanning',
gtm_plan TEXT,
launch_date TEXT,
stop_loss_triggered INTEGER DEFAULT 0,
actual_vs_forecast TEXT,
created_at TEXT DEFAULT (datetime('now'))
);
-- ============================================================
-- 7. M&A / CORPORATE DEVELOPMENT OS
-- ============================================================
CREATE TABLE IF NOT EXISTS ma_targets (
id TEXT PRIMARY KEY,
org_id TEXT NOT NULL,
target_name TEXT NOT NULL,
industry TEXT,
estimated_value REAL,
fit_score INTEGER DEFAULT 0,
stage TEXT DEFAULT 'screening',
dd_findings TEXT,
valuation_memo TEXT,
synergy_model TEXT,
ic_pack_status TEXT DEFAULT 'pending',
board_pack_ready INTEGER DEFAULT 0,
close_date TEXT,
created_at TEXT DEFAULT (datetime('now'))
);
-- ============================================================
-- 8. PMI / STRATEGIC PMO OS
-- ============================================================
CREATE TABLE IF NOT EXISTS pmo_projects (
id TEXT PRIMARY KEY,
org_id TEXT NOT NULL,
title TEXT NOT NULL,
type TEXT DEFAULT 'pmi',
status TEXT DEFAULT 'active',
day1_readiness INTEGER DEFAULT 0,
plan_30_60_90 TEXT,
synergy_target REAL DEFAULT 0,
synergy_realized REAL DEFAULT 0,
blockers TEXT,
health TEXT DEFAULT 'green',
created_at TEXT DEFAULT (datetime('now'))
);
-- ============================================================
-- 9. EXECUTIVE / BOARD OS
-- ============================================================
CREATE TABLE IF NOT EXISTS approvals (
id TEXT PRIMARY KEY,
org_id TEXT NOT NULL,
module TEXT NOT NULL,
reference_id TEXT NOT NULL,
title TEXT NOT NULL,
amount REAL,
risk_level TEXT DEFAULT 'medium',
status TEXT DEFAULT 'pending',
requested_by TEXT,
approved_by TEXT,
decision_at TEXT,
evidence_pack TEXT,
created_at TEXT DEFAULT (datetime('now'))
);
CREATE TABLE IF NOT EXISTS executive_packs (
id TEXT PRIMARY KEY,
org_id TEXT NOT NULL,
week_label TEXT,
actual_revenue REAL DEFAULT 0,
forecast_revenue REAL DEFAULT 0,
open_approvals INTEGER DEFAULT 0,
blockers TEXT,
next_best_actions TEXT,
risk_heatmap TEXT,
generated_at TEXT DEFAULT (datetime('now'))
);
-- ============================================================
-- AUDIT CHAIN (cross-module)
-- ============================================================
-- =============================================
-- Revenue Intelligence OS — Lead Machine Tables
-- =============================================
-- ICP configs per org
CREATE TABLE IF NOT EXISTS icp_configs (
id TEXT PRIMARY KEY,
org_id TEXT NOT NULL,
name TEXT NOT NULL,
config TEXT NOT NULL, -- JSON ICPConfig
is_active INTEGER DEFAULT 1,
created_by TEXT,
created_at TEXT DEFAULT (datetime('now'))
);
-- Discovered leads (raw, before enrichment)
CREATE TABLE IF NOT EXISTS intelligence_leads (
id TEXT PRIMARY KEY,
org_id TEXT NOT NULL,
company_name TEXT NOT NULL,
domain TEXT,
industry TEXT,
region TEXT,
company_size TEXT,
description TEXT,
website TEXT,
tech_stack TEXT, -- JSON list
signals TEXT, -- JSON list
recent_news TEXT, -- JSON list
contact_name TEXT,
contact_title TEXT,
contact_email TEXT,
contact_phone TEXT,
contact_linkedin TEXT,
decision_maker_score INTEGER DEFAULT 0,
enrichment_source TEXT DEFAULT 'web',
enrichment_confidence REAL DEFAULT 0.5,
source TEXT,
source_url TEXT,
raw_snippet TEXT,
trigger TEXT,
-- Scores
score_fit INTEGER DEFAULT 0,
score_intent INTEGER DEFAULT 0,
score_access INTEGER DEFAULT 0,
score_value INTEGER DEFAULT 0,
score_urgency INTEGER DEFAULT 0,
score_master INTEGER DEFAULT 0,
priority_tier TEXT DEFAULT 'P4',
score_reasons TEXT, -- JSON list
next_action TEXT,
next_action_ar TEXT,
-- Outreach
outreach_whatsapp_ar TEXT,
outreach_email_subject_ar TEXT,
outreach_email_body_ar TEXT,
outreach_linkedin_ar TEXT,
outreach_angle TEXT,
-- Pipeline tracking
pipeline_run_id TEXT,
crm_lead_id TEXT, -- linked to leads table
status TEXT DEFAULT 'discovered', -- discovered | contacted | qualified | archived
reviewed_by TEXT,
reviewed_at TEXT,
enriched_at TEXT,
discovered_at TEXT DEFAULT (datetime('now')),
created_at TEXT DEFAULT (datetime('now'))
);
-- Pipeline run history
CREATE TABLE IF NOT EXISTS intelligence_runs (
id TEXT PRIMARY KEY,
org_id TEXT NOT NULL,
icp_id TEXT,
run_mode TEXT DEFAULT 'auto', -- auto | manual | triggered
motion TEXT DEFAULT 'sales',
total_discovered INTEGER DEFAULT 0,
total_deduped INTEGER DEFAULT 0,
total_enriched INTEGER DEFAULT 0,
tier_p1 INTEGER DEFAULT 0,
tier_p2 INTEGER DEFAULT 0,
tier_p3 INTEGER DEFAULT 0,
tier_p4 INTEGER DEFAULT 0,
duration_sec REAL DEFAULT 0,
status TEXT DEFAULT 'running', -- running | complete | error
error_message TEXT,
created_by TEXT,
created_at TEXT DEFAULT (datetime('now'))
);
-- Watchlist for trigger alerts
CREATE TABLE IF NOT EXISTS intelligence_watchlist (
id TEXT PRIMARY KEY,
org_id TEXT NOT NULL,
company_name TEXT NOT NULL,
domain TEXT,
priority INTEGER DEFAULT 0,
last_scanned TEXT,
active INTEGER DEFAULT 1,
added_by TEXT,
created_at TEXT DEFAULT (datetime('now'))
);
-- Trigger events detected
CREATE TABLE IF NOT EXISTS intelligence_triggers (
id TEXT PRIMARY KEY,
org_id TEXT NOT NULL,
company_name TEXT NOT NULL,
trigger_type TEXT NOT NULL,
trigger_label_ar TEXT,
signal_strength INTEGER DEFAULT 0,
evidence TEXT,
source_url TEXT,
recommended_action_ar TEXT,
recommended_action_en TEXT,
is_actioned INTEGER DEFAULT 0,
actioned_by TEXT,
detected_at TEXT DEFAULT (datetime('now'))
);
-- Entity registry (deduplication)
CREATE TABLE IF NOT EXISTS intelligence_entities (
id TEXT PRIMARY KEY,
canonical_name TEXT NOT NULL,
normalized_name TEXT,
domain TEXT,
aliases TEXT, -- JSON list
created_at TEXT DEFAULT (datetime('now'))
);
CREATE TABLE IF NOT EXISTS audit_log (
id INTEGER PRIMARY KEY AUTOINCREMENT,
org_id TEXT NOT NULL,
module TEXT NOT NULL,
action TEXT NOT NULL,
actor_id TEXT,
resource_id TEXT,
payload TEXT,
prev_hash TEXT,
entry_hash TEXT,
ts TEXT DEFAULT (datetime('now'))
);
""")
# Seed admin users
import hashlib, uuid
users = [
("admin-001", "admin@dealix.io", "Admin", "admin"),
("mgr-001", "manager@dealix.io", "Manager", "manager"),
("sales-001", "sales@dealix.io", "Sales Rep", "sales"),
]
passwords = {"admin": "Admin1234!", "manager": "Manager1234!", "sales": "Sales1234!"}
for uid, email, name, role in users:
pw = hashlib.sha256(passwords[role].encode()).hexdigest()
conn.execute("""
INSERT OR IGNORE INTO users (id, email, name, role, password_hash)
VALUES (?, ?, ?, ?, ?)
""", (uid, email, name, role, pw))
# Seed discount policies
conn.execute("""
INSERT OR IGNORE INTO discount_policies (id, org_id, max_discount_pct, approver_role, deal_value_min, deal_value_max)
VALUES ('dp-1','dealix',10,'sales',0,50000),
('dp-2','dealix',20,'manager',50000,200000),
('dp-3','dealix',35,'admin',200000,NULL)
""")
# Seed sample data for dashboard
_seed_sample_data(conn)
def _seed_sample_data(conn):
import uuid
# Sample leads
leads = [
("lead-001","dealix","البنك الأهلي","محمد الغامدي","m@anb.com","0500000001","referral","banking","enterprise","500M+","Riyadh","qualified",88,"proposal","sales-001"),
("lead-002","dealix","stc","فيصل الحربي","f@stc.com","0500000002","website","telecom","enterprise","1B+","Riyadh","qualified",91,"negotiation","sales-001"),
("lead-003","dealix","أرامكو","خالد المالكي","k@aramco.com","0500000003","partner","energy","enterprise","10B+","Dhahran","new",72,"intake","sales-001"),
("lead-004","dealix","مجموعة العثيم","سارة القحطاني","s@othaim.com","0500000004","website","retail","large","100M+","Riyadh","contacted",65,"discovery","sales-001"),
("lead-005","dealix","مستشفى الملك فيصل","أحمد الزهراني","a@kfsh.com","0500000005","referral","healthcare","enterprise","200M+","Riyadh","qualified",79,"proposal","sales-001"),
]
for l in leads:
conn.execute("""INSERT OR IGNORE INTO leads
(id,org_id,company_name,contact_name,contact_email,contact_phone,source,industry,company_size,annual_revenue,region,status,score,stage,assigned_to)
VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)""", l)
# Sample deals
deals = [
("deal-001","dealix","lead-001","صفقة البنك الأهلي — Revenue OS",850000,"SAR","proposal",75,"2026-06-30","sales-001","acc-001"),
("deal-002","dealix","lead-002","stc — Enterprise Suite",1200000,"SAR","negotiation",85,"2026-05-31","sales-001","acc-002"),
("deal-003","dealix","lead-003","أرامكو — Executive OS",2500000,"SAR","discovery",40,"2026-09-30","sales-001","acc-003"),
("deal-004","dealix","lead-005","KFSH — Procurement OS",420000,"SAR","proposal",65,"2026-07-15","sales-001","acc-004"),
]
for d in deals:
conn.execute("""INSERT OR IGNORE INTO deals
(id,org_id,lead_id,title,value,currency,stage,probability,close_date,owner_id,account_id)
VALUES (?,?,?,?,?,?,?,?,?,?,?)""", d)
# Sample accounts
accounts = [
("acc-001","dealix","البنك الأهلي","banking","enterprise",850000,82,"sales-001"),
("acc-002","dealix","stc","telecom","strategic",1200000,91,"sales-001"),
("acc-003","dealix","أرامكو","energy","strategic",0,88,"sales-001"),
("acc-004","dealix","KFSH","healthcare","enterprise",420000,74,"sales-001"),
]
for a in accounts:
conn.execute("""INSERT OR IGNORE INTO accounts
(id,org_id,company_name,industry,tier,arr,health_score,csm_id)
VALUES (?,?,?,?,?,?,?,?)""", a)
# Sample partners
partners = [
("part-001","dealix","Oracle Arabia","technology","active",87,320000,82,"علي الدوسري","a@oracle.com"),
("part-002","dealix","SAP KSA","technology","active",91,580000,88,"نورة العتيبي","n@sap.com"),
("part-003","dealix","Deloitte KSA","consulting","prospect",73,0,0,"طارق المحمد","t@deloitte.com"),
]
for p in partners:
conn.execute("""INSERT OR IGNORE INTO partners
(id,org_id,company_name,partner_type,status,fit_score,revenue_contribution,health_score,contact_name,contact_email)
VALUES (?,?,?,?,?,?,?,?,?,?)""", p)
# Sample M&A targets
conn.execute("""INSERT OR IGNORE INTO ma_targets
(id,org_id,target_name,industry,estimated_value,fit_score,stage)
VALUES ('ma-001','dealix','Salesbook KSA','SaaS',8500000,84,'due_diligence')""")
# Sample renewals
conn.execute("""INSERT OR IGNORE INTO renewals
(id,org_id,account_id,current_arr,renewal_date,churn_risk_score,expansion_score,status)
VALUES ('ren-001','dealix','acc-001',850000,'2026-12-31',22,67,'upcoming'),
('ren-002','dealix','acc-002',1200000,'2026-10-31',8,88,'upcoming')""")
# Sample approvals
conn.execute("""INSERT OR IGNORE INTO approvals
(id,org_id,module,reference_id,title,amount,risk_level,status,requested_by)
VALUES
('appr-001','dealix','pricing','deal-001','خصم 25% — البنك الأهلي',212500,'high','pending','sales-001'),
('appr-002','dealix','procurement','pr-001','تجديد عقد Oracle',145000,'medium','pending','mgr-001'),
('appr-003','dealix','partnership','part-003','تفعيل شراكة Deloitte',0,'low','pending','mgr-001')""")
# Executive pack
conn.execute("""INSERT OR IGNORE INTO executive_packs
(id,org_id,week_label,actual_revenue,forecast_revenue,open_approvals,blockers,next_best_actions)
VALUES ('ep-001','dealix','الأسبوع 16 — 2026',3850000,4200000,3,
'["صفقة أرامكو: تأخر RFP","تجديد Oracle: انتهاء العقد في 30 يوم"]',
'["أغلق خصم البنك الأهلي — 25%","ادفع تجديد Oracle قبل الانتهاء","جدول kickoff مع Deloitte"]')""")
# Audit chain seed
prev = "GENESIS"
entries = [
("dealix","revenue","lead_created","admin-001","lead-001","{}"),
("dealix","pricing","quote_created","sales-001","deal-001","{}"),
("dealix","partnership","partner_added","mgr-001","part-001","{}"),
("dealix","executive","pack_generated","admin-001","ep-001","{}"),
]
for org, module, action, actor, resource, payload in entries:
import hashlib, time
content = f"{org}:{module}:{action}:{actor}:{resource}:{time.time()}"
entry_hash = hashlib.sha256(f"{prev}:{content}".encode()).hexdigest()
conn.execute("""INSERT INTO audit_log (org_id,module,action,actor_id,resource_id,payload,prev_hash,entry_hash)
VALUES (?,?,?,?,?,?,?,?)""", (org, module, action, actor, resource, payload, prev, entry_hash))
prev = entry_hash

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# Dealix Revenue Intelligence OS — Lead Machine Layer

378
salesflow-saas/backend/app/intelligence/discovery.py Normal file
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"""
Lead Discovery Engine — Multi-source, Arabic/English
Searches web, news, job boards, and directories to find lead targets.
Returns structured LeadCandidate objects ready for enrichment.
"""
import re
import uuid
import hashlib
import unicodedata
from dataclasses import dataclass, field
from typing import List, Dict, Any, Optional
import urllib.request
import urllib.parse
import json
import time
@dataclass
class LeadCandidate:
"""Raw discovered lead — before enrichment and scoring"""
id: str = field(default_factory=lambda: str(uuid.uuid4()))
company_name: str = ""
company_name_ar: str = ""
domain: str = ""
industry: str = ""
region: str = ""
source: str = "" # web_search | news | job_board | directory
source_url: str = ""
raw_snippet: str = ""
contact_name: str = ""
contact_title: str = ""
contact_email: str = ""
contact_linkedin: str = ""
phone: str = ""
signals: List[str] = field(default_factory=list) # ["hiring", "expansion", ...]
trigger: str = "" # what triggered discovery
confidence: float = 0.5 # 0-1 source confidence
discovered_at: str = ""
def normalize_company_name(name: str) -> str:
"""Normalize company name for deduplication — Arabic + English"""
name = name.strip().lower()
# Remove Arabic definite article
name = re.sub(r'^(ال|شركة\s+|مجموعة\s+)', '', name)
# Remove common suffixes
suffixes = [
r'\s+(llc|ltd|co\.|inc\.|corp\.?|group|holding|sa|كو|ليميتد|للتقنية|للخدمات|السعودية)$'
]
for s in suffixes:
name = re.sub(s, '', name, flags=re.IGNORECASE)
# Normalize unicode
name = unicodedata.normalize('NFKC', name)
return name.strip()
def extract_domain_from_url(url: str) -> str:
try:
parsed = urllib.parse.urlparse(url)
domain = parsed.netloc.replace('www.', '')
return domain
except Exception:
return ""
def extract_emails_from_text(text: str) -> List[str]:
pattern = r'\b[A-Za-z0-9._%+\-]+@[A-Za-z0-9.\-]+\.[A-Z|a-z]{2,}\b'
return list(set(re.findall(pattern, text)))
def extract_phones_from_text(text: str) -> List[str]:
pattern = r'(\+966|00966|05\d)[\s\-]?(\d[\s\-]?){8,9}'
return list(set(re.findall(pattern, text) or []))
def extract_linkedin_profiles(text: str) -> List[str]:
pattern = r'linkedin\.com/in/[\w\-]+'
return list(set(re.findall(pattern, text, re.IGNORECASE)))
def detect_signals(text: str) -> List[str]:
"""Detect intent/trigger signals in text"""
signals = []
text_lower = text.lower()
signal_map = {
"hiring": ["hiring", "we're hiring", "join our team", "نحن نوظف", "فرص عمل", "وظائف"],
"expansion": ["expansion", "new office", "توسع", "افتتاح فرع", "نطاق جديد"],
"funding": ["funding", "raised", "investment", "تمويل", "استثمار", "سلسلة", "series"],
"partnership": ["partnership", "collaboration", "شراكة", "تعاون"],
"digital_transformation": ["digital transformation", "تحول رقمي", "رقمنة"],
"new_product": ["launch", "new product", "إطلاق", "منتج جديد"],
"pain_point_crm": ["crm", "sales management", "إدارة المبيعات", "عملاء"],
"pain_point_outreach": ["outreach", "leads", "عملاء محتملين", "مبيعات"],
"regulation": ["zatca", "pdpl", "vat", "ضريبة", "ضريبة القيمة المضافة", "حوكمة"],
"ipo": ["ipo", "طرح عام", "اكتتاب"],
}
for signal, keywords in signal_map.items():
if any(kw in text_lower for kw in keywords):
signals.append(signal)
return signals
# ─── Curated Saudi B2B Lead Database (fallback when web search is rate-limited) ───
SAUDI_B2B_SEED_LEADS = [
# Tech / SaaS
{"company_name": "Elm", "domain": "elm.sa", "industry": "technology", "region": "Riyadh", "company_size": "1000+", "signals": ["digital_transformation", "hiring"]},
{"company_name": "Unifonic", "domain": "unifonic.com", "industry": "tech", "region": "Riyadh", "company_size": "200-1000", "signals": ["expansion", "funding"]},
{"company_name": "Foodics", "domain": "foodics.com", "industry": "saas", "region": "Riyadh", "company_size": "200-1000", "signals": ["funding", "expansion"]},
{"company_name": "Salla", "domain": "salla.sa", "industry": "technology", "region": "Jeddah", "company_size": "200-1000", "signals": ["expansion", "hiring"]},
{"company_name": "Zid", "domain": "zid.sa", "industry": "saas", "region": "Riyadh", "company_size": "50-200", "signals": ["digital_transformation"]},
{"company_name": "Lean Technologies", "domain": "leantech.me", "industry": "fintech", "region": "Riyadh", "company_size": "50-200", "signals": ["funding"]},
{"company_name": "Tamara", "domain": "tamara.co", "industry": "fintech", "region": "Riyadh", "company_size": "200-1000", "signals": ["funding", "expansion"]},
{"company_name": "Mozn", "domain": "mozn.sa", "industry": "technology", "region": "Riyadh", "company_size": "50-200", "signals": ["digital_transformation", "hiring"]},
{"company_name": "Rewaa", "domain": "rewaaapp.com", "industry": "saas", "region": "Riyadh", "company_size": "50-200", "signals": ["pain_point_crm"]},
{"company_name": "Tamatem", "domain": "tamatem.co", "industry": "technology", "region": "Riyadh", "company_size": "50-200", "signals": ["expansion"]},
# Healthcare
{"company_name": "مجموعة دله للرعاية الصحية", "domain": "dallah-hospital.com", "industry": "healthcare", "region": "Riyadh", "company_size": "1000+", "signals": ["digital_transformation"]},
{"company_name": "مستشفى الحمادي", "domain": "hammadi.com", "industry": "healthcare", "region": "Riyadh", "company_size": "200-1000", "signals": ["hiring"]},
{"company_name": "Aster DM Healthcare Saudi", "domain": "asterhospitals.sa", "industry": "healthcare", "region": "Riyadh", "company_size": "200-1000", "signals": ["expansion"]},
# Finance / Banking
{"company_name": "Riyad Bank", "domain": "riyadbank.com", "industry": "banking", "region": "Riyadh", "company_size": "1000+", "signals": ["digital_transformation", "hiring"]},
{"company_name": "SABB", "domain": "sabb.com", "industry": "banking", "region": "Jeddah", "company_size": "1000+", "signals": ["digital_transformation"]},
{"company_name": "Alinma Bank", "domain": "alinma.com", "industry": "banking", "region": "Riyadh", "company_size": "1000+", "signals": ["digital_transformation", "expansion"]},
{"company_name": "STC Pay", "domain": "stcpay.com.sa", "industry": "fintech", "region": "Riyadh", "company_size": "200-1000", "signals": ["expansion", "hiring"]},
# Retail / E-commerce
{"company_name": "نون", "domain": "noon.com", "industry": "retail", "region": "Riyadh", "company_size": "1000+", "signals": ["expansion", "hiring", "digital_transformation"]},
{"company_name": "Jarir Bookstore", "domain": "jarir.com", "industry": "retail", "region": "Riyadh", "company_size": "1000+", "signals": ["digital_transformation"]},
{"company_name": "Extra", "domain": "extra.com", "industry": "retail", "region": "Riyadh", "company_size": "200-1000", "signals": ["pain_point_crm"]},
# Logistics
{"company_name": "NAQEL Express", "domain": "naqel.com.sa", "industry": "logistics", "region": "Riyadh", "company_size": "1000+", "signals": ["digital_transformation", "expansion"]},
{"company_name": "Aramex Saudi Arabia", "domain": "aramex.com", "industry": "logistics", "region": "Riyadh", "company_size": "1000+", "signals": ["expansion"]},
{"company_name": "Fetchr", "domain": "fetchr.us", "industry": "logistics", "region": "Riyadh", "company_size": "50-200", "signals": ["digital_transformation"]},
# Real Estate
{"company_name": "Bayut Saudi Arabia", "domain": "bayut.sa", "industry": "real estate", "region": "Riyadh", "company_size": "50-200", "signals": ["digital_transformation"]},
{"company_name": "مدار للعقارات", "domain": "madar.com.sa", "industry": "real estate", "region": "Riyadh", "company_size": "50-200", "signals": ["expansion"]},
# Manufacturing / Industrial
{"company_name": "SABIC", "domain": "sabic.com", "industry": "manufacturing", "region": "Riyadh", "company_size": "1000+", "signals": ["digital_transformation", "ipo"]},
{"company_name": "Saudi Cement", "domain": "saudicement.com.sa", "industry": "manufacturing", "region": "Riyadh", "company_size": "1000+", "signals": ["hiring"]},
# Consulting / Professional Services
{"company_name": "Deloitte Saudi Arabia", "domain": "deloitte.com/sa", "industry": "consulting", "region": "Riyadh", "company_size": "200-1000", "signals": ["hiring", "expansion"]},
{"company_name": "McKinsey Riyadh", "domain": "mckinsey.com", "industry": "consulting", "region": "Riyadh", "company_size": "50-200", "signals": ["pain_point_outreach"]},
{"company_name": "PwC Saudi Arabia", "domain": "pwc.com/m1", "industry": "consulting", "region": "Riyadh", "company_size": "200-1000", "signals": ["regulation", "hiring"]},
# Media / Education
{"company_name": "MBC Group", "domain": "mbc.net", "industry": "media", "region": "Riyadh", "company_size": "1000+", "signals": ["digital_transformation"]},
{"company_name": "Edraak", "domain": "edraak.org", "industry": "education", "region": "Amman", "company_size": "50-200", "signals": ["digital_transformation"]},
# Energy / Government
{"company_name": "Saudi Electricity Company", "domain": "se.com.sa", "industry": "energy", "region": "Riyadh", "company_size": "1000+", "signals": ["digital_transformation", "regulation"]},
{"company_name": "Maaden", "domain": "maaden.com.sa", "industry": "manufacturing", "region": "Riyadh", "company_size": "1000+", "signals": ["expansion", "ipo"]},
]
class LeadDiscoveryEngine:
"""
Multi-source lead discovery engine.
Searches web sources and extracts structured lead candidates.
"""
def __init__(self, icp=None):
self.icp = icp
def search_web_simple(self, query: str, max_results: int = 10) -> List[Dict]:
"""
Lightweight web search via DuckDuckGo HTML (no API key required).
Returns list of {title, url, snippet} dicts.
"""
results = []
try:
encoded = urllib.parse.quote(query)
url = f"https://html.duckduckgo.com/html/?q={encoded}"
req = urllib.request.Request(
url,
headers={
"User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 Chrome/120.0",
"Accept": "text/html,application/xhtml+xml",
"Accept-Language": "ar,en;q=0.9",
}
)
with urllib.request.urlopen(req, timeout=8) as resp:
html = resp.read().decode('utf-8', errors='ignore')
# DDG HTML uses redirect URLs: //duckduckgo.com/l/?uddg=<encoded_real_url>
# Extract all anchor text + href pairs
link_pattern = re.compile(
r'<a[^>]+href="(//duckduckgo\.com/l/\?uddg=[^"]+)"[^>]*>(.*?)</a>',
re.DOTALL | re.IGNORECASE
)
# Extract snippets from result__snippet divs
snippet_pattern = re.compile(
r'class="result__snippet"[^>]*>(.*?)</a>',
re.DOTALL | re.IGNORECASE
)
snippets_raw = snippet_pattern.findall(html)
# Extract URL domain displays (result__url spans)
domain_pattern = re.compile(
r'<span class="result__url"[^>]*>\s*([^<]+)\s*</span>',
re.IGNORECASE
)
domains_raw = domain_pattern.findall(html)
# Extract title links
title_pattern = re.compile(
r'class="result__a"[^>]*>(.*?)</a>',
re.DOTALL | re.IGNORECASE
)
titles_raw = title_pattern.findall(html)
# Extract real URLs from DDG redirect
uddg_pattern = re.compile(
r'uddg=([A-Za-z0-9%+_.-]+)',
re.IGNORECASE
)
all_hrefs = re.findall(
r'class="result__a"[^>]*href="([^"]+)"',
html, re.IGNORECASE
)
for i in range(min(max_results, len(titles_raw))):
title = re.sub(r'<[^>]+>', '', titles_raw[i]).strip()
if not title or len(title) < 4:
continue
# Decode real URL
real_url = ""
if i < len(all_hrefs):
href = all_hrefs[i]
m = uddg_pattern.search(href)
if m:
try:
real_url = urllib.parse.unquote(m.group(1))
except Exception:
real_url = ""
snippet = ""
if i < len(snippets_raw):
snippet = re.sub(r'<[^>]+>', '', snippets_raw[i]).strip()
results.append({
"title": title[:200],
"url": real_url[:500],
"snippet": snippet[:800],
})
except Exception as e:
pass # Silent fail — don't break the pipeline
return results
def candidate_from_search_result(
self, result: Dict, query: str, source: str = "web_search"
) -> Optional[LeadCandidate]:
"""Convert a raw search result into a LeadCandidate"""
title = result.get("title", "")
snippet = result.get("snippet", "")
url = result.get("url", "")
if not title or len(title) < 3:
return None
text = f"{title} {snippet}"
signals = detect_signals(text)
emails = extract_emails_from_text(text)
phones = extract_phones_from_text(text)
linkedin_profiles = extract_linkedin_profiles(text)
candidate = LeadCandidate(
company_name=title[:100],
domain=extract_domain_from_url(url),
source=source,
source_url=url[:500],
raw_snippet=snippet[:1000],
signals=signals,
trigger=query[:200],
contact_email=emails[0] if emails else "",
phone=str(phones[0]) if phones else "",
contact_linkedin=linkedin_profiles[0] if linkedin_profiles else "",
confidence=0.6 if signals else 0.4,
discovered_at=time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
)
return candidate
def _seed_candidates_from_db(self, icp=None) -> List[LeadCandidate]:
"""Generate candidates from curated Saudi B2B seed database"""
candidates = []
now = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
for entry in SAUDI_B2B_SEED_LEADS:
# ICP filter by industry if ICP is provided
if icp and icp.industries:
industry = entry.get("industry", "").lower()
if not any(ind.lower() in industry or industry in ind.lower() for ind in icp.industries):
continue
c = LeadCandidate(
company_name=entry["company_name"],
domain=entry.get("domain", ""),
industry=entry.get("industry", ""),
region=entry.get("region", ""),
source="seed_database",
source_url=f"https://{entry.get('domain','')}",
# Embed region in raw_snippet so scoring picks it up
raw_snippet=f"{entry.get('company_name','')} | {entry.get('industry','')} | {entry.get('region','')} Saudi Arabia KSA",
signals=entry.get("signals", []),
trigger="ICP match — seed database",
confidence=0.7,
discovered_at=now,
)
candidates.append(c)
return candidates
def discover(self, queries: List[str], max_per_query: int = 8) -> List[LeadCandidate]:
"""
Run discovery across all queries, return deduplicated LeadCandidates.
Falls back to seed database if web search returns 0 results.
"""
all_candidates = []
seen_domains = set()
seen_names = set()
web_found = 0
for query in queries:
time.sleep(0.3) # rate limiting
results = self.search_web_simple(query, max_results=max_per_query)
web_found += len(results)
for result in results:
candidate = self.candidate_from_search_result(result, query)
if candidate is None:
continue
# Dedup by domain
if candidate.domain and candidate.domain in seen_domains:
continue
# Dedup by normalized name
norm_name = normalize_company_name(candidate.company_name)
if norm_name and norm_name in seen_names:
continue
if candidate.domain:
seen_domains.add(candidate.domain)
if norm_name:
seen_names.add(norm_name)
all_candidates.append(candidate)
# Fallback: if web search returned nothing (rate-limited), use seed DB
if web_found == 0:
seed_candidates = self._seed_candidates_from_db(self.icp)
for candidate in seed_candidates:
if candidate.domain and candidate.domain in seen_domains:
continue
norm_name = normalize_company_name(candidate.company_name)
if norm_name and norm_name in seen_names:
continue
if candidate.domain:
seen_domains.add(candidate.domain)
if norm_name:
seen_names.add(norm_name)
all_candidates.append(candidate)
else:
# Also enrich with seed DB entries not already found
seed_candidates = self._seed_candidates_from_db(self.icp)
for candidate in seed_candidates:
if candidate.domain and candidate.domain in seen_domains:
continue
norm_name = normalize_company_name(candidate.company_name)
if norm_name and norm_name in seen_names:
continue
if candidate.domain:
seen_domains.add(candidate.domain)
if norm_name:
seen_names.add(norm_name)
all_candidates.append(candidate)
return all_candidates
def discover_from_icp(self, icp=None, max_per_query: int = 6) -> List[LeadCandidate]:
"""Run discovery using ICP-generated queries"""
icp = icp or self.icp
if icp is None:
return []
queries = icp.build_search_queries()
return self.discover(queries, max_per_query=max_per_query)

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salesflow-saas/backend/app/intelligence/enrichment.py Normal file
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"""
Enrichment Layer — Company + Person + Intent signals
Enriches LeadCandidates with additional data from multiple sources.
Designed to plug in Apollo/PDL/Clay APIs via env vars when available.
"""
import os
import re
import json
import time
import urllib.request
import urllib.parse
from typing import Dict, Any, Optional, List
from dataclasses import dataclass, field, asdict
from app.intelligence.discovery import LeadCandidate, extract_emails_from_text, detect_signals
@dataclass
class EnrichedLead:
"""Fully enriched lead — ready for scoring"""
# Identity
id: str = ""
company_name: str = ""
company_name_ar: str = ""
domain: str = ""
website: str = ""
# Company facts
industry: str = ""
industry_ar: str = ""
company_size: str = ""
employee_count: int = 0
founded_year: int = 0
annual_revenue_sar: float = 0.0
headquarters: str = ""
region: str = ""
description: str = ""
description_ar: str = ""
# Technology stack (signals for fit)
tech_stack: List[str] = field(default_factory=list)
uses_crm: bool = False
uses_erp: bool = False
# Contact
contact_name: str = ""
contact_title: str = ""
contact_title_ar: str = ""
contact_email: str = ""
contact_phone: str = ""
contact_linkedin: str = ""
decision_maker_score: int = 0 # 0-100: how likely this person makes the buy decision
# Intent signals
signals: List[str] = field(default_factory=list)
intent_keywords: List[str] = field(default_factory=list)
recent_news: List[str] = field(default_factory=list)
open_jobs_count: int = 0
open_jobs_relevant: List[str] = field(default_factory=list)
# Enrichment metadata
enrichment_source: str = "web" # web | apollo | pdl | clay
enrichment_confidence: float = 0.5
enriched_at: str = ""
# Original discovery data
source: str = ""
source_url: str = ""
raw_snippet: str = ""
trigger: str = ""
def to_dict(self) -> Dict[str, Any]:
return asdict(self)
# Title → Seniority mapping (Arabic + English)
TITLE_SENIORITY = {
"ceo": 100, "chief executive": 100, "الرئيس التنفيذي": 100, "المدير العام": 100,
"coo": 95, "chief operating": 95, "المدير التشغيلي": 95,
"cro": 95, "chief revenue": 95,
"cfo": 90, "chief financial": 90,
"vp": 85, "vice president": 85, "نائب الرئيس": 85,
"head of": 80, "رئيس قسم": 80,
"director": 75, "مدير": 70,
"manager": 55, "مشرف": 40,
"executive": 65, "تنفيذي": 65,
}
TECH_KEYWORDS = [
"salesforce", "sap", "oracle", "hubspot", "zoho", "dynamics", "pipedrive",
"نت سويت", "odoo", "quickbooks", "workday", "servicenow",
"jira", "slack", "teams", "whatsapp business",
]
CRM_KEYWORDS = ["salesforce", "hubspot", "zoho crm", "dynamics crm", "pipedrive", "crm"]
ERP_KEYWORDS = ["sap", "oracle", "odoo", "netsuite", "dynamics erp", "erp"]
def infer_seniority_score(title: str) -> int:
title_lower = title.lower()
for kw, score in TITLE_SENIORITY.items():
if kw in title_lower:
return score
return 30
def infer_tech_stack(text: str) -> List[str]:
text_lower = text.lower()
return [tech for tech in TECH_KEYWORDS if tech in text_lower]
def estimate_company_size(text: str) -> str:
"""Try to extract company size from text"""
patterns = [
(r'(\d{1,5})\s*\+?\s*(employees|موظف|staff)', lambda m: int(m.group(1))),
(r'(small|صغير)', lambda m: 0),
(r'(medium|متوسط)', lambda m: 150),
(r'(large|كبير|enterprise)', lambda m: 1000),
]
for pattern, extractor in patterns:
match = re.search(pattern, text, re.IGNORECASE)
if match:
try:
count = extractor(match)
if count < 50: return "1-50"
elif count < 200: return "50-200"
elif count < 1000: return "200-1000"
else: return "1000+"
except Exception:
pass
return "unknown"
def fetch_company_website_data(domain: str) -> Dict[str, Any]:
"""Try to fetch company website and extract key signals"""
if not domain:
return {}
try:
url = f"https://{domain}"
req = urllib.request.Request(
url,
headers={"User-Agent": "Mozilla/5.0 (compatible; DealixBot/1.0)"}
)
with urllib.request.urlopen(req, timeout=6) as resp:
html = resp.read().decode('utf-8', errors='ignore')[:15000]
emails = extract_emails_from_text(html)
tech_stack = infer_tech_stack(html)
signals = detect_signals(html)
size = estimate_company_size(html)
# Extract title/description
title_match = re.search(r'<title[^>]*>(.*?)</title>', html, re.IGNORECASE | re.DOTALL)
desc_match = re.search(
r'<meta\s+name=["\']description["\'][^>]*content=["\']([^"\']+)["\']',
html, re.IGNORECASE
)
return {
"page_title": re.sub(r'<[^>]+>', '', title_match.group(1)).strip() if title_match else "",
"description": desc_match.group(1).strip() if desc_match else "",
"emails": emails[:3],
"tech_stack": tech_stack,
"signals": signals,
"company_size": size,
}
except Exception:
return {}
def search_company_news(company_name: str) -> List[str]:
"""Quick news search for a company name"""
try:
query = urllib.parse.quote(f"{company_name} news 2025 2026")
url = f"https://html.duckduckgo.com/html/?q={query}"
req = urllib.request.Request(
url, headers={"User-Agent": "Mozilla/5.0 (compatible; DealixBot/1.0)"}
)
with urllib.request.urlopen(req, timeout=5) as resp:
html = resp.read().decode('utf-8', errors='ignore')
snippets = re.findall(r'<a class="result__snippet"[^>]*>(.*?)</a>', html)
return [re.sub(r'<[^>]+>', '', s).strip() for s in snippets[:4]]
except Exception:
return []
def enrich_candidate(candidate: LeadCandidate) -> EnrichedLead:
"""
Enrich a LeadCandidate with website data, news, and inferred signals.
Falls back gracefully when data unavailable.
"""
enriched = EnrichedLead(
id=candidate.id,
company_name=candidate.company_name,
domain=candidate.domain,
website=f"https://{candidate.domain}" if candidate.domain else "",
source=candidate.source,
source_url=candidate.source_url,
raw_snippet=candidate.raw_snippet,
trigger=candidate.trigger,
signals=candidate.signals.copy(),
contact_email=candidate.contact_email,
contact_phone=candidate.contact_phone,
contact_linkedin=candidate.contact_linkedin,
enriched_at=time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
)
# Fetch website data
if candidate.domain:
site_data = fetch_company_website_data(candidate.domain)
enriched.description = site_data.get("description", "")
enriched.tech_stack = site_data.get("tech_stack", [])
enriched.uses_crm = any(t in site_data.get("tech_stack", []) for t in CRM_KEYWORDS)
enriched.uses_erp = any(t in site_data.get("tech_stack", []) for t in ERP_KEYWORDS)
enriched.company_size = site_data.get("company_size", "unknown")
# Merge signals
for sig in site_data.get("signals", []):
if sig not in enriched.signals:
enriched.signals.append(sig)
# Extract emails if not already present
if not enriched.contact_email and site_data.get("emails"):
enriched.contact_email = site_data["emails"][0]
# Fetch news
if candidate.company_name:
enriched.recent_news = search_company_news(candidate.company_name)
# Detect signals in news
for news_item in enriched.recent_news:
for sig in detect_signals(news_item):
if sig not in enriched.signals:
enriched.signals.append(sig)
# Infer decision maker score
enriched.decision_maker_score = infer_seniority_score(candidate.contact_title)
# Confidence based on available data
data_points = sum([
bool(enriched.domain),
bool(enriched.contact_email),
bool(enriched.description),
bool(enriched.signals),
bool(enriched.recent_news),
])
enriched.enrichment_confidence = min(1.0, 0.3 + (data_points * 0.14))
enriched.enrichment_source = "web"
return enriched
def enrich_batch(candidates: List[LeadCandidate], delay: float = 0.5) -> List[EnrichedLead]:
"""Enrich a list of candidates with rate limiting"""
enriched_leads = []
for candidate in candidates:
enriched = enrich_candidate(candidate)
enriched_leads.append(enriched)
time.sleep(delay)
return enriched_leads

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salesflow-saas/backend/app/intelligence/entity_resolution.py Normal file
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"""
Entity Resolution & Deduplication Engine
Arabic/English normalization + fuzzy company matching.
Prevents same company appearing twice under different names.
"""
import re
import unicodedata
from typing import List, Dict, Tuple, Optional
from difflib import SequenceMatcher
# Common Arabic/English company suffixes to strip
STRIP_SUFFIXES_AR = [
r'\s*(شركة|مجموعة|مؤسسة|ش\.م\.م|ش\.م\.س|ذ\.م\.م|للخدمات|للتقنية|للمعلوماتية'
r'|السعودية|العربية|الخليجية|الدولية|التجارية|الحديثة|المتحدة|المتقدمة)\s*$'
]
STRIP_SUFFIXES_EN = [
r'\s*(llc|ltd|co\.|co|inc\.|inc|corp\.|corp|group|holding|holdings|sa|plc'
r'|technologies|solutions|services|systems|international|global|company)\s*$'
]
ARABIC_ARTICLE = r'^(ال)'
# Arabic → English character transliteration for matching
ARABIC_ROMAN_MAP = {
'ا': 'a', 'أ': 'a', 'إ': 'a', 'آ': 'a',
'ب': 'b', 'ت': 't', 'ث': 'th', 'ج': 'j', 'ح': 'h', 'خ': 'kh',
'د': 'd', 'ذ': 'dh', 'ر': 'r', 'ز': 'z', 'س': 's', 'ش': 'sh',
'ص': 's', 'ض': 'd', 'ط': 't', 'ظ': 'z', 'ع': 'a', 'غ': 'gh',
'ف': 'f', 'ق': 'q', 'ك': 'k', 'ل': 'l', 'م': 'm', 'ن': 'n',
'ه': 'h', 'و': 'w', 'ي': 'y', 'ى': 'a', 'ة': 'h',
'ئ': 'y', 'ء': '', 'ؤ': 'w',
}
def transliterate_arabic(text: str) -> str:
"""Convert Arabic script to approximate Latin for cross-script matching"""
return ''.join(ARABIC_ROMAN_MAP.get(c, c) for c in text)
def normalize_name(name: str) -> str:
"""Canonical form for deduplication matching"""
if not name:
return ""
name = name.strip().lower()
# Strip Arabic article
name = re.sub(ARABIC_ARTICLE, '', name)
# Strip Arabic suffixes
for pattern in STRIP_SUFFIXES_AR:
name = re.sub(pattern, '', name, flags=re.IGNORECASE)
# Strip English suffixes
for pattern in STRIP_SUFFIXES_EN:
name = re.sub(pattern, '', name, flags=re.IGNORECASE)
# Normalize unicode
name = unicodedata.normalize('NFKC', name)
# Remove punctuation
name = re.sub(r'[^\w\s\u0600-\u06FF]', '', name)
name = re.sub(r'\s+', ' ', name).strip()
return name
def normalize_domain(domain: str) -> str:
"""Strip www, https, subdomains for domain matching"""
domain = domain.lower().strip()
domain = re.sub(r'^https?://', '', domain)
domain = re.sub(r'^www\.', '', domain)
domain = re.sub(r'/.*$', '', domain)
return domain
def fuzzy_match_score(a: str, b: str) -> float:
"""Similarity ratio between two strings 0-1"""
return SequenceMatcher(None, a, b).ratio()
def are_same_company(
name_a: str, domain_a: str,
name_b: str, domain_b: str,
threshold: float = 0.82
) -> Tuple[bool, float, str]:
"""
Determine if two company records refer to the same entity.
Returns: (is_same, confidence, reason)
"""
# Domain match is definitive
if domain_a and domain_b:
d_a = normalize_domain(domain_a)
d_b = normalize_domain(domain_b)
if d_a == d_b and d_a:
return True, 1.0, "exact_domain_match"
# Normalize names
norm_a = normalize_name(name_a)
norm_b = normalize_name(name_b)
if not norm_a or not norm_b:
return False, 0.0, "insufficient_data"
# Exact normalized match
if norm_a == norm_b:
return True, 0.98, "exact_name_match"
# Fuzzy match on original names
ratio = fuzzy_match_score(norm_a, norm_b)
if ratio >= threshold:
return True, ratio, f"fuzzy_match_{ratio:.2f}"
# Cross-script: transliterate Arabic and compare with English
translit_a = transliterate_arabic(norm_a)
translit_b = transliterate_arabic(norm_b)
cross_ratio = fuzzy_match_score(translit_a, norm_b)
if cross_ratio >= threshold:
return True, cross_ratio, f"cross_script_match_{cross_ratio:.2f}"
cross_ratio2 = fuzzy_match_score(norm_a, translit_b)
if cross_ratio2 >= threshold:
return True, cross_ratio2, f"cross_script_match_{cross_ratio2:.2f}"
return False, max(ratio, cross_ratio), "no_match"
class EntityRegistry:
"""
Maintains a registry of known companies with deduplication.
Use resolve() to find or create a canonical entity.
"""
def __init__(self):
self._entities: List[Dict] = [] # List of canonical entity records
self._domain_index: Dict[str, int] = {} # domain → entity index
self._name_index: Dict[str, int] = {} # normalized name → entity index
def resolve(self, name: str, domain: str = "") -> Tuple[int, bool]:
"""
Find existing entity or create new one.
Returns: (entity_id, is_new)
"""
norm_name = normalize_name(name)
norm_domain = normalize_domain(domain) if domain else ""
# Fast lookup by domain
if norm_domain and norm_domain in self._domain_index:
return self._domain_index[norm_domain], False
# Fast lookup by exact name
if norm_name and norm_name in self._name_index:
return self._name_index[norm_name], False
# Fuzzy scan
for idx, entity in enumerate(self._entities):
is_same, confidence, reason = are_same_company(
name, domain,
entity.get("canonical_name", ""),
entity.get("domain", ""),
)
if is_same:
# Update entity with better data
if not entity.get("domain") and norm_domain:
entity["domain"] = norm_domain
self._domain_index[norm_domain] = idx
return idx, False
# Create new entity
new_id = len(self._entities)
entity = {
"id": new_id,
"canonical_name": name,
"normalized_name": norm_name,
"domain": norm_domain,
"aliases": [],
}
self._entities.append(entity)
if norm_domain:
self._domain_index[norm_domain] = new_id
if norm_name:
self._name_index[norm_name] = new_id
return new_id, True
def deduplicate_lead_list(self, leads: List[Dict]) -> List[Dict]:
"""
Deduplicate a list of lead dicts.
Each lead must have 'company_name' and optionally 'domain'.
Returns deduplicated list with canonical names.
"""
seen = {} # entity_id → first lead index
deduped = []
for lead in leads:
name = lead.get("company_name", "")
domain = lead.get("domain", "")
entity_id, is_new = self.resolve(name, domain)
if is_new or entity_id not in seen:
seen[entity_id] = len(deduped)
lead["entity_id"] = entity_id
deduped.append(lead)
else:
# Merge: keep richer record
existing = deduped[seen[entity_id]]
for field in ["contact_email", "contact_phone", "contact_linkedin",
"description", "tech_stack", "signals"]:
if not existing.get(field) and lead.get(field):
existing[field] = lead[field]
# Merge signals list
if isinstance(existing.get("signals"), list) and isinstance(lead.get("signals"), list):
existing["signals"] = list(set(existing["signals"] + lead["signals"]))
return deduped
@property
def entity_count(self) -> int:
return len(self._entities)

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"""
ICP Builder — Ideal Customer Profile Engine
Defines and stores ICP configs per org. Drives all discovery logic.
"""
from dataclasses import dataclass, field, asdict
from typing import List, Optional, Dict, Any
import json
@dataclass
class ICPConfig:
"""Ideal Customer Profile — full definition per org"""
org_id: str
# Company attributes
industries: List[str] = field(default_factory=list) # e.g. ["tech", "healthcare", "banking"]
company_sizes: List[str] = field(default_factory=list) # e.g. ["50-200", "200-1000"]
regions: List[str] = field(default_factory=list) # e.g. ["Riyadh", "Jeddah", "KSA"]
revenue_range_sar: Dict[str, float] = field(default_factory=dict) # {"min": 1000000, "max": 50000000}
tech_signals: List[str] = field(default_factory=list) # e.g. ["Salesforce", "SAP", "HubSpot"]
growth_signals: List[str] = field(default_factory=list) # e.g. ["hiring", "funding", "expansion"]
languages: List[str] = field(default_factory=list) # e.g. ["ar", "en"]
# Person attributes (buying committee)
target_titles_ar: List[str] = field(default_factory=list) # Arabic titles
target_titles_en: List[str] = field(default_factory=list) # English titles
seniority_levels: List[str] = field(default_factory=list) # e.g. ["C-level", "VP", "Director"]
# Opportunity type
motion: str = "sales" # sales | partnership | channel | tender
segment: str = "B2B" # B2B | B2C | B2T
# Scoring weights (must sum to 1.0)
fit_weight: float = 0.30
intent_weight: float = 0.25
access_weight: float = 0.15
value_weight: float = 0.20
urgency_weight: float = 0.10
# Discovery sources
discovery_sources: List[str] = field(default_factory=lambda: [
"web_search", "linkedin_public", "news", "job_boards", "directories"
])
def to_dict(self) -> Dict[str, Any]:
return asdict(self)
def build_search_queries(self) -> List[str]:
"""Auto-generate search queries from ICP attributes — Arabic + English"""
queries = []
for industry in self.industries[:3]:
for region in self.regions[:2]:
queries.append(f"شركات {industry} في {region}")
queries.append(f"{industry} companies in {region} Saudi Arabia")
for signal in self.growth_signals[:2]:
for industry in self.industries[:2]:
queries.append(f"{industry} {signal} Saudi Arabia 2025 2026")
for title in self.target_titles_ar[:2]:
for industry in self.industries[:2]:
queries.append(f"{title} {industry} السعودية")
for title in self.target_titles_en[:2]:
for industry in self.industries[:2]:
queries.append(f"{title} {industry} Saudi Arabia LinkedIn")
return queries[:20] # cap at 20 queries
# Default Dealix ICP — B2B SaaS / Enterprise, Saudi-first
DEALIX_DEFAULT_ICP = ICPConfig(
org_id="dealix",
industries=["تقنية", "رعاية صحية", "مالية وبنوك", "عقارات", "تصنيع", "تجزئة", "لوجستيات",
"technology", "healthcare", "banking", "real estate", "manufacturing", "retail"],
company_sizes=["10-50", "50-200", "200-1000", "1000+"],
regions=["الرياض", "جدة", "الدمام", "المنطقة الشرقية", "Riyadh", "Jeddah", "Dammam", "KSA"],
revenue_range_sar={"min": 500_000, "max": 500_000_000},
tech_signals=["Salesforce", "SAP", "Oracle", "HubSpot", "Zoho", "Microsoft Dynamics", "Excel", "WhatsApp Business"],
growth_signals=["hiring", "expansion", "funding", "partnership", "IPO", "digital transformation",
"توظيف", "توسع", "تمويل", "شراكة", "تحول رقمي"],
languages=["ar", "en"],
target_titles_ar=["مدير تطوير الأعمال", "مدير المبيعات", "الرئيس التنفيذي", "المدير التجاري",
"مدير الشراكات", "مدير التسويق", "مدير المشتريات", "نائب الرئيس"],
target_titles_en=["CEO", "CCO", "VP Sales", "Head of Business Development", "Commercial Director",
"Chief Revenue Officer", "Sales Director", "Partnerships Manager"],
seniority_levels=["C-level", "VP", "Director", "Head of", "Manager"],
motion="sales",
segment="B2B",
discovery_sources=["web_search", "news", "job_boards", "directories", "linkedin_public"],
)

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"""
Outreach Brief Generator — Arabic-first
Generates personalized outreach messages for B2B, B2C, B2T motions.
Templates are rule-based with signal-driven personalization.
Plugs into LLM when OPENAI_API_KEY is set.
"""
import os
import json
import urllib.request
import urllib.error
from typing import Dict, Any, Optional, List
from dataclasses import dataclass
@dataclass
class OutreachBrief:
lead_id: str
company_name: str
contact_name: str
contact_title: str
motion: str # sales | partnership | channel | tender
# Arabic messages
whatsapp_ar: str = ""
email_subject_ar: str = ""
email_body_ar: str = ""
linkedin_ar: str = ""
# English fallback
email_subject_en: str = ""
email_body_en: str = ""
# Strategy
angle: str = "" # the specific hook being used
pain_hypothesis: str = "" # what problem we assume they have
value_proposition: str = ""
call_to_action: str = ""
# Metadata
personalization_score: int = 0 # 0-100 how personalized this is
generated_by: str = "template" # template | llm
# Signal → angle mapping
SIGNAL_ANGLES = {
"hiring": {
"angle": "توسع الفريق",
"hook_ar": "لاحظنا أنكم تُوسّعون فريقكم — هذا المرحلة تحتاج منظومة مبيعات قوية",
"hook_en": "Noticed you're scaling your team — this is exactly when a strong sales OS matters",
},
"funding": {
"angle": "تمويل جديد",
"hook_ar": "تهانينا على جولة التمويل — الشركات بعد التمويل تبني محرك إيرادات سريع",
"hook_en": "Congrats on the funding — post-investment is when you need to build your revenue engine fast",
},
"expansion": {
"angle": "توسع جغرافي",
"hook_ar": "رأينا توسعكم في السوق — دعونا نساعدكم تُحوّل هذا التوسع لعقود حقيقية",
"hook_en": "Saw your expansion news — let us help you convert that market entry into real contracts",
},
"digital_transformation": {
"angle": "تحول رقمي",
"hook_ar": "مبادرات التحول الرقمي تحتاج محرك مبيعات ذكي يواكبها",
"hook_en": "Digital transformation initiatives need an intelligent sales engine to match",
},
"ipo": {
"angle": "استعداد للطرح العام",
"hook_ar": "الاستعداد للطرح العام يتطلب منظومة إيرادات موثوقة وقابلة للتدقيق",
"hook_en": "IPO readiness demands a verifiable and auditable revenue system",
},
"pain_point_crm": {
"angle": "إدارة علاقات العملاء",
"hook_ar": "إدارة العملاء بالإكسل في 2026 تُكلّف الشركة عقوداً ضائعة",
"hook_en": "Managing clients in spreadsheets in 2026 costs you real contracts",
},
"pain_point_outreach": {
"angle": "التواصل مع العملاء",
"hook_ar": "فرق المبيعات اليوم تحتاج أدوات ذكية تُولّد ليدز وتُغلق صفقات تلقائياً",
"hook_en": "Sales teams today need AI tools that generate leads and close deals automatically",
},
}
# Motion-specific value propositions
MOTION_VALUE_PROPS = {
"sales": {
"ar": "Dealix يُحوّل فريق مبيعاتكم إلى ماكينة إيرادات ذاتية بـ 9 أنظمة تشغيل مدمجة",
"en": "Dealix turns your sales team into a self-driving revenue machine with 9 integrated operating systems",
},
"partnership": {
"ar": "Dealix يبني منظومة شراكة تُدير التحالفات والحوافز والإيرادات من مكان واحد",
"en": "Dealix builds a partnership ecosystem that manages alliances, incentives and revenues in one place",
},
"channel": {
"ar": "برنامج الشركاء في Dealix يمنح موزّعيكم أدوات المبيعات الاحترافية بدون تكلفة إضافية",
"en": "Dealix partner program gives your resellers professional sales tools at no extra cost",
},
"tender": {
"ar": "Dealix يُساعد في بناء ملف المؤهلات الكامل وتتبع الفرص الحكومية والتجارية",
"en": "Dealix helps build full qualification packages and track government and commercial opportunities",
},
}
CTA_BY_TIER = {
"P1": {
"ar": "هل لديكم 15 دقيقة هذا الأسبوع لعرض سريع؟",
"en": "Do you have 15 minutes this week for a quick demo?",
},
"P2": {
"ar": "أودّ إرسال لكم ملف موجز يوضح كيف تستفيد شركات مثلكم من Dealix",
"en": "I'd like to send you a brief overview of how companies like yours benefit from Dealix",
},
"P3": {
"ar": "سأُبقيكم على اطلاع بتحديثات Dealix — هل موافقون؟",
"en": "I'll keep you updated on Dealix — would that be okay?",
},
"P4": {
"ar": "تواصل معنا عند الجاهزية",
"en": "Reach out when the time is right",
},
}
def pick_angle(signals: List[str]) -> Dict:
"""Pick the best outreach angle based on available signals"""
priority_order = ["funding", "ipo", "expansion", "hiring", "digital_transformation",
"pain_point_crm", "pain_point_outreach"]
for sig in priority_order:
if sig in signals:
return SIGNAL_ANGLES[sig]
return {
"angle": "تطوير الأعمال",
"hook_ar": "نساعد الشركات الرائدة في السعودية على بناء محرك إيرادات ذكي",
"hook_en": "We help leading Saudi companies build an intelligent revenue engine",
}
def build_whatsapp_message(
company: str, contact: str, angle_data: Dict, motion: str, tier: str
) -> str:
"""Build a short WhatsApp-optimized Arabic message"""
hook = angle_data.get("hook_ar", "")
vp = MOTION_VALUE_PROPS.get(motion, MOTION_VALUE_PROPS["sales"])["ar"]
cta = CTA_BY_TIER.get(tier, CTA_BY_TIER["P3"])["ar"]
contact_greeting = f"مرحباً {contact}" if contact else f"مرحباً فريق {company}"
return f"""{contact_greeting}،
{hook}.
{vp}.
{cta}
— فريق Dealix"""
def build_email(
company: str, contact: str, title: str,
angle_data: Dict, motion: str, tier: str,
signals: List[str]
) -> Dict[str, str]:
"""Build email subject and body in Arabic + English"""
hook_ar = angle_data.get("hook_ar", "")
hook_en = angle_data.get("hook_en", "")
vp_ar = MOTION_VALUE_PROPS.get(motion, MOTION_VALUE_PROPS["sales"])["ar"]
vp_en = MOTION_VALUE_PROPS.get(motion, MOTION_VALUE_PROPS["sales"])["en"]
cta_ar = CTA_BY_TIER.get(tier, CTA_BY_TIER["P3"])["ar"]
cta_en = CTA_BY_TIER.get(tier, CTA_BY_TIER["P3"])["en"]
contact_ar = f"{contact}" if contact else f"فريق {company}"
title_mention_ar = f" | {title}" if title else ""
title_mention_en = f", {title}" if title else ""
subject_ar = f"Dealix × {company} — {angle_data.get('angle', 'فرصة تعاون')}"
subject_en = f"Dealix × {company} — {angle_data.get('angle', 'Partnership Opportunity')}"
body_ar = f"""مرحباً {contact_ar}{title_mention_ar}،
{hook_ar}.
{vp_ar}.
نحن نعمل مع شركات في قطاعكم ونرى نتائج واضحة:
• زيادة في معدل إغلاق الصفقات
• تقليل وقت دورة المبيعات
• رؤية كاملة للـ pipeline التنفيذي
{cta_ar}
مع التقدير،
فريق Dealix
https://dealix.ai"""
body_en = f"""Hi {contact or 'there'}{title_mention_en},
{hook_en}.
{vp_en}.
We work with companies in your sector and see clear results:
• Higher deal close rates
• Shorter sales cycle time
• Full executive pipeline visibility
{cta_en}
Best regards,
The Dealix Team
https://dealix.ai"""
return {
"subject_ar": subject_ar,
"body_ar": body_ar,
"subject_en": subject_en,
"body_en": body_en,
}
def build_linkedin_message(
company: str, contact: str, angle_data: Dict, motion: str
) -> str:
"""LinkedIn connection message — short and professional (300 chars)"""
hook = angle_data.get("hook_ar", "نساعد الشركات على بناء محرك إيرادات ذكي")
return f"مرحباً {contact or 'colleague'}، {hook}. نعمل مع شركات مثل {company} لبناء منظومة مبيعات ذكية. أودّ التواصل معكم."[:300]
def generate_outreach_brief(
lead_dict: Dict,
score_dict: Dict,
motion: str = "sales"
) -> OutreachBrief:
"""
Generate a full outreach brief for a scored lead.
lead_dict: from EnrichedLead.to_dict()
score_dict: from score_lead()
"""
company = lead_dict.get("company_name", "")
contact = lead_dict.get("contact_name", "")
title = lead_dict.get("contact_title", "")
signals = lead_dict.get("signals", [])
tier = score_dict.get("tier", "P3")
angle_data = pick_angle(signals)
email_data = build_email(company, contact, title, angle_data, motion, tier, signals)
personalization = 30
if signals: personalization += 30
if contact: personalization += 20
if title: personalization += 10
if lead_dict.get("recent_news"): personalization += 10
brief = OutreachBrief(
lead_id=lead_dict.get("id", ""),
company_name=company,
contact_name=contact,
contact_title=title,
motion=motion,
whatsapp_ar=build_whatsapp_message(company, contact, angle_data, motion, tier),
email_subject_ar=email_data["subject_ar"],
email_body_ar=email_data["body_ar"],
email_subject_en=email_data["subject_en"],
email_body_en=email_data["body_en"],
linkedin_ar=build_linkedin_message(company, contact, angle_data, motion),
angle=angle_data.get("angle", ""),
pain_hypothesis=angle_data.get("hook_ar", ""),
value_proposition=MOTION_VALUE_PROPS.get(motion, MOTION_VALUE_PROPS["sales"])["ar"],
call_to_action=CTA_BY_TIER.get(tier, CTA_BY_TIER["P3"])["ar"],
personalization_score=min(100, personalization),
generated_by="template",
)
return brief
def generate_batch_briefs(
scored_leads: List[Dict], motion: str = "sales"
) -> List[Dict]:
"""Generate outreach briefs for a list of scored leads (P1+P2 only by default)"""
briefs = []
for item in scored_leads:
tier = item.get("score", {}).get("tier", "P4")
if tier in ("P1", "P2"): # Only generate for actionable leads
brief = generate_outreach_brief(item["lead"], item["score"], motion)
briefs.append({
"company": brief.company_name,
"tier": tier,
"angle": brief.angle,
"whatsapp_ar": brief.whatsapp_ar,
"email_subject_ar": brief.email_subject_ar,
"email_body_ar": brief.email_body_ar,
"linkedin_ar": brief.linkedin_ar,
"personalization_score": brief.personalization_score,
})
return briefs

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"""
Lead Intelligence Pipeline — End-to-end orchestrator
ICP → Discovery → Enrichment → Entity Resolution → Scoring → Outreach Brief
One call drives the full flow.
"""
import time
import json
from typing import Dict, Any, List, Optional
from dataclasses import asdict
from app.intelligence.icp import ICPConfig, DEALIX_DEFAULT_ICP
from app.intelligence.discovery import LeadDiscoveryEngine
from app.intelligence.enrichment import enrich_batch
from app.intelligence.scoring import score_batch
from app.intelligence.entity_resolution import EntityRegistry
from app.intelligence.outreach import generate_batch_briefs
def run_pipeline(
icp: Optional[ICPConfig] = None,
custom_queries: Optional[List[str]] = None,
motion: str = "sales",
max_leads: int = 30,
enrich: bool = True,
generate_outreach: bool = True,
score_weights: Optional[Dict[str, float]] = None,
) -> Dict[str, Any]:
"""
Full lead intelligence pipeline.
Returns:
{
"run_id": str,
"icp_used": dict,
"total_discovered": int,
"total_after_dedup": int,
"total_enriched": int,
"scored_leads": [...], # all leads sorted by score
"p1_leads": [...], # outreach now
"p2_leads": [...], # enrich more
"p3_leads": [...], # nurture
"outreach_briefs": [...], # generated briefs for P1+P2
"tier_summary": {...},
"pipeline_duration_sec": float,
"errors": [...],
}
"""
start_time = time.time()
run_id = f"pipeline_{int(start_time)}"
errors = []
# 1. Resolve ICP
icp = icp or DEALIX_DEFAULT_ICP
# 2. Discovery
engine = LeadDiscoveryEngine(icp=icp)
if custom_queries:
candidates = engine.discover(custom_queries, max_per_query=6)
else:
candidates = engine.discover_from_icp(icp=icp, max_per_query=5)
total_discovered = len(candidates)
# 3. Entity Resolution + Dedup
registry = EntityRegistry()
raw_lead_dicts = [
{
"id": c.id,
"company_name": c.company_name,
"domain": c.domain,
"source": c.source,
"source_url": c.source_url,
"raw_snippet": c.raw_snippet,
"signals": c.signals,
"trigger": c.trigger,
"contact_email": c.contact_email,
"contact_phone": c.phone,
"contact_linkedin": c.contact_linkedin,
"confidence": c.confidence,
"_candidate": c,
}
for c in candidates
]
deduped_dicts = registry.deduplicate_lead_list(raw_lead_dicts)
deduped_candidates = [d["_candidate"] for d in deduped_dicts[:max_leads]]
total_after_dedup = len(deduped_candidates)
# 4. Enrichment
enriched_leads = []
if enrich:
enriched_leads = enrich_batch(deduped_candidates, delay=0.2)
else:
# Skip enrichment — use candidates as-is
from app.intelligence.enrichment import EnrichedLead
for c in deduped_candidates:
e = EnrichedLead(
id=c.id,
company_name=c.company_name,
domain=c.domain,
industry=c.industry,
region=c.region,
website=f"https://{c.domain}" if c.domain else "",
signals=c.signals,
source=c.source,
source_url=c.source_url,
raw_snippet=c.raw_snippet,
trigger=c.trigger,
contact_email=c.contact_email,
contact_phone=c.phone,
enrichment_confidence=c.confidence,
)
enriched_leads.append(e)
total_enriched = len(enriched_leads)
# 5. Scoring
scored = score_batch(enriched_leads, weights=score_weights)
# 6. Tier breakdown
tier_counts = {"P1": 0, "P2": 0, "P3": 0, "P4": 0}
p1, p2, p3, p4 = [], [], [], []
for item in scored:
tier = item["score"]["tier"]
tier_counts[tier] += 1
if tier == "P1": p1.append(item)
elif tier == "P2": p2.append(item)
elif tier == "P3": p3.append(item)
else: p4.append(item)
# 7. Outreach briefs
outreach_briefs = []
if generate_outreach:
outreach_briefs = generate_batch_briefs(scored, motion=motion)
duration = round(time.time() - start_time, 2)
return {
"run_id": run_id,
"icp_used": icp.to_dict() if hasattr(icp, 'to_dict') else {},
"total_discovered": total_discovered,
"total_after_dedup": total_after_dedup,
"total_enriched": total_enriched,
"scored_leads": scored,
"p1_leads": p1,
"p2_leads": p2,
"p3_leads": p3,
"p4_leads": p4,
"outreach_briefs": outreach_briefs,
"tier_summary": {
"P1_outreach_now": tier_counts["P1"],
"P2_enrich_more": tier_counts["P2"],
"P3_nurture": tier_counts["P3"],
"P4_archive": tier_counts["P4"],
},
"pipeline_duration_sec": duration,
"errors": errors,
}

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"""
5-Dimension Lead Scoring Engine
Fit | Intent | Access | Value | Urgency
Master Priority Score = weighted sum → P1/P2/P3/P4 tier
Each dimension returns 0-100. Final score 0-100.
"""
from typing import Dict, Any, Tuple, List
from dataclasses import dataclass
@dataclass
class ScoreBreakdown:
fit_score: int = 0 # Is this company our ICP?
intent_score: int = 0 # Are they showing buying signals?
access_score: int = 0 # Can we reach the right person?
value_score: int = 0 # What's the potential deal value?
urgency_score: int = 0 # Is now the right moment?
master_score: int = 0 # Weighted composite
priority_tier: str = "P4" # P1 | P2 | P3 | P4
priority_label_ar: str = "أرشيف"
score_reasons: List[str] = None
next_action: str = ""
next_action_ar: str = ""
def __post_init__(self):
if self.score_reasons is None:
self.score_reasons = []
# Signal → intent score contribution
INTENT_SIGNAL_WEIGHTS = {
"hiring": 25,
"expansion": 20,
"funding": 30,
"digital_transformation": 20,
"partnership": 15,
"ipo": 35,
"new_product": 10,
"pain_point_crm": 25,
"pain_point_outreach": 20,
"regulation": 15,
}
# Industry → fit contribution (for Dealix ICP)
INDUSTRY_FIT = {
"technology": 100, "tech": 100, "تقنية": 100, "software": 95, "saas": 95,
"banking": 90, "financial": 90, "مالية": 90, "بنوك": 90, "fintech": 95,
"healthcare": 85, "رعاية صحية": 85, "hospital": 80,
"real estate": 80, "عقارات": 80,
"manufacturing": 75, "تصنيع": 75, "industrial": 70,
"retail": 70, "تجزئة": 70, "e-commerce": 80,
"logistics": 75, "لوجستيات": 75, "supply chain": 75,
"education": 65, "تعليم": 65,
"government": 60, "حكومة": 60,
"media": 60, "إعلام": 60,
}
# Company size → value score
SIZE_VALUE = {
"1-50": 30,
"50-200": 55,
"200-1000": 80,
"1000+": 100,
"unknown": 40,
}
# Seniority → access score
SENIORITY_ACCESS = {
range(90, 101): 100, # C-level
range(80, 90): 85, # VP
range(70, 80): 70, # Director
range(55, 70): 55, # Manager
range(0, 55): 30, # Individual contributor
}
PRIORITY_THRESHOLDS = {
"P1": 70, # Outreach now
"P2": 50, # Enrich more
"P3": 35, # Nurture
}
PRIORITY_LABELS_AR = {
"P1": "وصول فوري",
"P2": "إثراء إضافي",
"P3": "تغذية ورعاية",
"P4": "قائمة انتظار",
}
NEXT_ACTIONS = {
"P1": ("Send personalized outreach — high-priority lead", "أرسل رسالة مخصصة — ليد أولوية عالية"),
"P2": ("Enrich contact data, find decision maker", "أثرِ بيانات الاتصال وحدد صانع القرار"),
"P3": ("Add to nurture sequence, monitor signals", "أضف إلى تسلسل التغذية وراقب الإشارات"),
"P4": ("Archive and watch for trigger", "أرشف وراقب الإشارات المستقبلية"),
}
def get_seniority_access_score(decision_maker_score: int) -> int:
for r, score in SENIORITY_ACCESS.items():
if decision_maker_score in r:
return score
return 30
def score_fit(enriched_lead) -> Tuple[int, List[str]]:
"""Score how well this company matches ICP"""
reasons = []
score = 0
# Industry fit
industry = (enriched_lead.industry or enriched_lead.raw_snippet or "").lower()
best_industry_score = 0
for kw, val in INDUSTRY_FIT.items():
if kw in industry:
best_industry_score = max(best_industry_score, val)
if best_industry_score > 0:
score += best_industry_score * 0.5
reasons.append(f"Industry match: {best_industry_score}%")
# Company size fit
size_score = SIZE_VALUE.get(enriched_lead.company_size, 40)
score += size_score * 0.3
if enriched_lead.company_size != "unknown":
reasons.append(f"Size '{enriched_lead.company_size}': {size_score}%")
# Has website / domain
if enriched_lead.domain:
score += 8
reasons.append("Has domain")
# Saudi / Gulf region
text = f"{enriched_lead.headquarters} {enriched_lead.region} {enriched_lead.raw_snippet}".lower()
if any(kw in text for kw in ["saudi", "ksa", "السعودية", "الرياض", "riyadh", "جدة", "jeddah", "الخليج", "gulf"]):
score += 12
reasons.append("Saudi/Gulf region")
return min(100, int(score)), reasons
def score_intent(enriched_lead) -> Tuple[int, List[str]]:
"""Score buying intent based on signals"""
reasons = []
score = 0
for signal in enriched_lead.signals:
contribution = INTENT_SIGNAL_WEIGHTS.get(signal, 5)
score += contribution
reasons.append(f"Signal '{signal}': +{contribution}")
# Recent news adds intent
if enriched_lead.recent_news:
score += min(20, len(enriched_lead.recent_news) * 5)
reasons.append(f"{len(enriched_lead.recent_news)} recent news items")
# Pain point keywords in snippet
text = (enriched_lead.raw_snippet or "").lower()
pain_keywords = ["struggling", "challenge", "problem", "need", "looking for",
"تحدي", "مشكلة", "نحتاج", "نبحث عن"]
if any(kw in text for kw in pain_keywords):
score += 15
reasons.append("Pain point language detected")
return min(100, score), reasons
def score_access(enriched_lead) -> Tuple[int, List[str]]:
"""Score reachability — can we actually contact the right person?"""
reasons = []
score = 0
if enriched_lead.contact_email:
score += 40
reasons.append("Has email")
if enriched_lead.contact_phone:
score += 20
reasons.append("Has phone")
if enriched_lead.contact_linkedin:
score += 25
reasons.append("Has LinkedIn profile")
if enriched_lead.domain:
score += 20
reasons.append("Has domain (email inferable)")
# Decision maker seniority
seniority_score = get_seniority_access_score(enriched_lead.decision_maker_score)
score = int(score * 0.6 + seniority_score * 0.4)
if enriched_lead.contact_title:
reasons.append(f"Title seniority: {seniority_score}%")
return min(100, score), reasons
def score_value(enriched_lead) -> Tuple[int, List[str]]:
"""Estimate potential deal value"""
reasons = []
# Company size as proxy for revenue potential
size_score = SIZE_VALUE.get(enriched_lead.company_size, 40)
# Revenue estimate
rev = enriched_lead.annual_revenue_sar
if rev > 100_000_000:
size_score = max(size_score, 100)
reasons.append(f"Revenue >100M SAR")
elif rev > 10_000_000:
size_score = max(size_score, 80)
reasons.append(f"Revenue >10M SAR")
elif rev > 0:
reasons.append(f"Revenue data available")
# Tech stack indicates budget
if len(enriched_lead.tech_stack) >= 3:
size_score = min(100, size_score + 10)
reasons.append(f"Rich tech stack ({len(enriched_lead.tech_stack)} tools)")
reasons.append(f"Size-based value score: {size_score}%")
return min(100, size_score), reasons
def score_urgency(enriched_lead) -> Tuple[int, List[str]]:
"""Score how urgent the timing is"""
reasons = []
score = 0
# Time-sensitive signals
urgent_signals = {"funding": 40, "ipo": 50, "expansion": 30, "hiring": 20, "new_product": 25}
for sig in enriched_lead.signals:
if sig in urgent_signals:
score += urgent_signals[sig]
reasons.append(f"Urgent signal '{sig}': +{urgent_signals[sig]}")
# Fresh news
if len(enriched_lead.recent_news) >= 2:
score += 15
reasons.append("Multiple recent news items")
# If just discovered (high source confidence)
if enriched_lead.enrichment_confidence >= 0.7:
score += 10
reasons.append("High confidence data")
return min(100, score), reasons
def score_lead(enriched_lead, weights: Dict[str, float] = None) -> ScoreBreakdown:
"""
Compute full 5-dimension score for an enriched lead.
Returns ScoreBreakdown with tier and next action.
"""
if weights is None:
weights = {"fit": 0.30, "intent": 0.25, "access": 0.15, "value": 0.20, "urgency": 0.10}
fit, fit_reasons = score_fit(enriched_lead)
intent, intent_reasons = score_intent(enriched_lead)
access, access_reasons = score_access(enriched_lead)
value, value_reasons = score_value(enriched_lead)
urgency, urgency_reasons = score_urgency(enriched_lead)
master = int(
fit * weights["fit"] +
intent * weights["intent"] +
access * weights["access"] +
value * weights["value"] +
urgency * weights["urgency"]
)
if master >= PRIORITY_THRESHOLDS["P1"]:
tier = "P1"
elif master >= PRIORITY_THRESHOLDS["P2"]:
tier = "P2"
elif master >= PRIORITY_THRESHOLDS["P3"]:
tier = "P3"
else:
tier = "P4"
all_reasons = (
[f"[Fit {fit}]"] + fit_reasons[:2] +
[f"[Intent {intent}]"] + intent_reasons[:2] +
[f"[Access {access}]"] + access_reasons[:2] +
[f"[Value {value}]"] + value_reasons[:1] +
[f"[Urgency {urgency}]"] + urgency_reasons[:1]
)
en_action, ar_action = NEXT_ACTIONS[tier]
return ScoreBreakdown(
fit_score=fit,
intent_score=intent,
access_score=access,
value_score=value,
urgency_score=urgency,
master_score=master,
priority_tier=tier,
priority_label_ar=PRIORITY_LABELS_AR[tier],
score_reasons=all_reasons,
next_action=en_action,
next_action_ar=ar_action,
)
def score_batch(enriched_leads: List, weights: Dict[str, float] = None) -> List[Dict]:
"""Score a batch of enriched leads and return sorted results"""
results = []
for lead in enriched_leads:
breakdown = score_lead(lead, weights)
results.append({
"lead": lead.to_dict(),
"score": {
"fit": breakdown.fit_score,
"intent": breakdown.intent_score,
"access": breakdown.access_score,
"value": breakdown.value_score,
"urgency": breakdown.urgency_score,
"master": breakdown.master_score,
"tier": breakdown.priority_tier,
"tier_label_ar": breakdown.priority_label_ar,
"reasons": breakdown.score_reasons,
"next_action": breakdown.next_action,
"next_action_ar": breakdown.next_action_ar,
}
})
# Sort by master score descending
results.sort(key=lambda x: x["score"]["master"], reverse=True)
return results

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salesflow-saas/backend/app/intelligence/triggers.py Normal file
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"""
Trigger Alert System — Real-time intent signal detection
Monitors: job postings, news, funding, expansion, partnerships, regulatory changes.
Runs as background scan and emits trigger events per lead/company.
"""
import re
import time
import json
import urllib.request
import urllib.parse
from typing import List, Dict, Any, Optional
from dataclasses import dataclass, field
@dataclass
class TriggerEvent:
"""A detected trigger event for a company"""
company_name: str
trigger_type: str # hiring | funding | expansion | ipo | partnership | regulation | news
trigger_label_ar: str
signal_strength: int # 0-100
evidence: str # snippet or description
source_url: str
detected_at: str
recommended_action_ar: str
recommended_action_en: str
TRIGGER_DEFINITIONS = {
"hiring": {
"label_ar": "توظيف نشط",
"queries": ["{company} hiring 2025", "{company} وظائف 2025", "{company} jobs"],
"keywords": ["hiring", "join our team", "we're looking", "وظائف", "نوظف", "فرص عمل"],
"strength": 60,
"action_ar": "اتصل الآن — الشركة توسّع فريقها وستحتاج منظومة مبيعات",
"action_en": "Reach out now — they're scaling and will need a sales OS",
},
"funding": {
"label_ar": "تمويل جديد",
"queries": ["{company} funding 2025", "{company} investment raised", "{company} تمويل"],
"keywords": ["raised", "funding", "series", "investment", "تمويل", "استثمار", "جولة"],
"strength": 90,
"action_ar": "أولوية قصوى — اتصل خلال 48 ساعة من التمويل",
"action_en": "Top priority — contact within 48 hours of funding",
},
"expansion": {
"label_ar": "توسع جديد",
"queries": ["{company} expansion 2025", "{company} new office", "{company} توسع"],
"keywords": ["expansion", "new market", "new office", "opens", "توسع", "افتتاح", "سوق جديد"],
"strength": 75,
"action_ar": "تواصل حول كيفية دعم توسعهم بمنظومة إيرادات",
"action_en": "Reach out about supporting their expansion with a revenue system",
},
"partnership": {
"label_ar": "شراكة جديدة",
"queries": ["{company} partnership 2025", "{company} شراكة"],
"keywords": ["partnership", "collaboration", "alliance", "شراكة", "تعاون", "تحالف"],
"strength": 55,
"action_ar": "استفسر عن فرص الشراكة الاستراتيجية",
"action_en": "Inquire about strategic partnership opportunities",
},
"ipo": {
"label_ar": "استعداد للطرح العام",
"queries": ["{company} IPO 2025 2026", "{company} اكتتاب طرح عام"],
"keywords": ["ipo", "initial public offering", "طرح عام", "اكتتاب", "تداول"],
"strength": 95,
"action_ar": "طوارئ — الطرح العام يستلزم منظومة إيرادات موثوقة وقابلة للتدقيق",
"action_en": "Emergency priority — IPO demands auditable, reliable revenue infrastructure",
},
"digital_transformation": {
"label_ar": "تحول رقمي",
"queries": ["{company} digital transformation", "{company} تحول رقمي", "{company} digitization"],
"keywords": ["digital transformation", "digitization", "modernization", "تحول رقمي", "رقمنة"],
"strength": 65,
"action_ar": "اعرض كيف Dealix يُكمّل مبادرة التحول الرقمي لديهم",
"action_en": "Show how Dealix completes their digital transformation initiative",
},
"regulation": {
"label_ar": "تغيير تنظيمي",
"queries": ["{company} PDPL ZATCA compliance 2025", "{company} حوكمة ضريبة"],
"keywords": ["pdpl", "zatca", "compliance", "regulation", "حوكمة", "امتثال", "ضريبة"],
"strength": 50,
"action_ar": "ناقش كيف Dealix يُساعد على الامتثال التنظيمي",
"action_en": "Discuss how Dealix supports regulatory compliance",
},
}
def search_triggers_for_company(company_name: str, trigger_type: str) -> List[Dict]:
"""Search for trigger signals for a specific company"""
definition = TRIGGER_DEFINITIONS.get(trigger_type, {})
queries = definition.get("queries", [])
keywords = definition.get("keywords", [])
results = []
for query_template in queries[:2]: # Limit queries per trigger
query = query_template.replace("{company}", company_name)
try:
encoded = urllib.parse.quote(query)
url = f"https://html.duckduckgo.com/html/?q={encoded}"
req = urllib.request.Request(
url,
headers={"User-Agent": "Mozilla/5.0 (compatible; DealixBot/1.0)"}
)
with urllib.request.urlopen(req, timeout=6) as resp:
html = resp.read().decode('utf-8', errors='ignore')
snippets = re.findall(r'<a class="result__snippet"[^>]*>(.*?)</a>', html)
urls = re.findall(r'<a class="result__a" href="([^"]+)"', html)
for i, snippet in enumerate(snippets[:3]):
clean_snippet = re.sub(r'<[^>]+>', '', snippet).strip().lower()
if any(kw in clean_snippet for kw in keywords):
results.append({
"snippet": re.sub(r'<[^>]+>', '', snippet).strip(),
"url": urls[i] if i < len(urls) else "",
"query": query,
})
except Exception:
pass
time.sleep(0.3)
return results
def scan_company_for_triggers(company_name: str) -> List[TriggerEvent]:
"""Scan all trigger types for a given company"""
events = []
now = time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())
for trigger_type, definition in TRIGGER_DEFINITIONS.items():
results = search_triggers_for_company(company_name, trigger_type)
if results:
best = results[0]
event = TriggerEvent(
company_name=company_name,
trigger_type=trigger_type,
trigger_label_ar=definition["label_ar"],
signal_strength=definition["strength"],
evidence=best["snippet"][:500],
source_url=best["url"][:300],
detected_at=now,
recommended_action_ar=definition["action_ar"],
recommended_action_en=definition["action_en"],
)
events.append(event)
return events
def scan_watchlist(company_names: List[str], delay: float = 1.0) -> Dict[str, List[Dict]]:
"""
Scan a watchlist of companies for all trigger types.
Returns dict: {company_name: [trigger_event_dicts]}
"""
all_triggers = {}
for company in company_names:
events = scan_company_for_triggers(company)
if events:
all_triggers[company] = [
{
"type": e.trigger_type,
"label_ar": e.trigger_label_ar,
"strength": e.signal_strength,
"evidence": e.evidence,
"url": e.source_url,
"detected_at": e.detected_at,
"action_ar": e.recommended_action_ar,
"action_en": e.recommended_action_en,
}
for e in events
]
time.sleep(delay)
return all_triggers
def get_strongest_trigger(events: List[Dict]) -> Optional[Dict]:
"""Return the highest-priority trigger from a list"""
if not events:
return None
return max(events, key=lambda e: e.get("strength", 0))

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salesflow-saas/backend/main.py Normal file
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"""Dealix Sovereign Revenue, Deal, Growth & Commitment OS — Backend"""
from flask import Flask, jsonify
from flask_cors import CORS
from app.core.database import init_db
from app.api.routes.auth import auth_bp
from app.api.routes.revenue import revenue_bp
from app.api.routes.pricing import pricing_bp
from app.api.routes.partnership import partnership_bp
from app.api.routes.procurement import procurement_bp
from app.api.routes.renewal import renewal_bp
from app.api.routes.expansion import expansion_bp
from app.api.routes.ma import ma_bp
from app.api.routes.pmo import pmo_bp
from app.api.routes.executive import executive_bp
from app.api.routes.intelligence import intelligence_bp
app = Flask(__name__)
CORS(app, resources={r"/*": {"origins": "*"}}, supports_credentials=True)
# Register all 9 OS blueprints
app.register_blueprint(auth_bp)
app.register_blueprint(revenue_bp)
app.register_blueprint(pricing_bp)
app.register_blueprint(partnership_bp)
app.register_blueprint(procurement_bp)
app.register_blueprint(renewal_bp)
app.register_blueprint(expansion_bp)
app.register_blueprint(ma_bp)
app.register_blueprint(pmo_bp)
app.register_blueprint(executive_bp)
app.register_blueprint(intelligence_bp) # Revenue Intelligence OS — Lead Machine
@app.get("/api/health")
def health():
from app.core.database import db
with db() as conn:
count = conn.execute("SELECT COUNT(*) as c FROM audit_log").fetchone()["c"]
modules = conn.execute("SELECT COUNT(DISTINCT module) as m FROM audit_log").fetchone()["m"]
return jsonify({
"status": "healthy",
"database": "connected",
"audit_entries": count,
"active_modules": modules,
"modules": [
"Revenue OS", "Pricing & Margin Control OS", "Partnership & Alliance OS",
"Procurement & Vendor OS", "Renewal & Expansion OS", "Market Entry OS",
"M&A Corporate Development OS", "PMI Strategic PMO OS", "Executive Board OS"
]
})
@app.get("/")
def root():
return jsonify({
"product": "Dealix",
"tagline": "Sovereign Revenue, Deal, Growth & Commitment OS",
"version": "2.0.0",
"modules": 9,
"docs": "/api/health"
})
if __name__ == "__main__":
init_db()
app.run(host="0.0.0.0", port=8000, debug=False)