mirror of
https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools.git
synced 2026-06-18 15:29:36 +00:00
510 lines
19 KiB
Python
510 lines
19 KiB
Python
"""
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Rules Router — deterministic, zero-LLM lead classification + scoring + messaging.
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Works fully without any LLM API key. Uses explicit rules over:
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- sector keywords
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- signals (from tech_detect output OR manual)
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- company hints (name/URL/country)
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Produces:
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- opportunity_type (9 types)
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- fit_score / intent_score / access_score / revenue_score (100-pt model)
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- priority_tier (P0/P1/P2/BACKLOG)
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- risk_level (LOW/MEDIUM/HIGH/BLOCKED)
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- recommended_channel
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- next_action
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- first_message_angle
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- compliance_note
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This is the "graceful degraded mode" backbone. When LLM becomes available, it
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can layer on top of this — but the rules alone are production-usable today.
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"""
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from __future__ import annotations
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import re
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from dataclasses import dataclass, asdict
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from typing import Any
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# ── Keyword taxonomies (lowercased substring match) ───────────
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AGENCY_KEYWORDS = {
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"agency", "digital marketing", "performance marketing", "media agency",
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"creative", "branding", "pr agency", "paid ads", "content agency",
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"وكالة", "تسويق", "إعلام", "إبداع",
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}
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IMPL_PARTNER_KEYWORDS = {
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"crm consultant", "hubspot partner", "salesforce partner", "revops",
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"implementation partner", "automation consultant", "zapier expert",
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"make expert", "integration services",
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}
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STRATEGIC_KEYWORDS = {
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"platform", "marketplace", "ecosystem", "payment gateway", "crm vendor",
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"accelerator", "incubator", "manso'ah", "misk", "kaust",
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"accounting saas", "wafeq", "qoyod", "dafater",
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"tap payments", "moyasar", "paytabs", "hyperpay", "stc pay",
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"salla", "zid", "shopify", "foodics",
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}
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INVESTOR_KEYWORDS = {
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"investor", "venture", "capital", "vc", "angel", "fund",
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"sanabil", "stv", "wamda", "raed", "500 startups", "gate ventures",
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"arzan", "vision ventures", "investment",
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}
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CONTENT_KEYWORDS = {
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"podcast", "newsletter", "community", "creator", "writer", "influencer",
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"content platform", "thought leader", "founder community",
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"مجتمع", "بودكاست", "نشرة",
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}
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SUPPLIER_KEYWORDS = {
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"supplier", "vendor", "tool", "integration partner",
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}
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B2C_KEYWORDS = {
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"delivery", "retail", "consumer", "b2c", "ecommerce",
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"food delivery", "grocery", "fashion",
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}
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DIRECT_CUSTOMER_SECTORS = {
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"saas", "fintech", "proptech", "contech", "edtech", "healthtech",
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"logistics", "marketplace", "b2b marketplace",
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"hr tech", "hr saas", "cxm", "restaurant", "ecom platform",
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}
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# ── Intent signal names (match against detected signals) ──────
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INTENT_SIGNALS = {
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"uses booking tool": 5,
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"CRM in use": 6,
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"marketing automation": 4,
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"payment gateway": 3,
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"MENA payment gateway": 5,
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"e-commerce platform": 4,
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"Salla/Zid merchant": 8,
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"live chat": 3,
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"WhatsApp widget": 8,
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"analytics active": 3,
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"running paid ads": 6,
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"inbound form": 5,
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"CMS": 1,
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"framework": 1,
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}
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FIT_ANCHORS = {
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"saas": 10, "fintech": 9, "proptech": 8, "contech": 8,
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"restaurant": 8, "ecom": 8, "marketplace": 9,
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"cxm": 9, "hr tech": 7, "hr saas": 8, "logistics": 7,
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"edtech": 6, "healthtech": 5, "agency": 8,
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"retail": 5, "telecom": 4,
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}
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CHANNELS_BY_TYPE = {
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"DIRECT_CUSTOMER": "LINKEDIN_MANUAL",
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"AGENCY_PARTNER": "LINKEDIN_MANUAL",
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"IMPLEMENTATION_PARTNER": "LINKEDIN_MANUAL",
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"REFERRAL_PARTNER": "LINKEDIN_MANUAL",
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"STRATEGIC_PARTNER": "PARTNER_INTRO",
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"CONTENT_COLLABORATION": "LINKEDIN_MANUAL",
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"INVESTOR_OR_ADVISOR": "EMAIL",
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"SUPPLIER_OR_INTEGRATION":"EMAIL",
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"B2C_AUDIENCE": "CONTENT_MENTION",
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}
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NEXT_ACTION_BY_TYPE = {
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"DIRECT_CUSTOMER": "PREPARE_DM",
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"AGENCY_PARTNER": "PREPARE_PARTNER_PITCH",
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"IMPLEMENTATION_PARTNER": "PREPARE_PARTNER_PITCH",
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"REFERRAL_PARTNER": "PREPARE_PARTNER_PITCH",
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"STRATEGIC_PARTNER": "PREPARE_PARTNER_PITCH",
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"CONTENT_COLLABORATION": "PREPARE_DM",
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"INVESTOR_OR_ADVISOR": "PREPARE_INVESTOR_NOTE",
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"SUPPLIER_OR_INTEGRATION":"RESEARCH_MORE",
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"B2C_AUDIENCE": "RESEARCH_MORE",
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}
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MESSAGE_ANGLES = {
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"DIRECT_CUSTOMER": (
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"AI sales rep بالعربي يرد على leads خلال 45 ثانية، يؤهّل، ويحجز demo — "
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"يركب فوق CRM الحالي ويسلّم sequence جاهزة للـ SDR."
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),
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"AGENCY_PARTNER": (
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"للوكالات: setup 3-15K + 20-30% من MRR كل عميل دائم. "
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"3-5 عملاء = revenue stream جديد بدون tech build."
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),
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"IMPLEMENTATION_PARTNER": (
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"شريك تنفيذ: Dealix يوفر الطبقة، أنت تقدم setup + retainer لعملائك."
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),
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"REFERRAL_PARTNER": (
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"referral 10% من MRR لـ 12 شهر على كل عميل يجي عبرك — صفر setup."
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),
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"STRATEGIC_PARTNER": (
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"Dealix + منتجك = offering مكمّل لعملائكم. add-on أو bundle — نناقش النسب."
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),
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"CONTENT_COLLABORATION": (
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"محتوى سعودي حول AI sales + GTM — تعاون podcast/newsletter/سلسلة."
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),
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"INVESTOR_OR_ADVISOR": (
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"Dealix = Arabic-first AI sales operator. نبحث عن portfolio introductions + advisory."
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),
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"SUPPLIER_OR_INTEGRATION": (
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"integration أو supply مقترح — نناقش التفاصيل."
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),
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"B2C_AUDIENCE": (
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"جمهور B2C — تفعيل عبر content + paid عبر الوكالات/الشركاء."
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),
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}
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COMPLIANCE_NOTES = {
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"LOW": "Public business contact only; no personal PII used.",
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"MEDIUM": "Public business contact from public source; single personalized manual DM/email; no bots.",
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"HIGH": "Personal PII path — requires explicit human approval before outreach.",
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"BLOCKED":"Source or channel disallowed; do not contact without legal review.",
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}
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@dataclass
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class RouteResult:
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opportunity_type: str
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fit_score: int
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intent_score: int
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access_score: int
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revenue_score: int
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priority_score: int
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priority_tier: str
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risk_level: str
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recommended_channel: str
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next_action: str
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first_message_angle: str
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human_approval_required: bool
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compliance_note: str
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reason: str
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def to_dict(self) -> dict[str, Any]:
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return asdict(self)
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def _contains_any(text: str, bag: set[str]) -> bool:
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t = (text or "").lower()
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return any(k in t for k in bag)
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def _classify_opportunity(*, sector: str, company: str, tags: str) -> str:
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combined = " ".join([sector or "", company or "", tags or ""]).lower()
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if _contains_any(combined, AGENCY_KEYWORDS):
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return "AGENCY_PARTNER"
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if _contains_any(combined, IMPL_PARTNER_KEYWORDS):
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return "IMPLEMENTATION_PARTNER"
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if _contains_any(combined, INVESTOR_KEYWORDS):
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return "INVESTOR_OR_ADVISOR"
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if _contains_any(combined, CONTENT_KEYWORDS):
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return "CONTENT_COLLABORATION"
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if _contains_any(combined, STRATEGIC_KEYWORDS):
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# Only flag strategic if it's a platform/ecosystem — not e.g. a customer using salla
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if any(k in combined for k in ("platform", "ecosystem", "marketplace", "vendor", "accelerator", "partner", "accounting saas")):
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return "STRATEGIC_PARTNER"
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if _contains_any(combined, SUPPLIER_KEYWORDS):
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return "SUPPLIER_OR_INTEGRATION"
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if _contains_any(combined, B2C_KEYWORDS):
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# Only B2C_AUDIENCE if consumer-final, not b2b-ecom platform
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if "b2c" in combined or "consumer" in combined:
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return "B2C_AUDIENCE"
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# Default — direct customer
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return "DIRECT_CUSTOMER"
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def _score(
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*,
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opportunity_type: str,
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sector: str,
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signals: list[dict],
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country: str,
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has_decision_maker: bool,
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size_hint: str,
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) -> tuple[int, int, int, int]:
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"""Returns (fit, intent, access, revenue) — each capped at max."""
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s_text = (sector or "").lower()
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fit = 0
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for anchor, pts in FIT_ANCHORS.items():
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if anchor in s_text:
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fit = max(fit, pts)
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# Saudi / GCC market bump
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if country and country.upper() in {"SA", "KSA", "AE-SA", "SA-GCC"}:
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fit += 5
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elif country and country.upper() in {"AE", "KW", "QA", "BH", "OM"}:
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fit += 2
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# Size fit (sweet spot 20-500)
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size_bump = 5 if size_hint in {"10-50", "50-200", "200-1000"} else 2
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fit += size_bump
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# Lead flow + sales workflow — credit if signals include forms/CRM/booking
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sig_names = [s.get("name", "").lower() for s in (signals or [])]
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sig_evid = [s.get("evidence", "").lower() for s in (signals or [])]
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sig_all = " ".join(sig_names + sig_evid)
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if "form" in sig_all or "inbound form" in sig_all:
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fit += 5
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if "crm" in sig_all:
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fit += 5
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if "booking" in sig_all:
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fit += 5
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fit = min(fit, 40)
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# Intent
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intent = 0
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for sig in (signals or []):
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name = sig.get("name", "")
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w = sig.get("weight", 0)
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# Look up known intent signal weights
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for k, default in INTENT_SIGNALS.items():
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if k.lower() in name.lower():
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intent += min(w or default, default)
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break
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intent = min(intent, 30)
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# Access
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access = 5 if has_decision_maker else 3
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if opportunity_type in {"DIRECT_CUSTOMER", "AGENCY_PARTNER"}:
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access += 5 # public LinkedIn path
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else:
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access += 3
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# personalization angle — any non-trivial signal counts
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access += 5 if len(signals or []) >= 2 else (3 if signals else 2)
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access = min(access, 15)
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# Revenue
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revenue = 5 # pilot affordable for anyone w/ real business
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if opportunity_type in {"AGENCY_PARTNER", "STRATEGIC_PARTNER", "REFERRAL_PARTNER"}:
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revenue += 7 # partner distribution multiplies
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elif opportunity_type == "DIRECT_CUSTOMER":
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revenue += 7 # retainer potential
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else:
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revenue += 3
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revenue += 3 # default partner expansion credit
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revenue = min(revenue, 15)
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return fit, intent, access, revenue
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def _tier(priority_score: int) -> str:
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if priority_score >= 80:
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return "P0"
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if priority_score >= 65:
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return "P1"
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if priority_score >= 45:
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return "P2"
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return "BACKLOG"
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def _risk(
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*,
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opportunity_type: str,
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has_decision_maker: bool,
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contact_channel: str,
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is_government: bool,
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) -> str:
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if is_government:
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return "HIGH"
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if contact_channel.upper() in {"PHONE", "WHATSAPP_UNKNOWN"}:
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return "HIGH"
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if opportunity_type in {"DIRECT_CUSTOMER", "AGENCY_PARTNER", "REFERRAL_PARTNER"}:
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return "LOW" if has_decision_maker else "MEDIUM"
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if opportunity_type == "INVESTOR_OR_ADVISOR":
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return "MEDIUM"
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return "LOW"
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def route_account(
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*,
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company: str,
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sector: str = "",
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country: str = "",
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domain: str = "",
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signals: list[dict] | None = None,
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tags: str = "",
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decision_maker: str | None = None,
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size_hint: str = "",
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is_government: bool = False,
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desired_goal: str | None = None,
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) -> RouteResult:
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"""
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Classify + score + route — deterministic, no LLM required.
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Returns a RouteResult ready for the Lead Output Schema.
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"""
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signals = signals or []
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opportunity_type = _classify_opportunity(
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sector=sector, company=company, tags=tags
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)
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# If explicit hint provided by caller, prefer it
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if desired_goal:
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hint = desired_goal.upper().replace("-", "_").replace(" ", "_")
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if hint in set(CHANNELS_BY_TYPE.keys()):
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opportunity_type = hint
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has_dm = bool(decision_maker and decision_maker.strip())
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fit, intent, access, revenue = _score(
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opportunity_type=opportunity_type,
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sector=sector,
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signals=signals,
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country=country,
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has_decision_maker=has_dm,
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size_hint=size_hint,
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)
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priority_score = fit + intent + access + revenue
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tier = _tier(priority_score)
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channel = CHANNELS_BY_TYPE.get(opportunity_type, "LINKEDIN_MANUAL")
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risk = _risk(
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opportunity_type=opportunity_type,
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has_decision_maker=has_dm,
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contact_channel=channel,
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is_government=is_government,
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)
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human_approval = risk in {"HIGH", "BLOCKED"}
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if human_approval:
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channel = "HOLD_FOR_APPROVAL"
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reason_parts = []
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if country:
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reason_parts.append(f"market={country}")
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if sector:
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reason_parts.append(f"sector={sector}")
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if signals:
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reason_parts.append(f"signals={len(signals)}")
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if has_dm:
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reason_parts.append("DM known")
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reason = " · ".join(reason_parts) or "baseline classification"
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return RouteResult(
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opportunity_type=opportunity_type,
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fit_score=fit,
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intent_score=intent,
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access_score=access,
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revenue_score=revenue,
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priority_score=priority_score,
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priority_tier=tier,
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risk_level=risk,
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recommended_channel=channel,
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next_action=NEXT_ACTION_BY_TYPE.get(opportunity_type, "RESEARCH_MORE"),
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first_message_angle=MESSAGE_ANGLES.get(opportunity_type, ""),
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human_approval_required=human_approval,
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compliance_note=COMPLIANCE_NOTES[risk],
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reason=reason,
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)
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# ── Message generator ──────────────────────────────────────────
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def _primary_signal(signals: list[dict]) -> dict | None:
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if not signals:
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return None
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return max(signals, key=lambda s: s.get("weight", 0))
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def generate_messages(
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*,
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company: str,
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decision_maker: str | None,
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opportunity_type: str,
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signals: list[dict] | None = None,
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calendly_url: str = "https://calendly.com/sami-assiri11/dealix-demo",
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partners_url: str = "https://dealix.me/partners.html",
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) -> dict[str, str]:
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"""Return LinkedIn DM + email + WhatsApp (warm only) + 3 follow-ups."""
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name = decision_maker or f"فريق {company}"
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sig = _primary_signal(signals or [])
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sig_evid = sig.get("evidence", "") if sig else ""
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sig_name = sig.get("name", "") if sig else ""
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def linkedin_direct() -> str:
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hook = ""
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if "WhatsApp" in sig_evid:
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hook = f"لاحظت إن {company} تستخدم WhatsApp كقناة مبيعات رئيسية — Dealix يضاعف الاستجابة بردود عربية خلال 45 ثانية، يؤهّل، ويحجز demo قبل ما يبرد."
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elif "CRM" in sig_name:
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hook = f"لاحظت إن {company} تستخدم {sig_evid} — Dealix يركب فوقه: يرد بالعربي، يؤهّل BANT، ويسلّم سجل جاهز داخل نفس الـ CRM."
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elif "booking" in sig_name:
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hook = f"لاحظت {sig_evid} عند {company} — Dealix يسبقه: يرد، يؤهّل، ويحجز slot في نفس الأداة."
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elif "paid ads" in sig_name or "ads" in sig_name:
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hook = f"{company} تدير حملات مدفوعة. المشكلة الشائعة بعد click: lead يدخل funnel، الرد بطيء، CPA يرتفع. Dealix يرد بالعربي خلال 45 ثانية."
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elif "Salla" in sig_evid or "Zid" in sig_evid or "ecom_mena" in sig_name:
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hook = f"{company} على منصة {sig_evid} — Dealix يرد على استفسارات المتجر بالعربي، يؤكد الطلب، ويسلّم للـ agent فقط عند negotiation."
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else:
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hook = f"Dealix = AI sales rep بالعربي يرد على leads خلال 45 ثانية، يؤهّل، ويحجز demo — فوق CRM الحالي."
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return (
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f"{name} مرحباً،\n\n{hook}\n\n"
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f"20 دقيقة demo نشوف مناسبته لـ {company}؟\n"
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f"📅 {calendly_url}\n\n"
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f"سامي — Dealix"
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)
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def linkedin_partner() -> str:
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return (
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f"{name} السلام عليكم،\n\n"
|
|
f"{company} تقدّم خدمات تسويق/CRM/automation لعملاء B2B. Dealix يضاعف قيمة خدمتك:\n"
|
|
f"- AI sales rep بالعربي فوق عملاء {company}\n"
|
|
f"- setup 3-15K ريال + 20-30% من MRR كل عميل دائم\n"
|
|
f"- 3-5 عملاء = 1,500-3,750 ريال شهري إضافي بدون tech build\n\n"
|
|
f"20 دقيقة partner meeting هذا الأسبوع؟\n"
|
|
f"🤝 {partners_url}\n📅 {calendly_url}\n\nسامي"
|
|
)
|
|
|
|
def linkedin_strategic() -> str:
|
|
return (
|
|
f"{name} مرحباً،\n\n"
|
|
f"Dealix = Arabic-first AI sales ops layer. {company} منصة / ecosystem مكمّل لذلك.\n"
|
|
f"اقتراح شراكة استراتيجية: بحث add-on داخل منصتكم أو bundle مشترك.\n"
|
|
f"20 دقيقة نستكشف الفكرة؟\n📅 {calendly_url}\n\nسامي"
|
|
)
|
|
|
|
def linkedin_investor() -> str:
|
|
return (
|
|
f"{name} السلام عليكم،\n\n"
|
|
f"Dealix = Arabic-first AI sales operator للسوق السعودي. "
|
|
f"نبحث عن advisor/investor familiar with B2B SaaS + MENA GTM. "
|
|
f"15 دقيقة مكالمة نستكشف fit + possible portfolio introductions؟\n"
|
|
f"📅 {calendly_url}\n\nسامي"
|
|
)
|
|
|
|
def email_variant(base_linkedin: str) -> str:
|
|
return (
|
|
base_linkedin
|
|
+ "\n\n---\n"
|
|
"لإيقاف هذه الرسائل، رد بكلمة: لا شكراً. نحترم رغبتك فوراً."
|
|
)
|
|
|
|
def whatsapp_warm() -> str:
|
|
return (
|
|
f"السلام عليكم،\n"
|
|
f"سامي من Dealix. AI sales rep بالعربي — يرد، يؤهّل، يحجز demo.\n"
|
|
f"مناسب نتكلم 10 دقائق؟"
|
|
)
|
|
|
|
picker = {
|
|
"DIRECT_CUSTOMER": linkedin_direct,
|
|
"AGENCY_PARTNER": linkedin_partner,
|
|
"IMPLEMENTATION_PARTNER": linkedin_partner,
|
|
"REFERRAL_PARTNER": linkedin_partner,
|
|
"STRATEGIC_PARTNER": linkedin_strategic,
|
|
"CONTENT_COLLABORATION": linkedin_direct,
|
|
"INVESTOR_OR_ADVISOR": linkedin_investor,
|
|
"SUPPLIER_OR_INTEGRATION":linkedin_direct,
|
|
"B2C_AUDIENCE": linkedin_direct,
|
|
}
|
|
base = picker.get(opportunity_type, linkedin_direct)()
|
|
|
|
return {
|
|
"linkedin": base,
|
|
"email": email_variant(base),
|
|
"whatsapp_warm_only": whatsapp_warm(),
|
|
"follow_up_plus_2": (
|
|
f"{name} تذكير سريع للرسالة السابقة — هل فرصة لـ 15 دقيقة demo هذا الأسبوع؟\n📅 {calendly_url}"
|
|
),
|
|
"follow_up_plus_5": (
|
|
f"{name} مرحباً — شاركت لك case study قصير عن شركة سعودية حصلت نتائج في 7 أيام. "
|
|
f"أرسله؟ [أو فقط قل: اهتمام/لاحقاً/لا]"
|
|
),
|
|
"follow_up_plus_10": (
|
|
f"{name} آخر متابعة — لو ما هو الوقت المناسب حالياً، تمام.\n"
|
|
f"سؤال أخير: هل تعرف شركة ثانية في السعودية قد تستفيد؟ "
|
|
f"referral 10% من MRR لـ 12 شهر."
|
|
),
|
|
}
|