diff --git a/salesflow-saas/backend/app/services/local_inference.py b/salesflow-saas/backend/app/services/local_inference.py
new file mode 100644
index 00000000..90d515f1
--- /dev/null
+++ b/salesflow-saas/backend/app/services/local_inference.py
@@ -0,0 +1,229 @@
+"""
+Local Inference Adapter — Dealix AI Revenue OS
+Connects to local/private LLM providers (Ollama, LM Studio, Atomic Chat)
+via OpenAI-compatible API. Privacy-first, cost-optimized, Arabic-tuned.
+"""
+import logging
+from datetime import datetime, timezone
+from typing import Optional
+
+from pydantic import BaseModel, Field
+
+logger = logging.getLogger(__name__)
+
+
+class LocalProvider(BaseModel):
+    name: str
+    base_url: str  # e.g., "http://localhost:11434/v1" for Ollama
+    model: str  # e.g., "qwen2.5:7b", "llama3.1:8b"
+    is_healthy: bool = False
+    last_check: Optional[datetime] = None
+    avg_latency_ms: float = 0.0
+    total_calls: int = 0
+    total_failures: int = 0
+
+
+# Default local providers to check
+DEFAULT_PROVIDERS = [
+    LocalProvider(
+        name="ollama",
+        base_url="http://localhost:11434/v1",
+        model="qwen2.5:7b",
+    ),
+    LocalProvider(
+        name="lm-studio",
+        base_url="http://localhost:1234/v1",
+        model="local-model",
+    ),
+    LocalProvider(
+        name="atomic-chat",
+        base_url="http://localhost:8080/v1",
+        model="default",
+    ),
+]
+
+# Tasks suitable for local inference
+LOCAL_SUITABLE_TASKS = {
+    "arabic_summarization": "تلخيص نصوص عربية",
+    "text_classification": "تصنيف نصوص",
+    "entity_extraction": "استخراج كيانات",
+    "internal_drafting": "صياغة مسودات داخلية",
+    "sentiment_analysis": "تحليل المشاعر",
+    "translation": "ترجمة نصوص",
+    "data_cleaning": "تنظيف بيانات",
+    "code_review_simple": "مراجعة كود بسيطة",
+}
+
+# Tasks that should NEVER use local inference
+CLOUD_ONLY_TASKS = {
+    "proposal_generation",
+    "complex_reasoning",
+    "long_document_analysis",
+    "customer_facing_messages",
+}
+
+
+class LocalInferenceResult(BaseModel):
+    provider: str
+    model: str
+    response: str
+    latency_ms: int
+    tokens_used: int = 0
+    cost_usd: float = 0.0  # Local = free
+    success: bool = True
+    error: Optional[str] = None
+
+
+class LocalInferenceAdapter:
+    """
+    Adapter for local/private LLM inference.
+    Tries providers in order, falls back gracefully to cloud.
+    """
+
+    def __init__(self):
+        self._providers = list(DEFAULT_PROVIDERS)
+        self._primary: Optional[LocalProvider] = None
+
+    async def health_check(self, provider: LocalProvider = None) -> bool:
+        """Check if a local provider is available."""
+        targets = [provider] if provider else self._providers
+        for p in targets:
+            try:
+                import httpx
+                async with httpx.AsyncClient(timeout=5.0) as client:
+                    resp = await client.get(f"{p.base_url}/models")
+                    if resp.status_code == 200:
+                        p.is_healthy = True
+                        p.last_check = datetime.now(timezone.utc)
+                        if not self._primary:
+                            self._primary = p
+                        logger.info(f"Local provider {p.name} is healthy at {p.base_url}")
+                        return True
+            except Exception:
+                p.is_healthy = False
+                p.last_check = datetime.now(timezone.utc)
+                continue
+        return False
+
+    async def health_check_all(self) -> dict[str, bool]:
+        """Check all configured local providers."""
+        results = {}
+        for p in self._providers:
+            results[p.name] = await self.health_check(p)
+        return results
+
+    def is_suitable_for_local(self, task_type: str) -> bool:
+        """Check if a task should use local inference."""
+        if task_type in CLOUD_ONLY_TASKS:
+            return False
+        return task_type in LOCAL_SUITABLE_TASKS
+
+    async def complete(
+        self,
+        prompt: str,
+        system_prompt: str = "",
+        task_type: str = "general",
+        max_tokens: int = 1024,
+        temperature: float = 0.7,
+    ) -> LocalInferenceResult:
+        """Run inference on local provider. Falls back gracefully."""
+        if not self._primary or not self._primary.is_healthy:
+            await self.health_check()
+
+        if not self._primary:
+            return LocalInferenceResult(
+                provider="none",
+                model="none",
+                response="",
+                latency_ms=0,
+                success=False,
+                error="لا يوجد مزود محلي متاح — استخدم السحابة",
+            )
+
+        start = datetime.now(timezone.utc)
+        provider = self._primary
+
+        try:
+            import httpx
+            messages = []
+            if system_prompt:
+                messages.append({"role": "system", "content": system_prompt})
+            messages.append({"role": "user", "content": prompt})
+
+            async with httpx.AsyncClient(timeout=60.0) as client:
+                resp = await client.post(
+                    f"{provider.base_url}/chat/completions",
+                    json={
+                        "model": provider.model,
+                        "messages": messages,
+                        "max_tokens": max_tokens,
+                        "temperature": temperature,
+                    },
+                )
+                resp.raise_for_status()
+                data = resp.json()
+
+            latency = int((datetime.now(timezone.utc) - start).total_seconds() * 1000)
+            provider.total_calls += 1
+            provider.avg_latency_ms = (
+                (provider.avg_latency_ms * (provider.total_calls - 1) + latency)
+                / provider.total_calls
+            )
+
+            content = data.get("choices", [{}])[0].get("message", {}).get("content", "")
+            tokens = data.get("usage", {}).get("total_tokens", 0)
+
+            return LocalInferenceResult(
+                provider=provider.name,
+                model=provider.model,
+                response=content,
+                latency_ms=latency,
+                tokens_used=tokens,
+                cost_usd=0.0,
+            )
+
+        except Exception as e:
+            provider.total_failures += 1
+            provider.is_healthy = False
+            latency = int((datetime.now(timezone.utc) - start).total_seconds() * 1000)
+            logger.warning(f"Local inference failed on {provider.name}: {e}")
+            return LocalInferenceResult(
+                provider=provider.name,
+                model=provider.model,
+                response="",
+                latency_ms=latency,
+                success=False,
+                error=str(e),
+            )
+
+    def add_provider(self, name: str, base_url: str, model: str) -> None:
+        """Register a new local provider."""
+        self._providers.append(LocalProvider(
+            name=name, base_url=base_url, model=model,
+        ))
+
+    def get_providers(self) -> list[dict]:
+        """List all configured providers with health status."""
+        return [
+            {
+                "name": p.name,
+                "base_url": p.base_url,
+                "model": p.model,
+                "healthy": p.is_healthy,
+                "last_check": p.last_check.isoformat() if p.last_check else None,
+                "avg_latency_ms": round(p.avg_latency_ms, 1),
+                "total_calls": p.total_calls,
+                "failure_rate": round(
+                    p.total_failures / p.total_calls * 100, 1
+                ) if p.total_calls > 0 else 0,
+                "is_primary": p == self._primary,
+            }
+            for p in self._providers
+        ]
+
+    def get_suitable_tasks(self) -> dict[str, str]:
+        """List tasks suitable for local inference."""
+        return dict(LOCAL_SUITABLE_TASKS)
+
+
+local_inference = LocalInferenceAdapter()
diff --git a/salesflow-saas/memory/runbooks/operations-schedule.md b/salesflow-saas/memory/runbooks/operations-schedule.md
new file mode 100644
index 00000000..0f3edd06
--- /dev/null
+++ b/salesflow-saas/memory/runbooks/operations-schedule.md
@@ -0,0 +1,115 @@
+# Operations Schedule — Dealix AI Revenue OS
+
+**Date**: 2026-04-11 | **Status**: active
+
+## Daily Operations (يومي)
+
+| الوقت | المهمة | المسؤول |
+|-------|--------|---------|
+| 08:00 | فحص صحة جميع الخدمات (Docker, DB, Redis, Celery) | ops |
+| 08:15 | مراجعة أخطاء Sentry الجديدة | ops |
+| 08:30 | فحص صحة مزودي الاستدلال المحلي | ops |
+| 09:00 | مراجعة تقرير المبيعات اليومي التلقائي | founder |
+| 12:00 | فحص Celery Beat tasks (sequences, follow-ups) | ops |
+| 16:00 | مراجعة tool verification logs — أي contradictions؟ | ops |
+| 17:00 | فحص memory sync وwiki health | knowledge |
+
+### أوامر الفحص اليومي:
+```bash
+# Health check
+curl -f https://api.dealix.sa/api/v1/health
+
+# Celery workers
+docker compose exec celery-worker celery -A app.workers inspect active
+
+# Sentry errors (last 24h)
+# Check https://sentry.io/organizations/dealix/
+
+# Tool verification contradictions
+curl https://api.dealix.sa/api/v1/hermes/health
+```
+
+## Weekly Operations (أسبوعي — كل أحد)
+
+| المهمة | المسؤول |
+|--------|---------|
+| تشغيل فحص Shannon الأمني على staging | security |
+| مراجعة مزودي LLM: تكلفة + أداء + استقرار | ops |
+| مقارنة local vs cloud: أي المهام أنسب محلياً؟ | ops |
+| مراجعة الـ runs الفاشلة ومعرفة السبب الجذري | ops |
+| مراجعة الإجراءات المتناقضة (contradicted actions) | security |
+| تنظيف الذاكرة: حذف duplicates + archive stale | knowledge |
+| مراجعة التكلفة الأسبوعية (هدف: < $50) | founder |
+| مراجعة drift الأوامر والمهارات | ops |
+
+### أوامر الفحص الأسبوعي:
+```bash
+# Shannon security scan
+curl -X POST https://api.dealix.sa/api/v1/hermes/security/scan \
+  -H "Content-Type: application/json" \
+  -d '{"environment": "staging", "base_url": "https://staging.dealix.sa"}'
+
+# Cost report
+curl https://api.dealix.sa/api/v1/hermes/cost?period=weekly
+
+# Self-improvement cycle
+curl -X POST https://api.dealix.sa/api/v1/hermes/improvements/cycle
+
+# Executive summary
+curl https://api.dealix.sa/api/v1/hermes/executive-summary?period=weekly
+```
+
+## Monthly Operations (شهري — أول أحد من كل شهر)
+
+| المهمة | المسؤول |
+|--------|---------|
+| مراجعة انحراف المعمارية (architecture drift) | ops |
+| مراجعة عملية الإطلاق والتحسين | ops |
+| تدريب rollback drill (استعادة من النسخة الاحتياطية) | ops |
+| تدريب backup/restore drill | ops |
+| إعادة benchmark لمزودي LLM | ops |
+| مراجعة انحراف نظام التصميم | delivery |
+| مراجعة وإعادة هيكلة سير العمل | founder |
+| تحديث ICP وstrategy بناءً على بيانات الشهر | founder |
+| مراجعة PDPL compliance checklist | security |
+| تقرير أداء شهري للمستثمرين/المؤسسين | founder |
+
+### أوامر الفحص الشهري:
+```bash
+# Full health report
+curl https://api.dealix.sa/api/v1/hermes/health
+
+# Knowledge brain lint
+# Run via Hermes: identify stale/orphan/duplicate wiki pages
+
+# Database backup test
+pg_dump dealix > /tmp/test_restore.sql
+psql -d dealix_test < /tmp/test_restore.sql
+rm /tmp/test_restore.sql
+
+# Provider benchmark rerun
+# Compare Groq vs OpenAI vs local on 50 test queries
+```
+
+## Emergency Procedures
+
+### Production Down
+1. Check Docker: `docker compose ps`
+2. Check logs: `docker compose logs -f backend --since 5m`
+3. Restart if needed: `docker compose restart backend`
+4. If persistent: rollback to last known good commit
+5. Notify team in communication channel
+
+### Data Breach Suspicion
+1. Immediately notify security profile
+2. Check audit logs for unauthorized access
+3. Check PDPL consent logs for anomalies
+4. Run Shannon emergency scan on affected area
+5. Prepare SDAIA notification if confirmed (within 72 hours)
+
+### Cost Spike
+1. Check observability: `GET /hermes/cost?period=hourly`
+2. Identify expensive workflow
+3. Pause autopilot if needed
+4. Switch to local inference for non-critical tasks
+5. Review and optimize the expensive workflow