Initial commit: CI Agent competitive-intelligence monitoring app

FastAPI + Celery + Next.js + Postgres/Redis app with company monitoring,
source collection, LLM-based change analysis, enrichment, and account
security (Turnstile, escalating lockout, email verification).

apps/api/app/prompts/synthesis.py

@@ -0,0 +1,54 @@
+"""Task C: cross-source synthesis. Combines related signals from multiple
+documents/changes into a conclusion (e.g. "hiring battery engineers" +
+"filed a battery patent" + "announced a facility expansion" = "possible
+battery manufacturing initiative"), always with alternative explanations and
+an explicit accounting of what's still unknown.
+"""
+
+from __future__ import annotations
+
+from pydantic import BaseModel, Field
+
+from app.analysis.llm.base import LLMProvider
+from app.prompts.base import build_user_prompt
+
+SYSTEM_PROMPT = (
+    "You are a competitive intelligence analyst. Given a set of independently-extracted "
+    "signals about a company, identify conclusions that multiple signals point toward "
+    "together. Only draw a conclusion when the evidence actually supports it - state your "
+    "confidence honestly, list plausible alternative explanations, and note what evidence "
+    "would be needed to be more certain. Do not synthesize a conclusion from a single signal."
+)
+
+
+class SynthesizedConclusion(BaseModel):
+    conclusion: str
+    evidence_summary: list[str] = Field(default_factory=list)
+    source_count: int = Field(ge=0)
+    confidence: float = Field(ge=0.0, le=1.0)
+    alternative_explanations: list[str] = Field(default_factory=list)
+    missing_information: list[str] = Field(default_factory=list)
+
+
+class SynthesisResult(BaseModel):
+    conclusions: list[SynthesizedConclusion] = Field(default_factory=list)
+
+
+async def synthesize_signals(
+    llm: LLMProvider,
+    *,
+    company_name: str,
+    monitoring_focus: str | None,
+    signals: list[dict],
+) -> SynthesisResult:
+    evidence = {
+        "company_name": company_name,
+        "monitoring_focus": monitoring_focus,
+        "signals": signals,
+    }
+    user_prompt = build_user_prompt(
+        "Identify conclusions supported by two or more of these signals together. Do not "
+        "synthesize from a single signal alone.",
+        evidence,
+    )
+    return await llm.generate_structured(SYSTEM_PROMPT, user_prompt, SynthesisResult)