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/change_detection/extractors.py

@@ -0,0 +1,25 @@
+"""Lightweight, best-effort regex extractors for specific signal types the
+severity model treats specially (pricing, leadership). These are heuristics,
+not NLP - they exist to catch the common "$X/month" and "named a new CEO"
+phrasings, not to parse arbitrary text reliably. Phase 7's LLM extraction
+task is the higher-fidelity version of this; these run cheaply and
+deterministically as part of scoring, without a model call.
+"""
+
+from __future__ import annotations
+
+import re
+
+_PRICE_RE = re.compile(r"\$\s?\d[\d,]*(?:\.\d{2})?\s*(?:/\s*(?:month|mo|year|yr))?")
+_LEADERSHIP_TITLE_RE = re.compile(
+    r"(?i)\b(Chief Executive Officer|CEO|Chief Financial Officer|CFO|Chief Technology Officer|"
+    r"CTO|President|Chairman|Chairwoman|Chairperson)\b"
+)
+
+
+def extract_prices(text: str) -> set[str]:
+    return set(_PRICE_RE.findall(text or ""))
+
+
+def mentions_leadership_title(text: str) -> bool:
+    return bool(_LEADERSHIP_TITLE_RE.search(text or ""))