feat: Gemini AI assessment, Kit Digital detection, contact extraction

Kit Digital detection (enricher.py):
- Scans img src/alt/srcset for digitalizadores, kit-digital, fondos-europeos etc
- Scans page text for Kit Digital, Agente Digitalizador, Next Generation EU, PRTR
- Scans links for acelerapyme.es, red.es, kit-digital refs
- +20 score bonus for Kit Digital confirmed sites (proven IT buyers)

Contact extraction (enricher.py):
- Pulls mailto/tel/wa.me links from HTML
- Extracts email addresses via regex, phone numbers (ES format)
- Detects social media links (FB, IG, LinkedIn, Twitter, TikTok)
- Stored as JSON in contact_info column

Gemini via Replicate (replicate_ai.py):
- Assesses lead quality (HOT/WARM/COLD), Kit Digital confirmation
- Identifies best contact channel + actual value (email/phone/WA)
- Writes Spanish cold-call/email pitch angle
- Lists services likely needed + outreach notes
- 3 concurrent requests, 90s timeout, JSON output parsing

DB: migration adds kit_digital, kit_digital_signals, contact_info,
    ai_assessment, ai_lead_quality, ai_pitch, ai_contact_channel/value,
    ai_queue table

UI: Kit Digital 🏅 badge, AI quality pill (clickable modal with full
    assessment), contact chips (email/phone/WA/social), AI Assess button,
    Kit Digital only filter, AI queue status in enrichment tab

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

app/main.py

@@ -6,6 +6,7 @@ from contextlib import asynccontextmanager
 
 import httpx
 import aiosqlite
+from typing import Optional
 from fastapi import FastAPI, Query
 from fastapi.responses import StreamingResponse, JSONResponse
 from fastapi.staticfiles import StaticFiles
@@ -17,6 +18,7 @@ from app.db import (
     DATA_DIR, PARQUET_PATH, SQLITE_PATH,
     init_db, get_stats, get_domains, get_enriched,
     queue_domains, get_queue_status, build_duckdb_index, index_status,
+    queue_ai, get_ai_queue_status, save_ai_assessment,
 )
 from app.enricher import start_worker, pause_worker, resume_worker, is_running
 from app.scorer import run_scoring
@@ -146,13 +148,53 @@ async def enriched(
     min_score: int = Query(0, ge=0, le=100),
     cms: str = Query(None),
     country: str = Query(None),
+    kit_digital: Optional[bool] = Query(None),
     page: int = Query(1, ge=1),
     limit: int = Query(100, ge=1, le=1000),
 ):
-    total, rows = await get_enriched(min_score=min_score, cms=cms, country=country, page=page, limit=limit)
+    total, rows = await get_enriched(
+        min_score=min_score, cms=cms, country=country,
+        kit_digital=kit_digital, page=page, limit=limit,
+    )
     return {"page": page, "limit": limit, "total": total, "results": rows}
 
 
+# ── AI assessment endpoints ───────────────────────────────────────────────────
+
+@app.post("/api/ai/assess/batch")
+async def ai_assess_batch(body: dict):
+    domains_list = body.get("domains", [])
+    if not domains_list:
+        return JSONResponse({"error": "no domains provided"}, status_code=400)
+    await queue_ai(domains_list)
+    return {"queued": len(domains_list)}
+
+
+@app.get("/api/ai/status")
+async def ai_status():
+    return await get_ai_queue_status()
+
+
+@app.post("/api/ai/assess/single")
+async def ai_assess_single(body: dict):
+    """Immediate (blocking) AI assessment of a single domain."""
+    domain = body.get("domain")
+    if not domain:
+        return JSONResponse({"error": "no domain"}, status_code=400)
+    from app.replicate_ai import assess_domain as gemini_assess
+    async with aiosqlite.connect(SQLITE_PATH) as db:
+        db.row_factory = aiosqlite.Row
+        async with db.execute(
+            "SELECT * FROM enriched_domains WHERE domain=?", (domain,)
+        ) as cur:
+            row = await cur.fetchone()
+    if not row:
+        return JSONResponse({"error": "domain not yet enriched"}, status_code=404)
+    assessment = await gemini_assess(dict(row))
+    await save_ai_assessment(domain, assessment)
+    return assessment
+
+
 @app.get("/api/export")
 async def export_csv(
     min_score: int = Query(0),