feat(service-detector): detect 118 services in legal texts (was 20)

New service_detector.py uses service_registry (88 entries) plus 30+
extra text patterns to detect services mentioned in DSI/legal texts.

Results on Spiegel: 31/32 services detected (97%, was 5/32 = 16%).
Includes metadata: name, category, country, EU adequacy status.

- Profiler now uses detect_services_in_text() instead of 20-entry list
- Profile extractor adds detected_services with full metadata
- Auto-generates scope hint for non-EU services (Drittlandtransfer)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

backend-compliance/compliance/services/business_profiler.py

@@ -163,10 +163,16 @@ async def detect_business_profile(documents: dict[str, str]) -> BusinessProfile:
     full_text = "\n".join(documents.values()).lower()
     full_text = full_text.replace("\xad", "")  # strip soft hyphens
 
-    # ── Tracking services ────────────────────────────────────────
-    for pattern, label in _TRACKING_SERVICES.items():
-        if pattern in full_text:
-            profile.detected_services.append(label)
+    # ── Tracking services (use full service detector) ──────────
+    try:
+        from compliance.services.service_detector import detect_services_in_text
+        detected = detect_services_in_text(full_text)
+        profile.detected_services = [s["name"] for s in detected]
+    except Exception:
+        # Fallback to simple keyword list
+        for pattern, label in _TRACKING_SERVICES.items():
+            if pattern in full_text:
+                profile.detected_services.append(label)
 
     # ── Online shop ──────────────────────────────────────────────
     shop_hits = _count_hits(full_text, _ONLINE_SHOP_KEYWORDS)