feat(compliance-check): profile extraction + scenario classification

- New profile_extractor.py: extracts Company Profile fields (name,
  legal form, address, DPO, USt-IdNr) and Compliance Scope hints
  (Art. 9 data, third country, profiling) from document texts
- Scenario per document: regenerate (<30%), fix (30-95%), import (>95%)
- Widerruf for B2B: no longer skipped, instead all checks flagged as
  INFO with "not needed for B2B" hint
- Move _build_profile_html to report builder module
- DocCheckResult gets scenario field

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

backend-compliance/compliance/api/agent_compliance_check_routes.py

@@ -268,15 +268,29 @@ async def _run_compliance_check(check_id: str, req: ComplianceCheckRequest):
                         l2p = sum(1 for c in l2 if c.passed)
                         r.correctness_pct = round(l2p / len(l2) * 100) if l2 else 0
 
-        # Step 4: Build report
+        # Step 4: Extract profile hints from documents
+        _update(check_id, "Profil wird aus Dokumenten extrahiert...")
+        from compliance.services.profile_extractor import extract_profile_from_documents
+        extracted_profile = extract_profile_from_documents(doc_texts, profile_dict)
+
+        # Step 4b: Determine scenario per document
+        for r in results:
+            if r.error:
+                r.scenario = "skip"
+            elif r.completeness_pct < 30:
+                r.scenario = "regenerate"
+            elif r.completeness_pct < 95:
+                r.scenario = "fix"
+            else:
+                r.scenario = "import"
+
+        # Step 5: Build report
         _update(check_id, "Report wird erstellt...")
         report_html = build_html_report(results, None)
-
-        # Prepend profile summary to report
         profile_html = _build_profile_html(profile)
         full_html = profile_html + report_html
 
-        # Step 5: Send email
+        # Step 6: Send email
         doc_count = len([r for r in results if not r.error])
         email_result = send_email(
             recipient=req.recipient,
@@ -284,10 +298,11 @@ async def _run_compliance_check(check_id: str, req: ComplianceCheckRequest):
             body_html=full_html,
         )
 
-        # Step 6: Store result
+        # Step 7: Store result
         response = {
             "results": [_result_to_dict(r) for r in results],
             "business_profile": profile_dict,
+            "extracted_profile": extracted_profile,
             "banner_result": {
                 "detected": banner_result.get("banner_detected", False) if banner_result else False,
                 "provider": banner_result.get("banner_provider", "") if banner_result else "",
@@ -406,16 +421,9 @@ async def _check_single(
 
 
 def _get_skip_types(profile) -> dict[str, str]:
-    """Return doc_types to skip entirely based on business profile.
+    """Doc_types to skip entirely. Currently empty — we check everything
-
+    and flag irrelevant items as INFO instead of skipping."""
-    Returns dict mapping doc_type -> skip reason.
+    return {}
-    """
-    skip: dict[str, str] = {}
-    if profile.business_type in ("b2b", "b2g"):
-        skip["widerruf"] = "Uebersprungen: Widerrufsbelehrung nur fuer B2C relevant"
-    if profile.business_type in ("b2b", "b2g") and not profile.has_online_shop:
-        skip["nutzungsbedingungen"] = "Uebersprungen: Nutzungsbedingungen bei B2B ohne Shop selten relevant"
-    return skip
 
 
 def _apply_profile_filter(result, profile, doc_type: str):
@@ -434,10 +442,16 @@ def _apply_profile_filter(result, profile, doc_type: str):
                 check.skipped = True
                 check.hint = "Nicht relevant (kein B2C Online-Shop)"
 
-        # Widerruf only relevant for B2C
+        # Widerruf: Flag entire document as unnecessary for B2B
         if doc_type == "widerruf" and profile.business_type not in ("b2c", "unknown"):
-            if check.severity == "INFO":
+            check.severity = "INFO"
-                check.skipped = True
+            if not check.passed:
+                check.hint = (
+                    "Als B2B-Unternehmen benoetigen Sie keine Widerrufsbelehrung "
+                    "(§355 BGB gilt nur fuer Verbrauchervertraege). "
+                    "Empfehlung: Entfernen Sie die Widerrufsbelehrung von "
+                    "Ihrer Website, da sie Verwirrung stiften kann."
+                )
 
         # Regulated profession: check for Kammer info
         if "kammer" in cid or "berufsordnung" in check.label.lower():
@@ -479,41 +493,13 @@ def _result_to_dict(r) -> dict:
         "correctness_pct": r.correctness_pct,
         "checks": [{f: getattr(c, f) for f in fields} for c in r.checks],
         "findings_count": r.findings_count, "error": r.error,
+        "scenario": getattr(r, "scenario", ""),
     }
 
 
 def _build_profile_html(profile) -> str:
-    """Build a small HTML block summarizing the detected business profile."""
+    from .agent_doc_check_report import build_profile_html
-    service_tags = ", ".join(profile.detected_services[:10]) or "keine erkannt"
+    return build_profile_html(profile)
-    flags = []
-    if profile.has_online_shop:
-        flags.append("Online-Shop")
-    if profile.has_editorial_content:
-        flags.append("Redaktionelle Inhalte")
-    if profile.is_regulated_profession:
-        flags.append(f"Regulierter Beruf ({profile.regulated_profession_type})")
-    if profile.needs_odr:
-        flags.append("ODR-pflichtig")
-    flags_str = ", ".join(flags) or "keine"
-
-    return (
-        '<div style="font-family:-apple-system,BlinkMacSystemFont,sans-serif;'
-        'max-width:700px;margin:0 auto 16px;padding:12px 16px;'
-        'background:#f0f9ff;border:1px solid #bae6fd;border-radius:8px">'
-        '<h3 style="margin:0 0 8px;font-size:14px;color:#0369a1">'
-        'Erkanntes Geschaeftsmodell</h3>'
-        '<table style="font-size:13px;color:#374151">'
-        f'<tr><td style="padding:2px 12px 2px 0;color:#6b7280">Typ:</td>'
-        f'<td><strong>{profile.business_type.upper()}</strong>'
-        f' ({profile.industry})</td></tr>'
-        f'<tr><td style="padding:2px 12px 2px 0;color:#6b7280">Merkmale:</td>'
-        f'<td>{flags_str}</td></tr>'
-        f'<tr><td style="padding:2px 12px 2px 0;color:#6b7280">Dienste:</td>'
-        f'<td>{service_tags}</td></tr>'
-        f'<tr><td style="padding:2px 12px 2px 0;color:#6b7280">Konfidenz:</td>'
-        f'<td>{int(profile.confidence * 100)}%</td></tr>'
-        '</table></div>'
-    )
 
 
 # Cross-check extracted to compliance.services.banner_cookie_cross_check

backend-compliance/compliance/api/agent_doc_check_report.py

@@ -173,3 +173,37 @@ def _render_cookie_banner(html: list[str], cookie_result: dict) -> None:
     else:
         html.append('<br><span style="color:#22c55e">Keine Verstoesse erkannt.</span>')
     html.append('</div>')
+
+
+def build_profile_html(profile) -> str:
+    """Build a small HTML block summarizing the detected business profile."""
+    service_tags = ", ".join(profile.detected_services[:10]) or "keine erkannt"
+    flags = []
+    if profile.has_online_shop:
+        flags.append("Online-Shop")
+    if profile.has_editorial_content:
+        flags.append("Redaktionelle Inhalte")
+    if profile.is_regulated_profession:
+        flags.append(f"Regulierter Beruf ({profile.regulated_profession_type})")
+    if profile.needs_odr:
+        flags.append("ODR-pflichtig")
+    flags_str = ", ".join(flags) or "keine"
+
+    return (
+        '<div style="font-family:-apple-system,BlinkMacSystemFont,sans-serif;'
+        'max-width:700px;margin:0 auto 16px;padding:12px 16px;'
+        'background:#f0f9ff;border:1px solid #bae6fd;border-radius:8px">'
+        '<h3 style="margin:0 0 8px;font-size:14px;color:#0369a1">'
+        'Erkanntes Geschaeftsmodell</h3>'
+        '<table style="font-size:13px;color:#374151">'
+        f'<tr><td style="padding:2px 12px 2px 0;color:#6b7280">Typ:</td>'
+        f'<td><strong>{profile.business_type.upper()}</strong>'
+        f' ({profile.industry})</td></tr>'
+        f'<tr><td style="padding:2px 12px 2px 0;color:#6b7280">Merkmale:</td>'
+        f'<td>{flags_str}</td></tr>'
+        f'<tr><td style="padding:2px 12px 2px 0;color:#6b7280">Dienste:</td>'
+        f'<td>{service_tags}</td></tr>'
+        f'<tr><td style="padding:2px 12px 2px 0;color:#6b7280">Konfidenz:</td>'
+        f'<td>{int(profile.confidence * 100)}%</td></tr>'
+        '</table></div>'
+    )

backend-compliance/compliance/api/agent_doc_check_routes.py

@@ -65,6 +65,7 @@ class DocCheckResult(BaseModel):
     checks: list[CheckItem] = []
     findings_count: int = 0
     error: str = ""
+    scenario: str = ""  # regenerate | fix | import | skip
 
 
 class DocCheckResponse(BaseModel):

backend-compliance/compliance/services/profile_extractor.py

@@ -0,0 +1,248 @@
+"""
+Profile Extractor — pre-fill Company Profile + Compliance Scope from documents.
+
+When a customer uploads their existing legal documents, we extract
+what we can and pre-fill the profile/scope wizard so they only need
+to confirm and fill gaps.
+
+Returns a dict that maps to CompanyProfile and ScopeProfilingAnswer fields.
+"""
+
+import logging
+import re
+
+logger = logging.getLogger(__name__)
+
+
+def extract_profile_from_documents(
+    doc_texts: dict[str, str],
+    business_profile: dict | None = None,
+) -> dict:
+    """Extract Company Profile fields from document texts.
+
+    Args:
+        doc_texts: dict mapping doc_type -> text
+        business_profile: optional detected business profile from profiler
+
+    Returns dict with pre-filled fields for Company Profile and Scope.
+    """
+    result: dict = {
+        "company_profile": {},
+        "compliance_scope_hints": [],
+        "extracted_from": [],
+    }
+
+    all_text = "\n".join(doc_texts.values()).lower()
+    all_text_original = "\n".join(doc_texts.values())
+
+    # ── Company name + legal form ────────────────────────────────
+    impressum = doc_texts.get("impressum", "")
+    if impressum:
+        _extract_company_info(impressum, result)
+        result["extracted_from"].append("impressum")
+
+    # Fallback: try DSI
+    if not result["company_profile"].get("companyName") and "dse" in doc_texts:
+        _extract_company_info(doc_texts["dse"], result)
+        result["extracted_from"].append("dse")
+
+    # ── DPO contact ──────────────────────────────────────────────
+    _extract_dpo(all_text_original, result)
+
+    # ── Business model from profiler ─────────────────────────────
+    if business_profile:
+        bp = business_profile
+        if bp.get("business_type") and bp["business_type"] != "unknown":
+            result["company_profile"]["businessModel"] = bp["business_type"]
+        if bp.get("industry") and bp["industry"] != "unknown":
+            result["company_profile"]["industry"] = [bp["industry"]]
+        if bp.get("has_online_shop"):
+            result["company_profile"]["offerings"] = ["online_shop"]
+        if bp.get("is_regulated_profession"):
+            result["company_profile"]["regulatedProfession"] = True
+            result["company_profile"]["regulatedProfessionType"] = bp.get(
+                "regulated_profession_type", ""
+            )
+
+    # ── Scope hints from document content ────────────────────────
+    _extract_scope_hints(all_text, result)
+
+    # ── Tracking services → data processing activities ───────────
+    if business_profile and business_profile.get("detected_services"):
+        result["detected_services"] = business_profile["detected_services"]
+
+    logger.info(
+        "Extracted %d profile fields, %d scope hints from %d documents",
+        len(result["company_profile"]),
+        len(result["compliance_scope_hints"]),
+        len(doc_texts),
+    )
+    return result
+
+
+def _extract_company_info(text: str, result: dict) -> None:
+    """Extract company name, legal form, address from text."""
+    cp = result["company_profile"]
+
+    # GmbH / AG / UG / e.K. etc.
+    legal_forms = {
+        r"(\S+(?:\s+\S+){0,4})\s+gmbh\b": ("GmbH", "gmbh"),
+        r"(\S+(?:\s+\S+){0,4})\s+ag\b": ("AG", "ag"),
+        r"(\S+(?:\s+\S+){0,4})\s+ug\b": ("UG", "ug"),
+        r"(\S+(?:\s+\S+){0,4})\s+e\.?\s*k\.?\b": ("e.K.", "ek"),
+        r"(\S+(?:\s+\S+){0,4})\s+gbr\b": ("GbR", "gbr"),
+        r"(\S+(?:\s+\S+){0,4})\s+ohg\b": ("OHG", "ohg"),
+        r"(\S+(?:\s+\S+){0,4})\s+gmbh\s*&\s*co\.?\s*kg": ("GmbH & Co. KG", "gmbh_co_kg"),
+    }
+    text_lower = text.lower()
+    for pattern, (form_label, form_id) in legal_forms.items():
+        m = re.search(pattern, text_lower)
+        if m:
+            raw_name = m.group(0).strip()
+            # Clean up: take from uppercase start
+            for i, ch in enumerate(text[m.start():m.end()]):
+                if ch.isupper():
+                    cp["companyName"] = text[m.start() + i:m.end()].strip()
+                    break
+            cp["legalForm"] = form_id
+            break
+
+    # PLZ + Ort
+    plz_match = re.search(
+        r"[d\-]?\s*(\d{5})\s+([A-Z\u00c0-\u017e][a-z\u00e0-\u00ff]+(?:\s+[a-z]+)*)",
+        text,
+    )
+    if plz_match:
+        cp["headquartersZip"] = plz_match.group(1)
+        cp["headquartersCity"] = plz_match.group(2).strip()
+        cp["headquartersCountry"] = "DE"
+
+    # Strasse
+    street_match = re.search(
+        r"([A-Z\u00c0-\u017e][a-z\u00e0-\u00ff]+(?:str(?:\.|asse)?|weg|allee|platz|ring|gasse)"
+        r"\s*\.?\s*\d+[a-z]?)",
+        text,
+    )
+    if street_match:
+        cp["headquartersStreet"] = street_match.group(1).strip()
+
+    # USt-IdNr
+    ust_match = re.search(r"DE\s*\d{9}", text)
+    if ust_match:
+        cp["ustIdNr"] = ust_match.group(0).replace(" ", "")
+
+    # HRB/HRA
+    hrb_match = re.search(r"HRB?\s*\d+", text, re.IGNORECASE)
+    if hrb_match:
+        cp["registrationNumber"] = hrb_match.group(0)
+
+    # Registergericht
+    reg_match = re.search(
+        r"(?:amtsgericht|registergericht|ag)\s+([A-Z\u00c0-\u017e][a-z\u00e0-\u00ff]+)",
+        text, re.IGNORECASE,
+    )
+    if reg_match:
+        cp["registrationCourt"] = reg_match.group(0)
+
+
+def _extract_dpo(text: str, result: dict) -> None:
+    """Extract DPO name and email."""
+    cp = result["company_profile"]
+
+    # DPO email
+    dpo_section = re.search(
+        r"datenschutzbeauftragte[rn]?\s*[\s\S]{0,300}",
+        text, re.IGNORECASE,
+    )
+    if dpo_section:
+        section = dpo_section.group(0)
+        email_match = re.search(r"[\w.+-]+@[\w-]+\.[\w.-]+", section)
+        if email_match:
+            cp["dpoEmail"] = email_match.group(0)
+
+        # DPO name (after "Datenschutzbeauftragter:" or similar)
+        name_match = re.search(
+            r"(?:datenschutzbeauftragte[rn]?\s*:?\s*)"
+            r"([A-Z\u00c0-\u017e][a-z\u00e0-\u00ff]+\s+"
+            r"[A-Z\u00c0-\u017e][a-z\u00e0-\u00ff]+)",
+            text,
+        )
+        if name_match:
+            cp["dpoName"] = name_match.group(1)
+
+
+def _extract_scope_hints(text: str, result: dict) -> None:
+    """Extract scope-relevant signals from document text."""
+    hints = result["compliance_scope_hints"]
+
+    # Sensitive data categories (Art. 9)
+    if any(kw in text for kw in [
+        "gesundheitsdaten", "biometrisch", "genetisch",
+        "religionszugehoerigkeit", "gewerkschaft", "sexualleben",
+        "politische meinung", "ethnische herkunft",
+    ]):
+        hints.append({
+            "field": "processesSpecialCategories",
+            "value": True,
+            "source": "Erwaehnung besonderer Datenkategorien (Art. 9 DSGVO) im Text",
+        })
+
+    # Third country transfer
+    if any(kw in text for kw in ["usa", "drittland", "drittstaaten", "third country"]):
+        hints.append({
+            "field": "hasThirdCountryTransfer",
+            "value": True,
+            "source": "Drittlandtransfer erwaehnt",
+        })
+
+    # Large-scale processing
+    if any(kw in text for kw in [
+        "umfangreiche verarbeitung", "grosse anzahl",
+        "large scale", "massenverarbeitung",
+    ]):
+        hints.append({
+            "field": "largeScaleProcessing",
+            "value": True,
+            "source": "Hinweis auf umfangreiche Verarbeitung",
+        })
+
+    # Automated decision-making
+    if any(kw in text for kw in [
+        "automatisierte entscheidung", "profiling", "scoring",
+        "automated decision", "art. 22",
+    ]):
+        hints.append({
+            "field": "automatedDecisionMaking",
+            "value": True,
+            "source": "Automatisierte Entscheidungsfindung erwaehnt",
+        })
+
+    # Auftragsverarbeitung (processor role)
+    if any(kw in text for kw in [
+        "auftragsverarbeitung", "auftragsverarbeiter",
+        "im auftrag", "weisungsgebunden",
+    ]):
+        hints.append({
+            "field": "isDataProcessor",
+            "value": True,
+            "source": "Auftragsverarbeitung erwaehnt",
+        })
+
+    # Newsletter / Marketing
+    if any(kw in text for kw in ["newsletter", "marketing", "werbung"]):
+        hints.append({
+            "field": "hasNewsletter",
+            "value": True,
+            "source": "Newsletter/Marketing erwaehnt",
+        })
+
+    # Employee data
+    if any(kw in text for kw in [
+        "mitarbeiterdaten", "beschaeftigtendaten", "personalakte",
+        "bewerberdaten", "arbeitnehmer",
+    ]):
+        hints.append({
+            "field": "processesEmployeeData",
+            "value": True,
+            "source": "Beschaeftigtendaten-Verarbeitung erwaehnt",
+        })