feat(agent): progress_pct + 6 BMW-Run Verbesserungen

Backend (agent_compliance_check_routes.py): - progress_pct (0-100%) im Job-State, ueber alle Phasen verteilt (Laden 0-30, Profil 35-40, Pruefen 40-80, Banner 80-92, Report 95-100) - Status-Texte vereinheitlicht ("Texte laden X/N", "Pruefen X/N") - Firmenname fuer Email-Subject jetzt aus URL abgeleitet (bmw.de -> "BMW", mercedes-benz.de -> "Mercedes-Benz") statt unzuverlaessigem extracted_profile.companyName (matchte oft juris.de) - E-Mail-Report enthaelt jetzt Banner+TCF-Vendor-Liste (build_provider_list_html) Backend (agent_doc_check_extras.py — neu): - build_scanned_urls_html: gepruefte URLs als Tabelle oben im Report (transparent fuer GF, welche Quellen wirklich gezogen wurden) - Cross-Domain-Hinweis bei >1 netloc (BMW: bmw.de / bmwgroup.com / bmwgroup.jobs — Auffindbarkeit nach Art. 12 DSGVO) - build_provider_list_html: Banner-Box + TCF-Vendor-Tabelle mit Spalten Name | Kategorie | Zweck | Drittland | Rechtsgrundlage Backend (business_profiler.py): - §34d-GewO Versicherungsvermittler-Hinweise zaehlen nicht mehr als "finance"-Industrie (BMW wurde dadurch falsch als B2B/finance erkannt) - Neue Industry "automotive" (Fahrzeug/KFZ/Konfigurator/Modellpalette) - B2B-Keywords: generische Begriffe wie "unternehmen", "beratung", "consulting" entfernt (matchten in jedem Konzerntext) - B2C-Fallback: bei Verbraucher-Signalen ("widerruf", "kunde", redaktioneller Inhalt) tendiert auf b2c statt b2b Frontend (ComplianceCheckTab.tsx): - Progress-Balken mit Width-% und XX%-Anzeige rechts - liest data.progress_pct aus Polling-Response Consent-Tester (dsi_discovery.py): - Cookie-Policy-Extraktion kritisch fixt: wait_for_function bis body.innerText > 500 chars (BMW SPA-Rendering brauchte mehr Zeit) - _extract_text_robust: 3-Strategien-Extraktion (Selektoren -> Body- Cleanup -> P/LI/TD-Tags) - _extract_text_from_iframes: liest OneTrust/Sourcepoint/Usercentrics Iframe-Inhalte (manche Cookie-Policies leben dort) Adressiert alle Findings aus dem BMW-Ground-Truth-Vergleich.
2026-05-16 17:53:14 +02:00
parent 4d1e0a7f8e
commit e61e9d9e2a
6 changed files with 515 additions and 53 deletions
@@ -73,6 +73,7 @@ export function ComplianceCheckTab() {
  const [useAgent, setUseAgent] = useState(false)
  const [loading, setLoading] = useState(false)
  const [progress, setProgress] = useState('')
+  const [progressPct, setProgressPct] = useState(0)
  const [results, setResults] = useState<any>(() => {
    if (typeof window === 'undefined') return null
    try { const s = localStorage.getItem(STORAGE_KEY_RESULTS); return s ? JSON.parse(s) : null } catch { return null }
@@ -109,15 +110,16 @@ export function ComplianceCheckTab() {
          if (!res.ok) continue
          const data = await res.json()
          if (data.progress) setProgress(data.progress)
+          if (typeof data.progress_pct === 'number') setProgressPct(data.progress_pct)
          if (data.status === 'completed' && data.result) {
-            setResults(data.result); setProgress(''); setLoading(false)
+            setResults(data.result); setProgress(''); setProgressPct(0); setLoading(false)
            localStorage.setItem(STORAGE_KEY_RESULTS, JSON.stringify(data.result))
            localStorage.removeItem(STORAGE_KEY_CHECK_ID); setActiveCheckId('')
            return
          }
          if (data.status === 'failed' || data.status === 'not_found') {
            if (data.status === 'failed') setError(data.error || 'Pruefung fehlgeschlagen')
-            setProgress(''); setLoading(false)
+            setProgress(''); setProgressPct(0); setLoading(false)
            localStorage.removeItem(STORAGE_KEY_CHECK_ID); setActiveCheckId('')
            return
          }
@@ -177,6 +179,7 @@ export function ComplianceCheckTab() {
    setError(null)
    setResults(null)
    setProgress('Compliance-Check wird gestartet...')
+    setProgressPct(0)

    try {
      const entries = DOCUMENT_TYPES
@@ -210,9 +213,11 @@ export function ComplianceCheckTab() {
        if (!pollRes.ok) { attempts++; continue }
        const pollData = await pollRes.json()
        if (pollData.progress) setProgress(pollData.progress)
+        if (typeof pollData.progress_pct === 'number') setProgressPct(pollData.progress_pct)
        if (pollData.status === 'completed' && pollData.result) {
          setResults(pollData.result)
          setProgress('')
+          setProgressPct(0)
          localStorage.setItem(STORAGE_KEY_RESULTS, JSON.stringify(pollData.result))
          localStorage.removeItem(STORAGE_KEY_CHECK_ID); setActiveCheckId('')

@@ -242,6 +247,7 @@ export function ComplianceCheckTab() {
    } catch (e) {
      setError(e instanceof Error ? e.message : 'Unbekannter Fehler')
      setProgress('')
+      setProgressPct(0)
    } finally {
      setLoading(false)
    }
@@ -334,12 +340,21 @@ export function ComplianceCheckTab() {

      {/* Progress */}
      {progress && (
-        <div className="bg-purple-50 border border-purple-200 rounded-lg p-3 text-sm text-purple-700 flex items-center gap-3">
+        <div className="bg-purple-50 border border-purple-200 rounded-lg p-3 text-sm text-purple-700 space-y-2">
+          <div className="flex items-center gap-3">
            <svg className="animate-spin w-4 h-4 text-purple-500 shrink-0" fill="none" viewBox="0 0 24 24">
              <circle className="opacity-25" cx="12" cy="12" r="10" stroke="currentColor" strokeWidth="4" />
              <path className="opacity-75" fill="currentColor" d="M4 12a8 8 0 018-8V0C5.373 0 0 5.373 0 12h4z" />
            </svg>
-          {progress}
+            <span className="flex-1">{progress}</span>
+            <span className="text-xs font-mono text-purple-600 tabular-nums">{progressPct}%</span>
+          </div>
+          <div className="h-1.5 bg-purple-100 rounded-full overflow-hidden">
+            <div
+              className="h-full bg-purple-500 rounded-full transition-all duration-500 ease-out"
+              style={{ width: `${Math.max(2, progressPct)}%` }}
+            />
+          </div>
        </div>
      )}

@@ -56,6 +56,7 @@ class ComplianceCheckStatusResponse(BaseModel):
    check_id: str
    status: str
    progress: str = ""
+    progress_pct: int = 0
    result: dict | None = None
    error: str = ""

@@ -124,6 +125,7 @@ async def start_compliance_check(req: ComplianceCheckRequest):
    _compliance_check_jobs[check_id] = {
        "status": "running",
        "progress": "Pruefung gestartet...",
+        "progress_pct": 0,
        "result": None,
        "error": "",
    }
@@ -141,6 +143,7 @@ async def get_compliance_check_status(check_id: str):
        check_id=check_id,
        status=job["status"],
        progress=job.get("progress", ""),
+        progress_pct=job.get("progress_pct", 0),
        result=job.get("result"),
        error=job.get("error", ""),
    )
@@ -155,16 +158,18 @@ async def _run_compliance_check(check_id: str, req: ComplianceCheckRequest):
        from .agent_doc_check_routes import CheckItem, DocCheckResult
        from .agent_doc_check_report import build_html_report

-        # Step 1: Resolve texts (fetch from URL if needed)
-        _update(check_id, "Texte werden geladen...")
+        # Step 1: Resolve texts (fetch from URL if needed) — 0-30%
+        _update(check_id, "Texte werden geladen...", 1)
        doc_texts: dict[str, str] = {}
        doc_entries: list[dict] = []

        # Cache fetched URLs to detect duplicates
        url_text_cache: dict[str, str] = {}

+        n_docs = max(1, len(req.documents))
        for i, doc in enumerate(req.documents):
-            _update(check_id, f"Dokument {i+1}/{len(req.documents)}: {doc.doc_type}...")
+            pct = int(1 + (i / n_docs) * 29)
+            _update(check_id, f"Texte laden {i+1}/{n_docs}: {doc.doc_type}...", pct)
            text = doc.text
            if not text and doc.url:
                url_key = doc.url.strip().rstrip("/").lower()
@@ -192,8 +197,8 @@ async def _run_compliance_check(check_id: str, req: ComplianceCheckRequest):
        split_shared_texts(doc_entries, url_text_cache)
        auto_fill_from_dsi(doc_entries)

-        # Step 1c: Cross-document search — find doc_types in wrong documents
-        _update(check_id, "Dokumente werden uebergreifend durchsucht...")
+        # Step 1c: Cross-document search — find doc_types in wrong documents (30-35%)
+        _update(check_id, "Dokumente werden uebergreifend durchsucht...", 32)
        placement_findings = cross_search_documents(doc_entries)

        # Refresh doc_texts after all splitting/searching
@@ -201,8 +206,8 @@ async def _run_compliance_check(check_id: str, req: ComplianceCheckRequest):
            if entry.get("text"):
                doc_texts[entry["doc_type"]] = entry["text"]

-        # Step 2: Detect business profile
-        _update(check_id, "Geschaeftsmodell wird erkannt...")
+        # Step 2: Detect business profile (35-40%)
+        _update(check_id, "Geschaeftsmodell wird erkannt...", 37)
        profile = await detect_business_profile(doc_texts)
        profile_dict = asdict(profile)

@@ -216,6 +221,8 @@ async def _run_compliance_check(check_id: str, req: ComplianceCheckRequest):
        # Filter out doc_types that don't apply to this business profile
        skip_types = _get_skip_types(profile)

+        # Document checks: 40-80%
+        n_entries = max(1, len(doc_entries))
        for i, entry in enumerate(doc_entries):
            text = entry["text"]
            doc_type = entry["doc_type"]
@@ -229,7 +236,8 @@ async def _run_compliance_check(check_id: str, req: ComplianceCheckRequest):
                ))
                continue

-            _update(check_id, f"Pruefe {label} ({i+1}/{len(doc_entries)})...")
+            pct = int(40 + (i / n_entries) * 40)
+            _update(check_id, f"Pruefen {i+1}/{n_entries}: {label}...", pct)

            if not text or len(text) < 50:
                results.append(DocCheckResult(
@@ -268,7 +276,7 @@ async def _run_compliance_check(check_id: str, req: ComplianceCheckRequest):
            parsed = urlparse(banner_url)
            banner_url = f"{parsed.scheme}://{parsed.netloc}"
        if banner_url:
-            _update(check_id, "Cookie-Banner wird geprueft...")
+            _update(check_id, "Cookie-Banner wird geprueft...", 82)
            try:
                async with httpx.AsyncClient(timeout=120.0) as client:
                    resp = await client.post(
@@ -280,9 +288,9 @@ async def _run_compliance_check(check_id: str, req: ComplianceCheckRequest):
            except Exception as e:
                logger.warning("Banner check failed: %s", e)

-        # Step 3c: Cross-check Banner vs Cookie-Richtlinie
+        # Step 3c: Cross-check Banner vs Cookie-Richtlinie (88-90%)
        if banner_result and "cookie" in doc_texts:
-            _update(check_id, "Banner vs. Cookie-Richtlinie abgleichen...")
+            _update(check_id, "Banner vs. Cookie-Richtlinie abgleichen...", 89)
            cross_findings = _cross_check_banner_vs_cookie(
                banner_result, doc_texts["cookie"],
            )
@@ -299,7 +307,7 @@ async def _run_compliance_check(check_id: str, req: ComplianceCheckRequest):
        tcf_vendors = banner_result.get("tcf_vendors", []) if banner_result else []
        vvt_entries: list[dict] = []
        if tcf_vendors and "dse" in doc_texts:
-            _update(check_id, f"{len(tcf_vendors)} TCF-Verarbeiter vs. DSI abgleichen...")
+            _update(check_id, f"{len(tcf_vendors)} TCF-Verarbeiter vs. DSI abgleichen...", 91)
            from compliance.services.banner_cookie_cross_check import cross_check_vendors_vs_dsi
            from compliance.services.vendor_vvt_mapper import map_vendors_to_vvt
            vendor_findings = cross_check_vendors_vs_dsi(tcf_vendors, doc_texts["dse"])
@@ -310,8 +318,8 @@ async def _run_compliance_check(check_id: str, req: ComplianceCheckRequest):
                            r.checks.append(CheckItem(**vf))
            vvt_entries = map_vendors_to_vvt(tcf_vendors)

-        # Step 4: Extract profile hints from documents
-        _update(check_id, "Profil wird aus Dokumenten extrahiert...")
+        # Step 4: Extract profile hints from documents (92-95%)
+        _update(check_id, "Profil wird aus Dokumenten extrahiert...", 93)
        from compliance.services.profile_extractor import extract_profile_from_documents
        extracted_profile = extract_profile_from_documents(doc_texts, profile_dict)

@@ -326,21 +334,32 @@ async def _run_compliance_check(check_id: str, req: ComplianceCheckRequest):
            else:
                r.scenario = "import"

-        # Step 5: Build report with management summary
-        _update(check_id, "Report wird erstellt...")
-        from .agent_doc_check_report import build_management_summary
+        # Step 5: Build report with management summary (95-98%)
+        _update(check_id, "Report wird erstellt...", 96)
+        from .agent_doc_check_report import (
+            build_management_summary,
+            build_scanned_urls_html,
+            build_provider_list_html,
+        )
        summary_html = build_management_summary(results)
+        scanned_html = build_scanned_urls_html(doc_entries)
+        providers_html = build_provider_list_html(banner_result, vvt_entries)
        report_html = build_html_report(results, None)
        profile_html = _build_profile_html(profile)
-        full_html = summary_html + profile_html + report_html
-
-        # Step 6: Send email — include website/company name in subject
-        doc_count = len([r for r in results if not r.error])
-        site_name = (
-            extracted_profile.get("company_profile", {}).get("companyName")
-            or _extract_domain(doc_entries)
-            or "Unbekannt"
+        full_html = (
+            summary_html + scanned_html + profile_html
+            + providers_html + report_html
        )
+
+        # Step 6: Send email — derive site name primarily from entered URL.
+        # The extracted_profile.companyName is often noisy (e.g. picks up
+        # juris.de from legal references). Domain-derived name is more
+        # predictable for the GF email subject.
+        doc_count = len([r for r in results if not r.error])
+        url_company = _company_name_from_url(doc_entries)
+        domain = _extract_domain(doc_entries)
+        site_name = url_company or domain or "Unbekannt"
+        _update(check_id, "E-Mail wird versendet...", 98)
        email_result = send_email(
            recipient=req.recipient,
            subject=f"[COMPLIANCE-CHECK] {site_name} — {doc_count} Dokumente geprueft",
@@ -368,6 +387,7 @@ async def _run_compliance_check(check_id: str, req: ComplianceCheckRequest):
        _compliance_check_jobs[check_id]["status"] = "completed"
        _compliance_check_jobs[check_id]["result"] = response
        _compliance_check_jobs[check_id]["progress"] = "Fertig"
+        _compliance_check_jobs[check_id]["progress_pct"] = 100

    except Exception as e:
        logger.error("Compliance check %s failed: %s", check_id, e, exc_info=True)
@@ -375,8 +395,11 @@ async def _run_compliance_check(check_id: str, req: ComplianceCheckRequest):
        _compliance_check_jobs[check_id]["error"] = str(e)[:500]


-def _update(check_id: str, msg: str):
-    _compliance_check_jobs[check_id]["progress"] = msg
+def _update(check_id: str, msg: str, pct: int | None = None):
+    job = _compliance_check_jobs[check_id]
+    job["progress"] = msg
+    if pct is not None:
+        job["progress_pct"] = max(0, min(100, int(pct)))


 async def _fetch_text(url: str) -> str:
@@ -503,14 +526,59 @@ async def _check_single(
    )


+_COMPOUND_TLDS = {
+    "co.uk", "co.jp", "co.nz", "co.kr", "co.za", "co.in",
+    "com.au", "com.br", "com.mx", "com.tr", "com.sg",
+}
+
+
 def _extract_domain(doc_entries: list[dict]) -> str | None:
-    """Extract domain name from first URL for email subject."""
+    """Extract base domain (without www) from first URL."""
    for entry in doc_entries:
        url = entry.get("url", "")
        if url and "://" in url:
            from urllib.parse import urlparse
-            host = urlparse(url).netloc
-            return host.replace("www.", "") if host else None
+            host = urlparse(url).netloc.lower()
+            if host.startswith("www."):
+                host = host[4:]
+            return host or None
+    return None
+
+
+def _company_name_from_url(doc_entries: list[dict]) -> str | None:
+    """Derive a display company name from the entered URLs.
+
+    Heuristic: take the second-level domain (e.g. "bmw" from "www.bmw.de"),
+    uppercase short acronyms (<=4 chars, no hyphens), title-case the rest.
+
+    Examples:
+      www.bmw.de              -> BMW
+      mercedes-benz.de        -> Mercedes-Benz
+      shop.example.co.uk      -> Example
+      juris.de                -> Juris
+    """
+    from urllib.parse import urlparse
+
+    for entry in doc_entries:
+        url = entry.get("url", "")
+        if not url or "://" not in url:
+            continue
+        host = urlparse(url).netloc.lower()
+        if host.startswith("www."):
+            host = host[4:]
+        parts = host.split(".")
+        if len(parts) < 2:
+            continue
+        # Handle compound TLDs (.co.uk etc.)
+        if len(parts) >= 3 and ".".join(parts[-2:]) in _COMPOUND_TLDS:
+            sld = parts[-3]
+        else:
+            sld = parts[-2]
+        if not sld:
+            continue
+        if len(sld) <= 4 and "-" not in sld:
+            return sld.upper()
+        return "-".join(p.capitalize() for p in sld.split("-"))
    return None


@@ -0,0 +1,226 @@
+"""
+Extras for the agent doc-check email report.
+
+Split out from agent_doc_check_report.py to keep both files under the
+500-line hard cap. Contains:
+  - build_scanned_urls_html      (list of fetched URLs + cross-domain notice)
+  - build_provider_list_html     (cookie banner + TCF vendor table)
+"""
+
+from __future__ import annotations
+
+
+def build_scanned_urls_html(doc_entries: list[dict]) -> str:
+    """Render the list of scanned URLs at the top of the report.
+
+    Transparent for the GF which sources were actually fetched/analysed.
+    Skips empty URLs (text-only uploads). Adds a cross-domain warning when
+    legal texts are distributed across multiple domains (e.g. BMW spreads
+    across bmw.de, bmwgroup.com, bmwgroup.jobs).
+    """
+    from urllib.parse import urlparse
+
+    rows: list[str] = []
+    seen: set[str] = set()
+    domains: dict[str, list[str]] = {}  # netloc -> list of doc_types
+    for entry in doc_entries:
+        url = (entry.get("url") or "").strip()
+        if not url or url in seen:
+            continue
+        seen.add(url)
+        label = _doc_type_label(entry.get("doc_type", ""))
+        words = entry.get("word_count") or 0
+        try:
+            netloc = urlparse(url).netloc.lower().lstrip("www.")
+            if netloc:
+                domains.setdefault(netloc, []).append(label)
+        except Exception:
+            pass
+        rows.append(
+            f'<tr>'
+            f'<td style="padding:3px 12px 3px 0;color:#475569;font-size:12px">{label}</td>'
+            f'<td style="padding:3px 12px 3px 0;font-size:12px;'
+            f'font-family:ui-monospace,monospace;color:#1e293b;word-break:break-all">'
+            f'<a href="{url}" style="color:#2563eb;text-decoration:none">{url}</a></td>'
+            f'<td style="padding:3px 0;color:#94a3b8;font-size:11px;text-align:right;'
+            f'white-space:nowrap">{words} Woerter</td>'
+            f'</tr>'
+        )
+    if not rows:
+        return ""
+
+    cross_domain_html = _cross_domain_notice(domains) if len(domains) >= 2 else ""
+
+    return (
+        '<div style="font-family:-apple-system,BlinkMacSystemFont,sans-serif;'
+        'max-width:700px;margin:0 auto 16px;padding:12px 16px;'
+        'background:#fafafa;border:1px solid #e5e7eb;border-radius:8px">'
+        '<h3 style="margin:0 0 8px;font-size:14px;color:#334155">'
+        f'Gepruefte Quellen ({len(rows)})</h3>'
+        '<table style="width:100%;border-collapse:collapse">'
+        + "".join(rows)
+        + '</table>'
+        + cross_domain_html
+        + '</div>'
+    )
+
+
+def _cross_domain_notice(domains: dict[str, list[str]]) -> str:
+    """Warning box when legal texts are spread across multiple domains.
+
+    Relevant for big corporate groups (BMW Group: bmw.de / bmwgroup.com /
+    bmwgroup.jobs). Affects findability for data subjects and may indicate
+    incomplete disclosure on the main site.
+    """
+    items = []
+    for netloc, labels in sorted(domains.items()):
+        labels_str = ", ".join(sorted(set(labels)))
+        items.append(
+            f'<li style="margin-bottom:2px"><strong>{netloc}</strong> '
+            f'<span style="color:#92400e;font-size:11px">&rarr; {labels_str}</span></li>'
+        )
+    return (
+        '<div style="margin-top:12px;padding:10px 12px;background:#fffbeb;'
+        'border-left:3px solid #f59e0b;border-radius:4px;font-size:12px;'
+        'color:#78350f">'
+        '<strong>Hinweis: Rechtstexte verteilt auf '
+        f'{len(domains)} Domains.</strong> '
+        'Erschwert die Auffindbarkeit fuer Betroffene (Art. 12 Abs. 1 DSGVO &mdash; '
+        'transparente Information). Pruefen Sie, ob alle Texte auch von der '
+        'Hauptdomain aus klar verlinkt sind.'
+        '<ul style="margin:6px 0 0 16px;padding-left:0">'
+        + "".join(items) +
+        '</ul></div>'
+    )
+
+
+def _doc_type_label(doc_type: str) -> str:
+    """Lazy resolver — avoids circular import with agent_compliance_check_routes."""
+    labels = {
+        "dse": "Datenschutzerklaerung",
+        "datenschutz": "Datenschutzerklaerung",
+        "privacy": "Datenschutzerklaerung",
+        "impressum": "Impressum",
+        "agb": "AGB",
+        "widerruf": "Widerrufsbelehrung",
+        "cookie": "Cookie-Richtlinie",
+        "avv": "Auftragsverarbeitung",
+        "loeschkonzept": "Loeschkonzept",
+        "dsfa": "Datenschutz-Folgenabschaetzung",
+        "social_media": "Social Media Datenschutz",
+        "nutzungsbedingungen": "Nutzungsbedingungen",
+        "dsb": "DSB-Kontakt",
+    }
+    return labels.get(doc_type, doc_type.upper() if doc_type else "Dokument")
+
+
+def build_provider_list_html(
+    banner_result: dict | None,
+    vvt_entries: list[dict] | None,
+) -> str:
+    """Render the cookie banner result + TCF vendor table for the email.
+
+    Sections:
+      1. Banner summary (provider, violations count)
+      2. Vendor table: Name | Kategorie | Zweck | Drittland | Rechtsgrundlage
+    """
+    if not banner_result and not vvt_entries:
+        return ""
+
+    parts: list[str] = [
+        '<div style="font-family:-apple-system,BlinkMacSystemFont,sans-serif;'
+        'max-width:700px;margin:0 auto 16px;padding:12px 16px;'
+        'background:#fafafa;border:1px solid #e5e7eb;border-radius:8px">'
+        '<h3 style="margin:0 0 10px;font-size:14px;color:#334155">'
+        'Cookie-Banner &amp; Verarbeiter</h3>'
+    ]
+
+    if banner_result:
+        detected = banner_result.get("banner_detected", False)
+        provider = banner_result.get("banner_provider") or "unbekannt"
+        violations = banner_result.get("banner_checks", {}).get("violations", [])
+        n_viol = len(violations) if isinstance(violations, list) else int(violations or 0)
+
+        status_color = "#16a34a" if detected and n_viol == 0 else (
+            "#d97706" if detected else "#6b7280"
+        )
+        parts.append(
+            f'<div style="font-size:13px;color:#374151;margin-bottom:10px">'
+            f'<span style="display:inline-block;width:8px;height:8px;'
+            f'border-radius:50%;background:{status_color};margin-right:8px"></span>'
+            f'Banner erkannt: <strong>{"Ja" if detected else "Nein"}</strong>'
+            f' &nbsp;&middot;&nbsp; Anbieter: <strong>{provider}</strong>'
+            f' &nbsp;&middot;&nbsp; Auffaelligkeiten: <strong>{n_viol}</strong>'
+            f'</div>'
+        )
+
+    vendors = vvt_entries or []
+    if vendors:
+        parts.append(
+            f'<div style="font-size:12px;color:#475569;margin:8px 0 6px">'
+            f'<strong>{len(vendors)} TCF-Verarbeiter ueber das Banner eingebunden:</strong>'
+            f'</div>'
+            '<table style="width:100%;border-collapse:collapse;font-size:11px">'
+            '<thead><tr style="background:#f1f5f9;color:#475569;text-align:left">'
+            '<th style="padding:5px 8px">Name</th>'
+            '<th style="padding:5px 8px">Kategorie</th>'
+            '<th style="padding:5px 8px">Zweck</th>'
+            '<th style="padding:5px 8px">Drittland</th>'
+            '<th style="padding:5px 8px">Rechtsgrundlage</th>'
+            '</tr></thead><tbody>'
+        )
+        for v in vendors[:50]:
+            parts.append(_render_vendor_row(v))
+        parts.append('</tbody></table>')
+        if len(vendors) > 50:
+            parts.append(
+                f'<div style="font-size:11px;color:#94a3b8;margin-top:4px">'
+                f'... und {len(vendors) - 50} weitere</div>'
+            )
+    elif banner_result and banner_result.get("banner_detected"):
+        parts.append(
+            '<div style="font-size:11px;color:#94a3b8">'
+            'Keine TCF-Verarbeiter erkannt (Banner nutzt kein TCF v2 Framework '
+            'oder Vendor-Liste konnte nicht ausgelesen werden).</div>'
+        )
+
+    parts.append('</div>')
+    return "".join(parts)
+
+
+def _render_vendor_row(v: dict) -> str:
+    name = v.get("name") or "Unbekannt"
+    kategorie = _category_label(v.get("kategorie", ""))
+    zweck = v.get("zweck_kurz") or ", ".join((v.get("zweck") or [])[:2])
+    drittland = v.get("drittland")
+    land = v.get("land") or ""
+    if drittland is True:
+        drittland_str = (f'<span style="color:#dc2626">Ja ({land})</span>'
+                         if land else '<span style="color:#dc2626">Ja</span>')
+    elif drittland is False:
+        drittland_str = (f'<span style="color:#16a34a">Nein ({land})</span>'
+                         if land else '<span style="color:#16a34a">Nein</span>')
+    else:
+        drittland_str = '<span style="color:#94a3b8">unbekannt</span>'
+    rg = v.get("rechtsgrundlage", "")
+    rg_short = "Einwilligung" if "Einwilligung" in rg else (
+        "Berechtigtes Interesse" if "Berechtigtes" in rg else rg[:40]
+    )
+    return (
+        f'<tr style="border-top:1px solid #e2e8f0">'
+        f'<td style="padding:4px 8px;color:#1e293b">{name}</td>'
+        f'<td style="padding:4px 8px;color:#475569">{kategorie}</td>'
+        f'<td style="padding:4px 8px;color:#475569">{zweck}</td>'
+        f'<td style="padding:4px 8px">{drittland_str}</td>'
+        f'<td style="padding:4px 8px;color:#475569">{rg_short}</td>'
+        f'</tr>'
+    )
+
+
+def _category_label(kat: str) -> str:
+    return {
+        "necessary": "Notwendig",
+        "functional": "Funktional",
+        "statistics": "Statistik",
+        "marketing": "Marketing",
+    }.get(kat, kat or "—")
@@ -290,6 +290,15 @@ def _render_cookie_banner(html: list[str], cookie_result: dict) -> None:
    html.append('</div>')


+# Re-export the helpers extracted to agent_doc_check_extras.py so existing
+# callers that did `from .agent_doc_check_report import build_scanned_urls_html`
+# keep working.
+from .agent_doc_check_extras import (  # noqa: E402,F401
+    build_provider_list_html,
+    build_scanned_urls_html,
+)
+
+
 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"
@@ -39,10 +39,13 @@ _B2C_KEYWORDS = [
 ]

 _B2B_KEYWORDS = [
-    "unternehmen", "geschaeftskunden", "geschäftskunden", "gewerblich",
-    "auftraggeber", "auftragnehmer", "geschaeftspartner",
-    "geschäftspartner", "firmenkunde", "b2b", "industriekunden",
-    "beratung", "consulting", "dienstleistung", "engineering",
+    # Discriminative — these don't appear in B2C consumer texts
+    "geschaeftskunden", "geschäftskunden", "firmenkunde", "b2b",
+    "industriekunden", "ausschliesslich gewerblich", "ausschließlich gewerblich",
+    "ausschliesslich unternehmer", "ausschließlich unternehmer",
+    "kein verbrauchergeschaeft", "kein verbrauchergeschäft",
+    # Note: "unternehmen", "beratung", "consulting", "dienstleistung"
+    # were removed — they match in any company text and bias toward B2B.
 ]

 _B2G_KEYWORDS = [
@@ -116,9 +119,20 @@ _INDUSTRY_KEYWORDS = {
                    "arbeitssicherheit", "brandschutz", "sicherheitstechnik", "zertifizierung"],
    "manufacturing": ["fertigung", "produktion", "maschinenbau", "anlagenbau", "zulieferer",
                       "werkzeugbau", "spritzguss", "cnc", "industrietechnik"],
+    "automotive": ["fahrzeug", "kraftfahrzeug", "kfz", "automobil", "neuwagen",
+                    "gebrauchtwagen", "konfigurator", "modellreihe", "modellpalette"],
    "media": ["redaktion", "verlag", "medien", "journalismus", "presse"],
 }

+# Terms that indicate "versicherung" / "bank" is only mentioned as a
+# §34d/§34c GewO disclosure (Versicherungsvermittler / Finanzanlagenvermittler)
+# rather than the core business. Used to suppress false finance matches.
+_VERMITTLER_CONTEXT_TERMS = [
+    "versicherungsvermittler", "berufshaftpflichtversicherung",
+    "vermittlerregister", "§34d", "§ 34 d", "§34c", "§ 34 c",
+    "finanzanlagenvermittler", "ihk muenchen", "ihk münchen",
+]
+
 _TRACKING_SERVICES = {
    "google analytics": "Google Analytics",
    "google tag manager": "Google Tag Manager",
@@ -231,13 +245,23 @@ async def detect_business_profile(documents: dict[str, str]) -> BusinessProfile:
        total = sum(max(0, v) for v in scores.values())
        profile.confidence = round(best_val / total, 2) if total > 0 else 0.5
    else:
-        # Fallback: GmbH/AG without B2C signals → assume B2B
-        has_company = any(kw in full_text for kw in [
-            "gmbh", "ag ", "ohg", "kg ", "ug ", "gbr",
-        ])
-        if has_company and b2c_score <= 0:
+        # Fallback: prefer B2C when the text mentions Verbraucherrechte,
+        # editorial content, or consumer-direction signals — even without
+        # checkout keywords. Only fall back to B2B if discriminative B2B
+        # markers fired (which the keyword list above already filtered to
+        # genuinely B2B-only terms).
+        consumer_hint = (
+            "verbraucher" in full_text
+            or "widerruf" in full_text
+            or "kunde" in full_text
+            or profile.has_editorial_content
+        )
+        if b2b_score >= 1 and not consumer_hint:
            profile.business_type = "b2b"
            profile.confidence = 0.4
+        elif consumer_hint:
+            profile.business_type = "b2c"
+            profile.confidence = 0.4
        else:
            profile.business_type = "unknown"
            profile.confidence = 0.2
@@ -255,8 +279,31 @@ async def detect_business_profile(documents: dict[str, str]) -> BusinessProfile:
        if hits >= 1:
            industry_scores[industry] = hits

+    # Suppress finance/insurance false positives caused by §34d/§34c GewO
+    # disclosures (Versicherungsvermittler, Berufshaftpflicht, etc.) — these
+    # are pflichtangaben for many companies (e.g. BMW AG) without being
+    # actual financial services providers.
+    if industry_scores.get("finance"):
+        vermittler_hits = _count_hits(full_text, _VERMITTLER_CONTEXT_TERMS)
+        if vermittler_hits >= 2:
+            # Only the §34d boilerplate triggered the match — drop or shrink.
+            non_insurance_finance = _count_hits(
+                full_text, ["bank", "finanz", "kredit", "anlage"],
+            )
+            if non_insurance_finance == 0:
+                industry_scores.pop("finance", None)
+            else:
+                industry_scores["finance"] = non_insurance_finance
+
+    # Require a clear winner — if top score is 1 and there are ties, prefer
+    # "unknown" over guessing.
    if industry_scores:
-        profile.industry = max(industry_scores, key=industry_scores.get)  # type: ignore[arg-type]
+        top = max(industry_scores.values())
+        winners = [k for k, v in industry_scores.items() if v == top]
+        if top >= 2 or len(winners) == 1:
+            profile.industry = winners[0]
+        else:
+            profile.industry = "unknown"
    elif profile.is_regulated_profession:
        prof_map = {"anwalt": "legal", "arzt": "healthcare",
                    "steuerberater": "finance", "architekt": "craft"}
@@ -273,18 +273,35 @@ async def discover_dsi_documents(
            is_self_dsi, self_lang = _matches_dsi_keyword(page_title)
        if is_self_dsi:
            try:
+                # Wait for substantive content to appear (SPAs need time to render).
+                # Polls body.innerText length up to 10s. Many sites (BMW, Daimler)
+                # render via React/Vue after domcontentloaded fires.
+                try:
+                    await page.wait_for_function(
+                        "() => (document.body && document.body.innerText || '').length > 500",
+                        timeout=10000,
+                    )
+                except Exception:
+                    pass  # Continue anyway, extractor below has fallbacks
+
                # Scroll to bottom to trigger lazy-loading of full content
                await page.evaluate("window.scrollTo(0, document.body.scrollHeight)")
                await page.wait_for_timeout(1500)
                await page.evaluate("window.scrollTo(0, document.body.scrollHeight)")
                await page.wait_for_timeout(1000)

-                self_text = await page.evaluate("""() => {
-                    const main = document.querySelector('main, article, [role="main"], .content, #content, .bodytext')
-                        || document.body;
-                    return main ? main.innerText : document.body.innerText;
-                }""")
+                self_text = await _extract_text_robust(page)
                self_wc = len(self_text.split()) if self_text else 0
+
+                # If still too short, try same-origin iframes (some sites
+                # embed cookie policies via OneTrust/Sourcepoint iframes).
+                if self_wc < 100:
+                    iframe_text = await _extract_text_from_iframes(page)
+                    if iframe_text and len(iframe_text.split()) > self_wc:
+                        self_text = iframe_text
+                        self_wc = len(self_text.split())
+                        logger.info("Self-extraction via iframe for %s: %d words", url, self_wc)
+
                if self_wc >= 100:
                    page_title = await page.title() or url
                    result.documents.append(DiscoveredDSI(
@@ -622,3 +639,83 @@ async def _find_inline_dsi_sections(page: Page) -> list[dict]:
        return sections or []
    except Exception:
        return []
+
+
+async def _extract_text_robust(page: Page) -> str:
+    """Multi-strategy text extraction for SPA-heavy pages (BMW, Daimler, etc).
+
+    Tries progressively broader selectors, falls back to body-minus-chrome,
+    final fallback: join all paragraph/list/cell tags' textContent.
+    """
+    try:
+        return await page.evaluate("""
+            () => {
+                // 1) Specific content containers
+                const selectors = [
+                    '.article-content', '.page-content', '.entry-content',
+                    '[class*="content-area"]', '[class*="main-content"]',
+                    '[class*="legal-text"]', '[class*="policy-content"]',
+                    'main article', 'main', 'article',
+                    '[role="main"]', '.content', '#content', '.bodytext',
+                ];
+                for (const sel of selectors) {
+                    const el = document.querySelector(sel);
+                    if (el && el.textContent.trim().length > 200) {
+                        return el.textContent.trim().replace(/\\s+/g, ' ');
+                    }
+                }
+                // 2) Body minus nav/header/footer/scripts
+                const body = document.body.cloneNode(true);
+                body.querySelectorAll(
+                    'nav, header, footer, script, style, noscript,' +
+                    ' [class*="nav"], [class*="sidebar"], [class*="cookie"],' +
+                    ' [class*="banner"], [id*="cookie"], [id*="banner"]'
+                ).forEach(e => e.remove());
+                const bodyText = (body.textContent || '').trim().replace(/\\s+/g, ' ');
+                if (bodyText.length > 200) return bodyText;
+                // 3) Final fallback: collect all text-bearing tags
+                const blocks = document.querySelectorAll('p, li, dd, td, h1, h2, h3, h4');
+                const parts = [];
+                for (const b of blocks) {
+                    const t = (b.textContent || '').trim();
+                    if (t.length > 20) parts.push(t);
+                }
+                return parts.join(' ').replace(/\\s+/g, ' ');
+            }
+        """) or ""
+    except Exception as e:
+        logger.warning("Robust text extraction failed: %s", e)
+        return ""
+
+
+async def _extract_text_from_iframes(page: Page) -> str:
+    """Collect text from same-origin iframes (OneTrust, Sourcepoint embeds).
+
+    Many sites render cookie policies inside iframes managed by CMP vendors.
+    """
+    try:
+        from urllib.parse import urlparse
+        page_host = urlparse(page.url).netloc
+        chunks: list[str] = []
+        for frame in page.frames:
+            if frame == page.main_frame:
+                continue
+            try:
+                frame_host = urlparse(frame.url).netloc
+                # Accept same-origin or known CMP frames
+                if frame_host and frame_host != page_host:
+                    cmp_hosts = ("onetrust", "cookiebot", "consensu", "sourcepoint",
+                                 "usercentrics", "didomi", "klaro")
+                    if not any(h in frame_host for h in cmp_hosts):
+                        continue
+                text = await frame.evaluate(
+                    "() => (document.body && document.body.innerText || '').trim()"
+                )
+                if text and len(text.split()) > 50:
+                    chunks.append(text)
+            except Exception:
+                continue
+        return "\n\n".join(chunks)
+    except Exception as e:
+        logger.debug("Iframe extraction failed: %s", e)
+        return ""