feat(audit): P107 Branchen-Benchmark-Cockpit fuer Big-4-Demos

benchmark_extractor.py — extract_kpis() liefert 18 KPIs pro Snapshot:
* vendors_total, vendors_us, vendors_non_eu (mit % je Vendor-Land)
* source_breakdown (llm/library/flat_pattern/table_paste/html_table_dom)
* max/avg cookies_per_vendor (Konzentrations-Mass)
* cookies_in_browser, cookies_detailed_count, cookie_doc_chars
* banner_detected, banner_provider, banner_violations
* compliance_score, data_quality_pct (wie viele unserer Datenquellen
  haben Inhalt)
* saving_low/high_eur (Heuristik: (vendors - 10) × 1k-5k)

anonymize_kpis() ersetzt site_label durch 'OEM 1/2/3' (Industry-Prefix
Map: automotive→OEM, banking→Bank, chemistry→Chem, luftfahrt→Airline).

GET /api/compliance/agent/admin/benchmark?industry=automotive&sites=
VW,BMW,Mercedes&anonymized=true — liefert kpis + summary
(n_sites, avg_vendors, total_saving_high).

Admin-Page /sdk/benchmark:
* Filter-Leiste: Industry-Dropdown, Sites-Input + 5 Preset-Gruppen
  (Automotive OEMs / Zulieferer, Chemie DAX, Luftfahrt, Banking DAX)
* Anonymize-Toggle prominent
* 5 Summary-KPI-Karten oben
* Vergleichstabelle 13 Spalten (Score, Vendors, US%, Drittland%,
  Cookies-Browser, Cookie-Doc-kB, Banner ✓/✗, Provider, Verstoesse,
  Saving €/Jahr, Daten-Qualitaet, Captured-Time)
* Red-/Amber-/Green-Indikatoren bei US%/Score/Drittland
* Big-4-Hinweis-Footer

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

admin-compliance/app/sdk/benchmark/page.tsx

@@ -0,0 +1,266 @@
+'use client'
+
+/**
+ * P107 — Branchen-Benchmark-Cockpit.
+ *
+ * Multi-Site-Vergleich auf einen Blick. Anonymize-Toggle für Big-4-
+ * Wirtschaftspruefer-Demos.
+ *
+ * URL: /sdk/benchmark
+ */
+
+import React, { useState, useEffect } from 'react'
+
+interface Kpi {
+  check_id: string
+  site_label: string
+  site_domain: string
+  captured_at: string
+  industry: string
+  vendors_total: number
+  vendors_us: number
+  vendors_non_eu: number
+  us_pct: number
+  non_eu_pct: number
+  source_breakdown: Record<string, number>
+  max_cookies_per_vendor: number
+  avg_cookies_per_vendor: number
+  cookies_in_browser: number
+  cookies_detailed_count: number
+  cookie_doc_chars: number
+  banner_detected: boolean
+  banner_provider: string
+  banner_violations: number
+  compliance_score: number | null
+  saving_low_eur: number
+  saving_high_eur: number
+  data_quality_pct: number
+}
+
+interface Summary {
+  n_sites: number
+  avg_vendors: number
+  avg_us_pct: number
+  avg_non_eu_pct: number
+  avg_cookies_browser: number
+  avg_score: number
+  max_vendors: number
+  max_saving_high: number
+  total_saving_low: number
+  total_saving_high: number
+}
+
+const INDUSTRIES = [
+  { id: '',           label: 'Alle Branchen' },
+  { id: 'automotive', label: 'Automotive (OEM)' },
+  { id: 'banking',    label: 'Banking / Finance' },
+  { id: 'chemistry',  label: 'Chemie / Pharma' },
+  { id: 'luftfahrt',  label: 'Luftfahrt' },
+  { id: 'ecommerce',  label: 'E-Commerce' },
+  { id: 'saas',       label: 'SaaS / Software' },
+]
+
+const PRESET_GROUPS = [
+  { id: 'automotive_oem',  label: 'Automotive OEMs',     sites: 'Volkswagen,BMW,Mercedes-Benz,SEAT,AUDI' },
+  { id: 'automotive_supl', label: 'Automotive Zulieferer', sites: 'ZF Friedrichshafen,Robert Bosch,Continental' },
+  { id: 'chemie',          label: 'Chemie (DAX)',        sites: 'BASF,Bayer,Henkel,Linde' },
+  { id: 'luftfahrt',       label: 'Luftfahrt',           sites: 'Lufthansa,Eurowings,Condor' },
+  { id: 'banking',         label: 'Banking (DAX)',       sites: 'Deutsche Bank,Commerzbank,DZ Bank,KfW' },
+]
+
+export default function BenchmarkPage() {
+  const [industry,   setIndustry]   = useState('')
+  const [sites,      setSites]      = useState('')
+  const [anonymized, setAnonymized] = useState(false)
+  const [data,       setData]       = useState<{kpis: Kpi[]; summary: Summary} | null>(null)
+  const [loading,    setLoading]    = useState(false)
+  const [error,      setError]      = useState<string | null>(null)
+
+  const fetchData = async () => {
+    setLoading(true); setError(null)
+    try {
+      const url = new URL('/api/compliance/admin/benchmark', window.location.origin)
+      if (industry) url.searchParams.set('industry', industry)
+      if (sites)    url.searchParams.set('sites', sites)
+      if (anonymized) url.searchParams.set('anonymized', 'true')
+      const r = await fetch(url.toString())
+      if (!r.ok) throw new Error(`HTTP ${r.status}`)
+      setData(await r.json())
+    } catch (e: any) {
+      setError(e.message || String(e))
+    } finally {
+      setLoading(false)
+    }
+  }
+
+  useEffect(() => { fetchData() }, [])
+
+  return (
+    <div className="p-6 max-w-7xl mx-auto">
+      <header className="mb-6">
+        <h1 className="text-2xl font-bold text-gray-900">
+          Branchen-Benchmark-Cockpit
+        </h1>
+        <p className="text-sm text-gray-600 mt-1">
+          DAX-Konzern-Vergleich auf Basis aller bisher gepruefter Sites.
+          Mit Anonymize-Toggle fuer Wirtschaftspruefer-Demos.
+        </p>
+      </header>
+
+      {/* Filter-Leiste */}
+      <div className="bg-white border border-gray-200 rounded-lg p-4 mb-4 flex flex-wrap gap-3 items-end">
+        <div>
+          <label className="block text-xs font-medium text-gray-700 mb-1">Branche</label>
+          <select value={industry} onChange={e => setIndustry(e.target.value)}
+                  className="px-3 py-2 border rounded text-sm">
+            {INDUSTRIES.map(i => <option key={i.id} value={i.id}>{i.label}</option>)}
+          </select>
+        </div>
+        <div className="flex-1 min-w-[300px]">
+          <label className="block text-xs font-medium text-gray-700 mb-1">
+            Sites (komma-getrennt) oder Preset wählen
+          </label>
+          <input value={sites} onChange={e => setSites(e.target.value)}
+                 placeholder="Volkswagen,BMW,Mercedes-Benz"
+                 className="w-full px-3 py-2 border rounded text-sm font-mono" />
+          <div className="flex flex-wrap gap-1 mt-1">
+            {PRESET_GROUPS.map(p => (
+              <button key={p.id} onClick={() => setSites(p.sites)}
+                      className="px-2 py-0.5 text-[10px] bg-gray-100 hover:bg-gray-200 rounded">
+                {p.label}
+              </button>
+            ))}
+          </div>
+        </div>
+        <label className="flex items-center gap-2 text-sm cursor-pointer">
+          <input type="checkbox" checked={anonymized}
+                 onChange={e => setAnonymized(e.target.checked)}
+                 className="rounded" />
+          <span><strong>Anonymisieren</strong> (OEM 1/2/3 statt Hersteller-Namen)</span>
+        </label>
+        <button onClick={fetchData} disabled={loading}
+                className="px-4 py-2 bg-purple-600 text-white rounded font-medium hover:bg-purple-700 disabled:opacity-50">
+          {loading ? 'Lade…' : 'Aktualisieren'}
+        </button>
+      </div>
+
+      {error && (
+        <div className="bg-red-50 border border-red-200 text-red-700 rounded p-3 text-sm mb-4">
+          Fehler: {error}
+        </div>
+      )}
+
+      {/* Summary-KPIs */}
+      {data?.summary && (
+        <div className="grid grid-cols-2 md:grid-cols-5 gap-2 mb-4">
+          <Kpi label="Sites im Vergleich"    value={data.summary.n_sites} />
+          <Kpi label="⌀ Vendors"             value={data.summary.avg_vendors} />
+          <Kpi label="⌀ US-Anteil"           value={`${data.summary.avg_us_pct}%`}
+               tone={data.summary.avg_us_pct > 60 ? 'warn' : 'ok'} />
+          <Kpi label="⌀ Score"               value={data.summary.avg_score || '—'} />
+          <Kpi label="Saving-Potenzial (Σ)"  value={`${Math.round(data.summary.total_saving_high/1000)}k €`}
+               tone="ok" />
+        </div>
+      )}
+
+      {/* Vergleichstabelle */}
+      {data?.kpis && data.kpis.length > 0 ? (
+        <div className="bg-white border border-gray-200 rounded-lg overflow-x-auto">
+          <table className="w-full text-xs">
+            <thead className="bg-gray-50 text-gray-700">
+              <tr>
+                <th className="text-left px-3 py-2 sticky left-0 bg-gray-50">Site</th>
+                <th className="text-right px-2 py-2">Score</th>
+                <th className="text-right px-2 py-2">Vendors</th>
+                <th className="text-right px-2 py-2">US%</th>
+                <th className="text-right px-2 py-2">Drittland%</th>
+                <th className="text-right px-2 py-2">Cookies Browser</th>
+                <th className="text-right px-2 py-2">Cookie-Doc kB</th>
+                <th className="text-center px-2 py-2">Banner</th>
+                <th className="text-left px-2 py-2">Provider</th>
+                <th className="text-right px-2 py-2">Banner-Verstöße</th>
+                <th className="text-right px-2 py-2">Saving € Jahr</th>
+                <th className="text-right px-2 py-2">Daten-Qualität</th>
+                <th className="text-left px-2 py-2">Captured</th>
+              </tr>
+            </thead>
+            <tbody>
+              {data.kpis.map((k, i) => (
+                <tr key={i} className={`border-t hover:bg-gray-50 ${i%2 ? 'bg-gray-50/30' : ''}`}>
+                  <td className="px-3 py-2 font-semibold sticky left-0 bg-inherit">
+                    {k.site_label}
+                    <div className="text-[9px] text-gray-400 font-mono">{k.check_id}</div>
+                  </td>
+                  <td className={`px-2 py-2 text-right ${
+                    !k.compliance_score ? 'text-gray-400' :
+                    k.compliance_score >= 80 ? 'text-green-700' :
+                    k.compliance_score >= 60 ? 'text-amber-700' : 'text-red-700'
+                  }`}>
+                    {k.compliance_score ?? '—'}
+                  </td>
+                  <td className="px-2 py-2 text-right font-mono">{k.vendors_total}</td>
+                  <td className={`px-2 py-2 text-right ${k.us_pct > 60 ? 'text-red-700 font-semibold' : ''}`}>
+                    {k.us_pct}%
+                  </td>
+                  <td className={`px-2 py-2 text-right ${k.non_eu_pct > 70 ? 'text-red-700' : ''}`}>
+                    {k.non_eu_pct}%
+                  </td>
+                  <td className="px-2 py-2 text-right font-mono">{k.cookies_in_browser}</td>
+                  <td className="px-2 py-2 text-right text-gray-500">
+                    {Math.round(k.cookie_doc_chars / 1000)}k
+                  </td>
+                  <td className="px-2 py-2 text-center">{k.banner_detected ? '✓' : '✗'}</td>
+                  <td className="px-2 py-2 text-gray-600">{k.banner_provider || '—'}</td>
+                  <td className={`px-2 py-2 text-right ${k.banner_violations ? 'text-red-700' : 'text-gray-400'}`}>
+                    {k.banner_violations || 0}
+                  </td>
+                  <td className="px-2 py-2 text-right text-green-700 font-mono">
+                    {k.saving_high_eur ? `${(k.saving_high_eur/1000).toFixed(0)}k` : '—'}
+                  </td>
+                  <td className={`px-2 py-2 text-right ${
+                    k.data_quality_pct >= 70 ? 'text-green-700' :
+                    k.data_quality_pct >= 40 ? 'text-amber-700' : 'text-red-700'
+                  }`}>
+                    {k.data_quality_pct}%
+                  </td>
+                  <td className="px-2 py-2 text-[10px] text-gray-500">
+                    {k.captured_at?.substring(0, 16).replace('T', ' ')}
+                  </td>
+                </tr>
+              ))}
+            </tbody>
+          </table>
+        </div>
+      ) : !loading && (
+        <div className="bg-gray-50 border border-gray-200 rounded-lg p-8 text-center text-gray-500">
+          Keine Snapshots gefunden — Filter anpassen oder einen Audit-Lauf starten.
+        </div>
+      )}
+
+      <div className="mt-4 text-xs text-gray-500">
+        <strong>Big-4-Hinweis:</strong> Mit Anonymize-Toggle koennen wir den
+        kompletten Branchen-Cut zeigen ohne Hersteller-Namen zu nennen
+        (z.B. "OEM 3 hat 78% US-Vendor-Anteil"). Damit ist die Daten-
+        Hoheit bei BreakPilot und Big 4 sieht den Mehrwert ohne dass
+        Wettbewerber-Vergleiche extern werden.
+      </div>
+    </div>
+  )
+}
+
+function Kpi({ label, value, tone = 'neutral' }: {
+  label: string; value: any; tone?: 'ok' | 'warn' | 'bad' | 'neutral'
+}) {
+  const colors: Record<string, string> = {
+    ok: 'text-green-700 bg-green-50 border-green-200',
+    warn: 'text-amber-700 bg-amber-50 border-amber-200',
+    bad: 'text-red-700 bg-red-50 border-red-200',
+    neutral: 'text-gray-700 bg-white border-gray-200',
+  }
+  return (
+    <div className={`border rounded p-3 ${colors[tone]}`}>
+      <div className="text-[10px] uppercase tracking-wider opacity-70">{label}</div>
+      <div className="text-xl font-bold mt-1">{value}</div>
+    </div>
+  )
+}

backend-compliance/compliance/api/agent_compliance_check_routes.py

@@ -207,6 +207,42 @@ async def get_snapshot(snapshot_id: str):
         db.close()
 
 
+@router.get("/admin/benchmark")
+async def benchmark(
+    industry: str = "",
+    sites: str = "",
+    anonymized: bool = False,
+    limit: int = 50,
+):
+    """P107 — Branchen-Benchmark-Cockpit Endpoint.
+    industry: 'automotive' / 'banking' / etc (optional)
+    sites: comma-separated site_label list (optional)
+    anonymized: bool — wenn true, Hersteller-Namen → 'OEM 1/2/3'
+    """
+    from database import SessionLocal
+    from compliance.services.benchmark_extractor import (
+        load_snapshots_for_benchmark, anonymize_kpis,
+        build_benchmark_summary,
+    )
+    site_list = [s.strip() for s in sites.split(",") if s.strip()] if sites else None
+    db = SessionLocal()
+    try:
+        kpis = load_snapshots_for_benchmark(
+            db, industry=industry or None, sites=site_list, limit=limit,
+        )
+    finally:
+        db.close()
+    if anonymized:
+        kpis = anonymize_kpis(kpis, industry=industry)
+    return {
+        "industry":  industry or "all",
+        "anonymized": anonymized,
+        "sites":     [k.get("site_label") for k in kpis],
+        "kpis":      kpis,
+        "summary":   build_benchmark_summary(kpis),
+    }
+
+
 @router.post("/admin/tcf-ingest")
 async def tcf_ingest():
     """P105 — IAB TCF Vendor-Liste ingestieren / refreshen.

backend-compliance/compliance/services/benchmark_extractor.py

@@ -0,0 +1,265 @@
+"""
+P107 — Branchen-Benchmark-KPIs pro Snapshot.
+
+Extrahiert aus einem compliance_check_snapshot 18 KPIs die fuer den
+Multi-Site-Vergleich relevant sind. Wird vom /admin/benchmark Endpoint
+genutzt um Vergleichstabellen zu rendern.
+
+USP: keine andere Compliance-Software gibt einen Wirtschaftspruefer
+einen so granularen Branchen-Querschnitt. Bei DAX-Konzernen ist das
+ein echtes Verkaufs-Asset (Big 4 koennen es ihren Kunden als
+'wir sehen die ganze Branche' verkaufen).
+"""
+
+from __future__ import annotations
+
+import logging
+import re
+from typing import Any
+
+from sqlalchemy import text as sa_text
+from sqlalchemy.orm import Session
+
+logger = logging.getLogger(__name__)
+
+
+_US_COUNTRIES = {"US", "USA", "United States"}
+_NON_EU = {"US", "CN", "RU", "IN", "JP", "BR", "AU", "CA", "KR",
+            "MX", "ZA", "TR", "SG", "TW", "HK"}
+
+
+def _safe_int(v: Any, default: int = 0) -> int:
+    try:
+        return int(v)
+    except (TypeError, ValueError):
+        return default
+
+
+def _country_from_vendor(v: dict) -> str:
+    c = (v.get("country") or "").strip().upper()
+    if c:
+        return c
+    # Aus vendor_country wenn vorhanden (TCF-Authority Eintraege)
+    return ""
+
+
+def extract_kpis(snapshot: dict) -> dict:
+    """Liefert 18 KPIs aus einem snapshot-row.
+
+    Snapshot-row keys: id, check_id, site_label, site_domain, created_at,
+    banner_result, cmp_vendors, doc_entries, scan_context.
+    """
+    br = snapshot.get("banner_result") or {}
+    cv = snapshot.get("cmp_vendors") or []
+    de = snapshot.get("doc_entries") or []
+    sc = snapshot.get("scan_context") or {}
+
+    # Banner-Phase Cookies
+    phases = br.get("phases") or {}
+    after_accept = (phases.get("after_accept") or {})
+    cookies_in_browser = len(after_accept.get("cookies") or [])
+    cd = br.get("cookies_detailed") or []
+
+    # Doc-Text Lengths
+    doc_text_total = sum(len((d.get("text") or "")) for d in de)
+    cookie_doc_len = next(
+        (len(d.get("text") or "") for d in de if d.get("doc_type") == "cookie"), 0,
+    )
+
+    # Vendor breakdown
+    n_vendors = len(cv)
+    countries = [_country_from_vendor(v) for v in cv]
+    countries = [c for c in countries if c]
+    n_us = sum(1 for c in countries if c in _US_COUNTRIES)
+    n_non_eu = sum(1 for c in countries if c in _NON_EU)
+    us_pct = round(n_us / max(1, n_vendors) * 100, 1)
+    non_eu_pct = round(n_non_eu / max(1, n_vendors) * 100, 1)
+
+    # Vendor-Source-Mix
+    by_src: dict[str, int] = {}
+    for v in cv:
+        for s in (v.get("source") or "?").split(";"):
+            s = s.strip() or "?"
+            by_src[s] = by_src.get(s, 0) + 1
+
+    # Cookies pro Vendor (Konzentration)
+    cookie_counts = [len(v.get("cookies") or []) for v in cv]
+    max_cookies_per_vendor = max(cookie_counts) if cookie_counts else 0
+    avg_cookies_per_vendor = (
+        round(sum(cookie_counts) / max(1, len(cookie_counts)), 1)
+        if cookie_counts else 0
+    )
+
+    # Banner-Checks
+    bc = br.get("banner_checks") or {}
+    n_banner_violations = len(bc.get("violations") or [])
+    banner_detected = bool(br.get("banner_detected"))
+
+    # Compliance-Score (best effort)
+    score = br.get("compliance_score") or br.get("completeness_pct")
+
+    # Estimated Saving (Lizenz-Konsolidierung, Heuristik)
+    # Pro 5 Vendor ueber Median (10) rechnen wir ~5k EUR/Jahr Einsparung
+    median_vendors = 10
+    saving_low = max(0, (n_vendors - median_vendors)) * 1000
+    saving_high = max(0, (n_vendors - median_vendors)) * 5000
+
+    return {
+        # Header
+        "check_id":        snapshot.get("check_id"),
+        "site_label":      snapshot.get("site_label"),
+        "site_domain":     snapshot.get("site_domain"),
+        "captured_at":     (snapshot.get("created_at").isoformat()
+                            if snapshot.get("created_at") else None),
+        "industry":        (sc or {}).get("industry") or "",
+        # Vendor-KPIs
+        "vendors_total":   n_vendors,
+        "vendors_us":      n_us,
+        "vendors_non_eu":  n_non_eu,
+        "us_pct":          us_pct,
+        "non_eu_pct":      non_eu_pct,
+        "source_breakdown": by_src,
+        "max_cookies_per_vendor": max_cookies_per_vendor,
+        "avg_cookies_per_vendor": avg_cookies_per_vendor,
+        # Cookie-KPIs
+        "cookies_in_browser":     cookies_in_browser,
+        "cookies_detailed_count": len(cd),
+        "cookie_doc_chars":       cookie_doc_len,
+        "doc_text_chars_total":   doc_text_total,
+        # Banner
+        "banner_detected":        banner_detected,
+        "banner_provider":        br.get("banner_provider") or "",
+        "banner_violations":      n_banner_violations,
+        # Compliance / Score
+        "compliance_score":       score,
+        # Saving (Heuristik)
+        "saving_low_eur":         saving_low,
+        "saving_high_eur":        saving_high,
+        # Capture-Quality (wie viele unserer 10+ Audit-Quellen liefern Daten)
+        "data_quality_pct":       _quality_pct(snapshot),
+    }
+
+
+def _quality_pct(snapshot: dict) -> int:
+    """Wieviel Prozent der erwarteten Datenquellen haben Inhalt?"""
+    br = snapshot.get("banner_result") or {}
+    cv = snapshot.get("cmp_vendors") or []
+    de = snapshot.get("doc_entries") or []
+    cd = br.get("cookies_detailed") or []
+    aa = (br.get("phases") or {}).get("after_accept") or {}
+
+    checks = [
+        br.get("banner_detected") is True,
+        len(cv) > 0,
+        len(de) > 0,
+        len(cd) > 0,
+        len(aa.get("cookies") or []) > 0,
+        any((d.get("text") or "") for d in de),
+        br.get("compliance_score") is not None or br.get("completeness_pct") is not None,
+    ]
+    return round(sum(1 for x in checks if x) / len(checks) * 100)
+
+
+def load_snapshots_for_benchmark(
+    db: Session,
+    industry: str | None = None,
+    sites: list[str] | None = None,
+    limit: int = 50,
+) -> list[dict]:
+    """Liefert dicts mit Snapshot-Daten + extracted KPIs."""
+    where = []
+    params: dict[str, Any] = {}
+    if industry:
+        where.append("(scan_context->>'industry') = :ind")
+        params["ind"] = industry
+    if sites:
+        where.append("site_label = ANY(:sites)")
+        params["sites"] = sites
+    where_sql = " AND ".join(where) if where else "TRUE"
+
+    sql = (
+        "SELECT id::text, check_id, site_label, site_domain, created_at, "
+        "       banner_result, cmp_vendors, doc_entries, scan_context "
+        "FROM compliance.compliance_check_snapshots "
+        f"WHERE {where_sql} "
+        "ORDER BY created_at DESC LIMIT :lim"
+    )
+    params["lim"] = limit
+
+    rows = db.execute(sa_text(sql), params).fetchall()
+    out: list[dict] = []
+    for r in rows:
+        import json as _j
+        def _parse(v):
+            if isinstance(v, (dict, list)) or v is None:
+                return v
+            try:
+                return _j.loads(v)
+            except Exception:
+                return v
+        snap = {
+            "id":             r[0],
+            "check_id":       r[1],
+            "site_label":     r[2],
+            "site_domain":    r[3],
+            "created_at":     r[4],
+            "banner_result":  _parse(r[5]),
+            "cmp_vendors":    _parse(r[6]) or [],
+            "doc_entries":    _parse(r[7]) or [],
+            "scan_context":   _parse(r[8]) or {},
+        }
+        out.append(extract_kpis(snap))
+    return out
+
+
+def anonymize_kpis(kpis: list[dict], industry: str = "") -> list[dict]:
+    """Ersetzt site_label durch 'OEM 1', 'OEM 2' etc.
+    Industry-Prefix waehlbar (Automotive→OEM, Banking→Bank, Chemie→Chem).
+    """
+    prefix_map = {
+        "automotive": "OEM",
+        "banking":    "Bank",
+        "chemistry":  "Chem",
+        "luftfahrt":  "Airline",
+        "saas":       "SaaS",
+        "ecommerce":  "Shop",
+    }
+    pfx = prefix_map.get(industry.lower(), "Site")
+    # Stable alphabetical numbering for determinism
+    seen: dict[str, str] = {}
+    next_idx = 1
+    out = []
+    for k in sorted(kpis, key=lambda x: (x.get("site_label") or "")):
+        sl = k.get("site_label") or ""
+        if sl not in seen:
+            seen[sl] = f"{pfx} {next_idx}"
+            next_idx += 1
+        anon_k = dict(k)
+        anon_k["site_label"] = seen[sl]
+        anon_k["site_domain"] = f"site-{next_idx-1}.example"
+        out.append(anon_k)
+    return out
+
+
+def build_benchmark_summary(kpis: list[dict]) -> dict:
+    """Aggregate-Stats fuer den ganzen Branchen-Cut."""
+    if not kpis:
+        return {}
+    def avg(field: str) -> float:
+        vals = [k.get(field) for k in kpis if isinstance(k.get(field), (int, float))]
+        return round(sum(vals) / max(1, len(vals)), 1) if vals else 0
+    def maxv(field: str):
+        vals = [k.get(field) for k in kpis if isinstance(k.get(field), (int, float))]
+        return max(vals) if vals else 0
+    return {
+        "n_sites":           len(kpis),
+        "avg_vendors":       avg("vendors_total"),
+        "avg_us_pct":        avg("us_pct"),
+        "avg_non_eu_pct":    avg("non_eu_pct"),
+        "avg_cookies_browser": avg("cookies_in_browser"),
+        "avg_score":         avg("compliance_score"),
+        "max_vendors":       maxv("vendors_total"),
+        "max_saving_high":   maxv("saving_high_eur"),
+        "total_saving_low":  sum(k.get("saving_low_eur") or 0 for k in kpis),
+        "total_saving_high": sum(k.get("saving_high_eur") or 0 for k in kpis),
+    }