feat: Add Document Crawler & Auto-Onboarding service (Phase 1.4)

New standalone Python/FastAPI service for automatic compliance document
scanning, LLM-based classification, IPFS archival, and gap analysis.
Includes extractors (PDF, DOCX, XLSX, PPTX), keyword fallback classifier,
compliance matrix, and full REST API on port 8098.

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

document-crawler/gap_analysis/analyzer.py

@@ -0,0 +1,59 @@
+"""Gap detection logic — compares found documents against compliance matrix."""
+
+import uuid
+from .compliance_matrix import COMPLIANCE_MATRIX, RequiredDocument
+
+
+def generate_gap_analysis(
+    classification_counts: dict[str, int],
+    company_profiles: list[str] | None = None,
+) -> dict:
+    """Analyze gaps between found documents and required compliance matrix.
+
+    Args:
+        classification_counts: e.g. {"VVT": 2, "TOM": 1, "DSE": 0}
+        company_profiles: list of applicable profiles.
+            Default: ["universal", "data_processor", "ai_user"]
+
+    Returns dict with compliance_score, gaps list, classification_breakdown.
+    """
+    if company_profiles is None:
+        company_profiles = ["universal", "data_processor", "ai_user"]
+
+    applicable = [
+        req for req in COMPLIANCE_MATRIX
+        if req.applies_to in company_profiles
+    ]
+
+    gaps = []
+    covered = 0
+
+    for req in applicable:
+        count = classification_counts.get(req.category, 0)
+        if count == 0:
+            gaps.append({
+                "id": str(uuid.uuid4()),
+                "category": req.category,
+                "description": req.description,
+                "severity": req.severity,
+                "regulation": req.regulation,
+                "requiredAction": f"{req.category} erstellen und dokumentieren",
+                "relatedStepId": None,
+            })
+        else:
+            covered += 1
+
+    total_required = len(applicable)
+    compliance_score = (covered / total_required * 100) if total_required > 0 else 0
+
+    return {
+        "compliance_score": round(compliance_score, 1),
+        "total_required": total_required,
+        "covered": covered,
+        "gaps": gaps,
+        "gap_summary": {
+            "critical": sum(1 for g in gaps if g["severity"] == "CRITICAL"),
+            "high": sum(1 for g in gaps if g["severity"] == "HIGH"),
+            "medium": sum(1 for g in gaps if g["severity"] == "MEDIUM"),
+        },
+    }