feat: NLP patch insights + standalone demo mode

- dlib/ai/insights.py: pure-Python NLP analysis that correlates accepted
  AI suggestion operations/keywords/sections with submission outcomes
  (pending_review / published = positive, archived = negative)
- Backend: GET /api/v1/insights route + service + Pydantic schema
- Frontend: InsightsPanel component with bar charts for operation impact,
  section impact, and keyword signal lift scores
- Insights tab added to the version panel; compact preview on doc overview
- NEXT_PUBLIC_DEMO=true makes the webapp fully standalone: loads
  DEMO_DOCUMENTS / DEMO_SUBMISSIONS / DEMO_INSIGHTS from demo-data.ts,
  disables all mutating actions, shows a DEMO badge in the top bar
- apps/webapp/public/demo-cv.docx: static dummy CV (Alex Rivera) for demo
- scripts/gen_demo_cv.py: script to regenerate the demo DOCX
- .env.example: document NEXT_PUBLIC_DEMO flag

https://claude.ai/code/session_01LWxu2qrwY6BRjUFXXn7NiM

apps/backend/fastapi/app/services/insights.py

@@ -0,0 +1,37 @@
+from __future__ import annotations
+
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy.orm import selectinload
+
+from dlib.ai.insights import InsightsResult, SubmissionRecord, SuggestionRecord, analyze
+from app.models import AiSuggestion, CvDocument, CvVersion, Submission
+
+
+async def get_insights(session: AsyncSession, *, owner_id: str) -> InsightsResult:
+    stmt = (
+        select(Submission)
+        .join(Submission.version)
+        .join(CvVersion.document)
+        .where(CvDocument.owner_id == owner_id)
+        .options(selectinload(Submission.suggestions))
+    )
+    rows = list((await session.execute(stmt)).scalars().all())
+
+    records = [
+        SubmissionRecord(
+            status=s.status.value,
+            suggestions=[
+                SuggestionRecord(
+                    operation=sug.operation,
+                    target_path=sug.target_path,
+                    proposed_text=sug.proposed_text,
+                    rationale=sug.rationale,
+                    accepted=sug.accepted,
+                )
+                for sug in s.suggestions
+            ],
+        )
+        for s in rows
+    ]
+    return analyze(records)