feat(company-explorer): bump version to 0.3.0, add VAT ID extraction, and fix deep-link scraping

- Updated version to v0.3.0 (UI & Backend) to clear potential caching confusion.
- Enhanced Impressum scraper to extract VAT ID (Umsatzsteuer-ID).
- Implemented 2-Hop scraping strategy: Looks for 'Kontakt' page if Impressum isn't on the start page.
- Added VAT ID display to the Legal Data block in Inspector.

GEMINI.md

@@ -37,14 +37,32 @@ The system is modular and consists of the following key components:
     *   **Google-First Discovery:** Uses SerpAPI to find the correct Wikipedia article, validating via domain match and city.
     *   **Visual Inspector:** The frontend `Inspector` now displays a comprehensive Wikipedia profile including category tags.
 
+*   **Web Scraping & Legal Data (v2.2):**
+    *   **Impressum Scraping:** Implemented a robust finder for "Impressum" / "Legal Notice" links.
+        *   **Root-URL Fallback:** If deep links (e.g., from `/about-us`) don't work, the scraper automatically checks the root domain (`example.com/impressum`).
+        *   **LLM Extraction:** Uses Gemini to parse unstructured Impressum text into structured JSON (Legal Name, Address, CEO).
+    *   **Clean JSON Parsing:** Implemented `clean_json_response` to handle AI responses containing Markdown (` ```json `), preventing crash loops.
+
 *   **Manual Overrides & Control:**
     *   **Wikipedia Override:** Added a UI to manually correct the Wikipedia URL. This triggers a re-scan and **locks** the record (`is_locked` flag) to prevent auto-overwrite.
     *   **Website Override:** Added a UI to manually correct the company website. This automatically clears old scraping data to force a fresh analysis on the next run.
 
-*   **Architecture & DB:**
-    *   **Database:** Updated `companies_v3_final.db` schema to include `RoboticsCategory` and `EnrichmentData.is_locked`.
-    *   **Services:** Refactored `ClassificationService` and `DiscoveryService` for better modularity and robustness.
+## Lessons Learned & Best Practices
+
+1.  **Numeric Extraction (German Locale):**
+    *   **Problem:** "1.005 Mitarbeiter" was extracted as "1" (treating dot as decimal).
+    *   **Solution:** Implemented context-aware logic. If a number has a dot followed by exactly 3 digits (and no comma), it is treated as a thousands separator. For Revenue (`is_umsatz=True`), dots are generally treated as decimals (e.g. "375.6 Mio") unless multiple dots exist.
+    *   **Rule:** Always check for both `,` and `.` presence to determine locale.
+
+2.  **LLM JSON Stability:**
+    *   **Problem:** LLMs often wrap JSON in Markdown blocks, causing `json.loads()` to fail.
+    *   **Solution:** ALWAYS use a `clean_json_response` helper that strips ` ```json ` markers before parsing. Never trust raw LLM output for structured data.
+
+3.  **Scraping Navigation:**
+    *   **Problem:** Searching for "Impressum" only on the *scraped* URL (which might be a subpage found via Google) often fails.
+    *   **Solution:** Always implement a fallback to the **Root Domain**. The legal notice is almost always linked from the homepage footer.
 
 ## Next Steps
+*   **Frontend Debugging:** Verify why the "Official Legal Data" block disappears in some states (likely due to conditional rendering checks on `impressum` object structure).
 *   **Quality Assurance:** Implement a dedicated "Review Mode" to validate high-potential leads.
-*   **Data Import:** Finalize the "List Matcher" to import and deduplicate Excel lists against the new DB.
+*   **Data Import:** Finalize the "List Matcher" to import and deduplicate Excel lists against the new DB.