- Added FastAPI backend with FFmpeg and Gemini 2.0 integration
- Added React frontend with upload and meeting list
- Integrated into main docker-compose stack and dashboard
- Restored missing method implementations in ClassificationService (classify, extract_metrics)
- Fixed Standardization Logic not being applied in metric cascade
- Bumped version to v0.7.4 in config.py
- Removed duplicate API endpoint in app.py
- Updated MIGRATION_PLAN.md
- **Standardization & Formula Logic:** Fixed NameError/SyntaxError in formula parser; added support for comments and capitalized placeholders.
- **Source URL Tracking:** Extended DB schema and cascade logic to store and track specific source URLs.
- **Frontend & UI:**
- Added 'Standardized Potential' display in Inspector.
- Added clickable source link with icon.
- Fixed Settings tab layout collapse (flex-shrink-0).
- **Export Capabilities:**
- Single-company JSON export now includes full quantitative metadata.
- New global CSV export endpoint /api/companies/export.
- **System Integrity:**
- Fixed Notion sync typo ('Stanardization').
- Corrected Nginx proxy routing and FastAPI route ordering.
- Ensured DB persistence via explicit docker-compose volume mapping.
- Implemented a "Re-evaluate Wikipedia" button in the UI.
- Added a backend endpoint to trigger targeted Wikipedia metric extraction.
- Hardened the LLM metric extraction prompt to prevent hallucinations.
- Corrected several database path errors that caused data loss.
- Updated application version to 0.6.4 and documented the ongoing issue.
- Implements a 3-tier database architecture (Canonical Products, Portfolio, Companies) to separate product master data from company-specific portfolio information.
- Upgrades import_competitive_radar.py to an intelligent "upsert" script that prevents duplicates by checking for existing entries before importing.
- This enables detailed GTM strategy tracking for RoboPlanet products while monitoring competitor portfolios.
- Updates documentation to reflect the new architecture and import process.
- Fixed a critical in the company-explorer by forcing a database re-initialization with a new file (). This ensures the application code is in sync with the database schema.
- Documented the schema mismatch incident and its resolution in MIGRATION_PLAN.md.
- Restored and enhanced BUILDER_APPS_MIGRATION.md by recovering extensive, valuable content from the git history that was accidentally deleted. The guide now again includes detailed troubleshooting steps and code templates for common migration pitfalls.
Refactors the GTM orchestrator prompts (phases 2-9) to use a question-based strategic framework derived from the internal marketing blueprint. This new 'Meta-Framework' approach ensures strategic depth and prevents content pollution from irrelevant examples when analyzing new product categories.
- Updates orchestrator prompts in .
- Adds documentation in explaining how to modify the new strategy logic.
- Includes minor fixes to the Node.js and dependency updates in .
- Implemented semantic classification for Products (e.g. 'Cleaning', 'Logistics') and Battlecards (e.g. 'Price', 'Support').
- Created 'import_competitive_radar.py' for full 4-database relational import to Notion.
- Updated Orchestrator with new prompts for structured output.
- Cleaned up obsolete scripts.
- Extended import_relational_radar.py to include a 'Products' database.
- Implemented full dual-way relations for Companies <-> Landmines, References, Products.
- Updated documentation to reflect the 4-database architecture.
- Added import_relational_radar.py for bidirectional database structure in Notion.
- Added refresh_references.py to populate analysis data with grounded facts via scraping.
- Updated documentation for Competitive Radar v2.0.
Improves the competitor reference analysis (Step 8) by replacing the previous LLM-only approach with a grounded, scraping-based method.
- Implemented a new scraper to actively search for and parse competitor reference/case study pages.
- The analysis is now based on actual website content, significantly increasing the accuracy and reliability of the results and preventing model hallucinations.
- Updated documentation to reflect the new 'Grounded References' architecture.
