AI-powered SaaS platform for automated analysis of scientific publications.
Target Users: Technology Transfer Offices (TTOs), VCs, Corporate Innovation Teams
- Paper Import - DOI, OpenAlex, PubMed, arXiv, Crossref
- 6-Dimensional AI Scoring - Novelty, IP Potential, Marketability, Feasibility, Commercialization, Team Readiness
- KanBan Pipeline - Structured paper management workflow
- Semantic Search - Vector-based similarity using pgvector
| Layer | Technology |
|---|---|
| Backend | Python 3.11+, FastAPI, SQLAlchemy (async), Pydantic v2 |
| Database | PostgreSQL 16 + pgvector (HNSW index) |
| Queue | arq (async-native) + Redis |
| Storage | MinIO (S3-compatible) |
| AI/LLM | GPT-5 mini (default), text-embedding-3-small |
# 1. Setup environment
cp .env.example .env
# Edit .env and add your OPENAI_API_KEY
# 2. Install dependencies
poetry install
# 3. Start services
docker compose up -d
# 4. Run migrations
poetry run alembic upgrade head
# 5. Start server
poetry run uvicorn paper_scraper.api.main:app --reload --port 8000API Docs: http://localhost:8000/docs
| Document | Description |
|---|---|
| SETUP.md | Detailed setup guide with troubleshooting |
| CLAUDE.md | Project context for Claude Code |
| 01_TECHNISCHE_ARCHITEKTUR.md | System architecture |
| 02_USER_STORIES.md | Prioritized backlog |
| 03_CLAUDE_CODE_GUIDE.md | Development workflow |
| 04_ARCHITECTURE_DECISIONS.md | ADRs |
| 05_IMPLEMENTATION_PLAN.md | Sprint plan |
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