The EU is set to invest âŹ1 trillion+ in infrastructure by 2030 (rail, energy grids, e-mobility, broadband). In Germany alone public tenders total âŹ250 billion/year, yet companies still hunt across a dozen portals, manually open PDFs, and sort hundreds of irrelevant notices. Existing aggregator tools simply keyword-matchâthey donât really âunderstandâ what you can build.
Our SaaS changes that by adding AI to every step:
1. Smart Crawling & Ingestion (V1)
* Start with 2â3 key portals (service.bund.de, TED.europa.eu, VDV-Portal)
* Scheduled Python pipeline downloads new tenders and attachments (PDF/DOCX/XLSX)
2. Automated Document Parsing
* Extract text and tables using pdfminer.six, python-docx and pandas.read\_excel()
* Normalize metadata: Tender ID, issuing authority, deadlines, CPV codes
3. Basic AI Matching Engine
* Rule-based filters on sector (rail, energy, e-mobility, digital networks)
* Fine-tuned DistilBERT model to tag scope, award criteria and estimated budget
* Company profile embeddings (keywords, past projects, certifications)
* Compute relevance score by combining cosine similarity and budget threshold
4. User-Friendly Output
* One-line summary, e.g. âUpgrade 20 km tram line in Munich, âŹ12 M, bids due 15 Sept.â
* Map view of project locations (Leaflet)
* Email or in-app alerts when score meets userâs threshold
Why This Is the Ideal SaaS:
* Large, growing market: âŹ250 B per year in Germany, âŹ1 T+ in the EU
* Low competition on true AI: existing tools only do keyword matches, not real NLP
* High willingness to pay: saves 80% of manual research time and uncovers 30â50% more relevant bids
* Scalable: adding new portals or sectors takes days, not months
What We Need:
* Technical co-founder to build and maintain:
âą Scraping pipelines and scheduler (Python, Airflow or Cron)
âą Document parsing and OCR (pdfminer, Tesseract, python-docx, pandas)
âą NLP/ML matching engine (PyTorch or TensorFlow, BERT-style models)
âą Cloud infrastructure and DevOps (Docker/Kubernetes or serverless on AWS/GCP)...
