Why I stopped trading and started tendering with AI

说明与用途

使用Python、LangChain和Pinecone的自动化招标搜索与分析工作流程，取代手工处理，快速识别相关机会。

问题

Consistent profitability in trading was elusive due to zero‑sum competition and information asymmetry.

结果

The workflow enables rapid identification of tender opportunities that match a firm’s capabilities, reducing manual effort and improving bid relevance.

工作流

• Scrape tender websites for URLs
• Download and extract text from PDFs (PDFMiner, BeautifulSoup)
• Generate embeddings with LangChain/OpenAI
• Store embeddings in Pinecone vector database
• Query database with capability description for semantic search
• Review top results via Flask/FastAPI interface

证据

Manually sifting through hundreds of pages of RFPs (Requests for Proposals), specifications, and addendums is incredibly time-consuming.

来源

I leveraged that, but shifted focus. Instead of time-series analysis, I needed Natural Language Processing (NLP) and document parsing.

来源

This is where the power of vector databases comes in. I can query the database with a description of my capabilities and find tenders that are semantically similar, even if they don't contain those exact keywords.

来源