Why I stopped trading and started tendering with AI

说明与用途

作者用 AI 系统取代交易机器人，自动化投标分析与响应，提升投标量、赢单率并降低成本。

问题

Traditional tendering is manual, time‑consuming, and error‑prone, making it infeasible for small businesses to respond to many tenders.

结果

The AI pipeline increased bid volume, improved win rate, reduced manual costs, and provided scalability for tender responses.

工作流

• Parse tender documents with OCR (PyTesseract, pdfminer.six)
• Chunk and summarize text
• Use a fine‑tuned Llama 2 7B for requirement extraction
• Embed requirements and query Pinecone for matching capabilities
• Generate compliant tender response with LLM prompts
• Cross‑check response against original requirements for compliance

证据

Traditional methods relied on manual reading and tagging - a process slow and expensive.

来源

I fine‑tuned a model (currently a quantized Llama 2 7B, deployed locally for cost and control) on a dataset of tender documents and manually labeled requirements.

来源

We can now analyze and respond to significantly more tenders.

来源