Small contractors shouldn't have to spend hours decoding government tender documents just to discover they're missing a critical requirement.
That's the problem I'm trying to solve with TenderTrap, an AI-powered tender intelligence tool that helps contractors evaluate bid opportunities before investing time and resources in preparation.
What I Built
Government procurement can create real opportunities for small contractors and MSMEs, but tender documents are often lengthy, technical, and difficult to evaluate. Eligibility conditions, turnover requirements, registrations, EMD, performance guarantees, and contractual clauses can all affect a bidding decision.
TenderTrap converts tender PDFs into structured, evidence-backed assessments.
Here's what it does:
- Extracts requirements: Identifies relevant criteria from tender documents.
- Classifies documents: Distinguishes individual tender notices from circulars, contract conditions, amendments, and mixed compilations.
- Traces findings to sources: Links extracted requirements to document pages and clauses.
- Checks company evidence: Compares requirements with available company information and identifies missing or uncertain evidence.
- Supports bid/no-bid decisions: Produces one of three recommendations — PROCEED, INVESTIGATE, or SKIP.
- Keeps uncertainty visible: Uses MET, UNMET, and UNKNOWN statuses, with human-review flags when evidence is incomplete or ambiguous.
The goal isn't to replace a contractor's judgment. It's to help them make a more informed decision before committing time to a bid.
Who would pay for it?
Small construction contractors, MSMEs, and independent tender consultants who regularly evaluate government procurement opportunities.
The business hypothesis is that a tool that reduces repetitive document review and highlights potential blockers could save enough time to justify a subscription. Pricing and willingness to pay still need validation with actual users.
Demo
- Live application: https://tendertrap.onrender.com
The current prototype demonstrates PDF analysis, document classification, requirement extraction, source citations, evidence matching, and review workflows.
Known limitation: Scanned PDFs are subject to OCR processing limits. In testing, a 157-page scanned compilation was processed partially, with unprocessed pages explicitly reported. The prototype does not guarantee eligibility, contract awards, or legal compliance.
Code
- GitHub repository: https://github.com/ankit9241/TenderTrap
The project uses a single codebase built with TanStack Start, React 19, TypeScript, and a Node.js/Nitro server.
How I Built It
TenderTrap combines open-weight AI with deterministic business logic rather than asking an LLM to make an unverified final decision.
Core technologies:
- Gemma 3 + Ollama: Open-weight model inference for structured analysis of tender documents in the local development setup.
- unpdf: Native PDF text extraction.
- Tesseract.js: OCR for scanned documents.
- TanStack Start, React 19, TypeScript: Application UI, routing, and server endpoints.
- MongoDB driver: Database integration for company and assessment data.
- Deterministic matching and decision rules: Compare extracted criteria with available company evidence and generate cautious recommendations.
One of the biggest challenges was preventing plausible-looking but unsupported results.
During testing with a 157-page scanned compilation, the system initially mixed contractual durations with bid deadlines and treated a compilation as though it were a single tender. I improved the pipeline to classify document types, identify sections, separate bid deadlines from contractual durations, validate source citations, and disclose incomplete OCR coverage.
The current automated test suite reports 102 passing tests, and the production build succeeds.
These results validate the tested code paths, not universal accuracy across all tender documents. Production cloud persistence and multi-tenant security still require further verification.
Why Does Open Innovation Matter?
TenderTrap's AI analysis uses Gemma, an open-weight model, through Ollama in local development.
This gives me control over the inference setup and makes it possible to experiment with a specialised document-analysis workflow without relying entirely on a closed, hosted AI API.
Open-source extraction and OCR libraries provide the document-processing foundation, while the matching and decision logic remains inspectable and testable.
For a product aimed at small contractors, infrastructure cost, explainability, and control over the processing pipeline matter. Open innovation makes it easier to experiment with models, change the workflow, and improve the product without locking every part of the system to a single proprietary AI provider.
The longer-term goal is to make tender evaluation more accessible to smaller businesses that cannot afford extensive manual procurement support.
Prize Categories
- Render — Best Use of Render: TenderTrap is built for deployment as a full-stack web application on Render, bringing the tender analysis workflow into one accessible platform.
- Gemma — Best Use of Gemma: Uses the open-weight Gemma 3 model through Ollama in the tender analysis pipeline to support document understanding and structured extraction.
- MongoDB Atlas — Best Use of MongoDB Atlas: Include this category if the submitted implementation uses MongoDB Atlas as its actual data layer.
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