TenderIQ

TenderIQ automates tender discovery, analysis, and recommendations using AI—saving contractors hours by extracting key data, flagging risks, and personalizing opportunities.

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Inspiration

The traditional tendering process is broken—contractors often waste hours manually scanning 200–300 page PDFs, navigating confusing portals, and missing crucial clauses that lead to disqualification.
We saw an opportunity to use AI to eliminate inefficiencies, reduce risks, and personalize discovery, all in one system.

🛠️ How We Built It

TenderIQ is built as a modular AI-powered tender intelligence platform. Key components include:

  • Automated PDF Extraction
    An agent-based pipeline extracts structured data (dates, eligibility, risk clauses, financials) from complex tender documents using LLMs and rule-based validation.

  • AI Tender Search
    Natural language search replaces dropdowns, making the experience human-centric and intent-driven.

  • Recommendation Engine
    Personalized tender recommendations with fit score, matched criteria, and opportunity highlights based on user profile and past data.

  • Risk & Alert Flags
    An NLP engine flags legal/financial risks (e.g., penalties, arbitration clauses) with color-coded severity.

  • Chatbot Assistant
    An AI chatbot guides users through tender requirements, helping decode legal and technical jargon.

  • Multi-language Support *(In Progress)*
    Agentic language adapters extract structured data from Hindi and regional-language tenders.

What We Learned

  • Real-world documents are messy. Designing reliable extraction across diverse tender formats pushed us to adopt a hybrid LLM + rule-based pipeline
  • Personalization is not trivial. Building trust in AI recommendations meant tracking user behavior, profiles, and refining matching logic
  • Performance matters. 300-page tender PDFs required optimization at both the LLM and data pipeline level to reduce latency
  • Agents help modularize complexity. Using manager–evaluator–extractor agent roles helped scale logic and reduce coupling.

Tech Stack Highlights

  • Google Gemini for document understanding, clause analysis, and chat
  • Gemma 3 (4B) for on-device open-weight fine-tuned recommendations
  • Google ADK for building agent-based systems (tender matcher, risk engine, etc.)
  • Flutter for cross-platform mobile-first UI
  • Firebase Hosting for frontend delivery
  • Firebase Functions (Python) for backend orchestration and AI logic
  • Firebase Data Connect to integrate structured query workflows

TenderIQ isn’t just a tool—it’s a mission to empower contractors with clarity, confidence, and speed in public procurement.

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