8080.AI
DesignReqsTasksUser FlowArchitecture
Log inSign up for free

clever-tender

byMr Unknown

AI-Based Tender Evaluation and Eligibility Analysis for Government Procurement by CRPF Context Government organisations such as the Central Reserve Police Force (CRPF) issue tenders to procure goods and services. Each tender specifies detailed requirements: technical specifications, financial thresholds, compliance rules, eligibility conditions, document checklists and mandatory certifications. These requirements are typically written in formal, legally careful language and are spread across many pages of the tender document. Private companies respond with bids, each submitting their own set of supporting documents — company profiles, financial statements, experience letters, tax registrations, certifications and more. The documents arrive in many formats: structured text PDFs, scanned copies, Word files, tables and even photographs of physical certificates. The same kind of information is presented in many different ways across bidders. Evaluating whether each bidder meets the stated eligibility criteria is currently a manual process. It is slow, inconsistent across evaluators, prone to oversight, and hard to audit. For a single tender, a committee may spend days cross-checking hundreds of pages against a list of criteria, and two evaluators may reach different conclusions from the same set of documents. There is a clear opportunity to bring modern AI techniques to this problem — to extract structured information from unstructured tender and bid documents, apply the eligibility rules consistently, and produce explainable evaluation reports that a human officer can trust and sign off on. The Problem Design a technical platform that, given a tender document and a set of bidder submissions, can do the following:Understand the tender Extract the eligibility criteria from the tender document — technical specifications, financial thresholds, compliance conditions, and document and certification requirements. Distinguish between mandatory and optional criteria. Capture each criterion in a form that can be matched against a bidder's submission. Understand each bidder Parse every bidder submission, regardless of whether the documents are typed PDFs, scanned copies, Word files or photographs. Extract the values and evidence relevant to each criterion from those documents. Handle variation in how bidders present the same information. Evaluate and explain For each bidder, decide whether they are Eligible, Not Eligible, or Need Manual Review against each criterion and overall. Produce an explanation for every verdict that references the specific criterion, the specific document and the specific value that drove the decision. Surface ambiguous or uncertain cases for human review rather than silently disqualifying them. Produce a consolidated evaluation report that a procurement officer can use as the basis for a decision. Non-Negotiables Every verdict must be explainable at the criterion level — which criterion was being checked, which document was used, what value was found, and why the bidder passed, failed or needs review. The system must never silently disqualify a bidder. Ambiguous or uncertain cases must be surfaced for human review with the reason. The system must handle scanned documents and photographs, not only digital text. The system must be auditable end-to-end and suitable for use in a formal government procurement decision. Real tender and bid data will not be released for Round 1. Any Round 2 implementation will run on representative mock or redacted documents inside a sandbox. What Success Looks Like A working solution should eventually make the following behaviours possible: A procurement officer uploads a tender document and a set of bidder submissions. The system extracts the eligibility criteria automatically and lists them for review. For each bidder, the system produces a criterion-by-criterion evaluation with references back to the source documents. Clearly eligible and clearly ineligible bidders are marked as such; genuinely ambiguous cases are flagged for manual review with the reason for the ambiguity. A consolidated report can be exported and signed off, with a complete audit trail of every automated decision. Sample Scenario To help you visualise the problem, consider a representative scenario: A government department issues a tender for construction services with the following eligibility criteria: a minimum annual turnover of ₹5 crore, at least 3 similar projects completed in the last 5 years, a valid GST registration, and an ISO 9001 certification. Ten bidders submit responses, each with their own combination of typed and scanned supporting documents. A good solution would extract these four criteria from the tender, parse each bidder's submission, and produce a report. For example: 6 bidders clearly eligible with evidence for each criterion, 3 clearly ineligible with the specific criterion they failed and the document that showed it, and 1 flagged for manual review because the turnover document is a scanned certificate with figures that could not be read with confidence. What Your Solution Should Cover Round 1 of this hackathon is a written solution submission. Your solution document should make clear how you would build this platform. At minimum, it should cover: Your understanding of the problem and the realities of government procurement, in your own words. Your approach to extracting eligibility criteria from a tender document, including how you separate technical, financial and compliance conditions, and how you distinguish mandatory from optional criteria. Your approach to parsing bidder submissions with heterogeneous document types — typed PDFs, scanned documents, tables, photographs — and extracting the values that map to each criterion. How you match extracted bidder information against the criteria, and how you handle ambiguity, partial information and variation in legal and technical language. How the system produces explainable, criterion-level verdicts, and how ambiguous cases are surfaced for human review instead of being silently rejected. How you would guarantee the auditability of every decision, suitable for a formal government procurement context. A clear architecture overview, the key technology and model choices you would make, and the reasons behind them. The main risks and trade-offs you see, and how you would handle them. A rough implementation plan for Round 2, assuming a sandbox with sample tender and bidder documents is provided. How We Will Evaluate Proposals Clarity of problem understanding — does the team show they have grasped the realities of government procurement, not just the surface problem? Technical soundness of the proposed approach, including document understanding, criterion matching and explainability. Depth of thinking on edge cases: scanned documents, photographs, ambiguous language, partial information and format inconsistency. Design of the human-in-the-loop path for ambiguous cases, and of the audit trail. Quality of the architecture, the justification of technology and model choices, and the identified risks and trade-offs. for ai for bharat hackathon

Landing
Landing

Comments (0)

No comments yet. Be the first!

System Requirements

System Requirement Document
Page 1 of 7

System Requirements Document (SRD)

Clever-Tender: AI-Based Tender Evaluation and Eligibility Analysis for Government Procurement by CRPF

1. Introduction

The Clever-Tender project aims to revolutionize the tender evaluation process for government procurement, specifically for organizations like the Central Reserve Police Force (CRPF) in India. By leveraging artificial intelligence, the system will automate the extraction and evaluation of eligibility criteria from tender documents and bidder submissions. This will ensure consistency, transparency, and efficiency in procurement decisions while maintaining compliance with formal government standards.

The solution will address challenges such as processing unstructured data, handling scanned documents, and providing explainable, auditable decisions. It will also incorporate a human-in-the-loop mechanism for ambiguous cases, ensuring no bidder is unfairly disqualified.

Page 2 of 7

2. System Overview

The Clever-Tender platform will:

  • Extract eligibility criteria from tender documents, distinguishing between mandatory and optional requirements.
  • Parse and normalize bidder submissions, regardless of format (PDFs, images, Word files, etc.).
  • Evaluate each bidder against the criteria, producing explainable verdicts ("Eligible," "Not Eligible," or "Needs Manual Review").
  • Provide a human-in-the-loop interface for ambiguous cases.
  • Generate consolidated, audit-ready evaluation reports.

The system will be designed to handle the complexities of government procurement in India, including compliance with legal and procedural standards. It will support multiple file formats, ensure data security, and provide a seamless user experience for procurement officers and reviewers.

3. Functional Requirements

  • As a Procurement Officer, I should be able to upload tender documents and bidder submissions in various formats (PDF, Word, images).
  • As a System, I should extract eligibility criteria from tender documents and categorize them as technical, financial, compliance, or document-based.
  • As a System, I should distinguish between mandatory and optional criteria.
  • As a System, I should parse bidder submissions and extract relevant values (e.g., turnover, certifications).
  • As a System, I should evaluate each bidder against the criteria and provide a verdict ("Eligible," "Not Eligible," or "Needs Manual Review").
  • As a System, I should generate an explanation for each verdict, referencing the specific criterion, document, and value used.
  • As a System, I should flag ambiguous cases for manual review and provide a confidence score for each extraction.
  • As a Reviewer, I should be able to review flagged cases, override decisions, and add remarks.
  • As a Procurement Officer, I should be able to download a consolidated evaluation report with an audit trail of all decisions.

4. User Personas

Page 3 of 7

1. Procurement Officer

  • Role: Oversees the tender evaluation process.
  • Responsibilities: Uploads documents, reviews extracted criteria, and finalizes evaluation reports.
  • Needs: A user-friendly interface for managing tenders and bidder evaluations.

2. Reviewer

  • Role: Handles flagged cases requiring manual review.
  • Responsibilities: Validates ambiguous cases, overrides decisions, and adds remarks.
  • Needs: A dashboard for reviewing flagged cases with detailed explanations and evidence.

3. System Administrator

  • Role: Manages system configurations and user access.
  • Responsibilities: Ensures data security, monitors system performance, and resolves technical issues.
  • Needs: Administrative tools for managing users and system settings.

5. Visuals Colors and Theme

Page 4 of 7

Color Palette

  • Primary: #1E4D92 (Deep Navy Blue) – Represents trust and professionalism.
  • Primary Light: #4A73C2 (Sky Blue) – For hover states and secondary UI elements.
  • Secondary: #E67E22 (Amber Orange) – Highlights headlines, links, and emphasis areas.
  • Accent: #2ECC71 (Emerald Green) – Used for CTAs, badges, and active states.
  • Highlight: #F39C12 (Golden Yellow) – For hover states, notifications, and active indicators.
  • Background (bg): #F7F9FC (Light Grayish Blue) – For page backgrounds.
  • Surface: rgba(255, 255, 255, 0.9) (White with slight transparency) – For cards and panels.
  • Text: #2C3E50 (Dark Slate Gray) – For primary text and headings.
  • Text Muted: #95A5A6 (Muted Gray) – For secondary text, labels, and timestamps.
  • Border: rgba(44, 62, 80, 0.2) – Subtle border color for UI elements.

6. Signature Design Concept

Page 5 of 7

Interactive Blueprint Interface

The Clever-Tender homepage will feature an interactive blueprint design that visually represents the tender evaluation workflow. Users will see a dynamic, scrollable blueprint of a government office, with each section representing a key feature of the platform (e.g., document upload, criteria extraction, bidder evaluation, manual review).

  • Interaction:
    • Hovering over a section will display a tooltip with a brief description.
    • Clicking on a section will zoom into a detailed view with animations (e.g., documents being parsed, criteria being matched).
    • Users can drag and pan across the blueprint to explore different features.
  • Animations:
    • Smooth transitions between sections using GSAP ScrollTrigger.
    • Micro-interactions for buttons and icons using Framer Motion.
  • Color Shifts:
    • The blueprint will subtly shift colors based on user actions, creating a dynamic and engaging experience.

This design will make the platform visually appealing and intuitive, while also reinforcing the structured, methodical nature of the tender evaluation process.

Page 6 of 7

7. Interaction Model & Motion Direction

  • Landing Page: Parallax interaction with layered depth effects. Decorative elements (e.g., blueprint lines, icons) will move at different speeds during scroll.
  • Internal Pages: Animated interaction model with hover transitions, spring physics for interactive elements, and scroll-triggered reveals.
  • Distinctive Section Mechanics:
    • Document upload: Drag-and-drop animation with real-time progress indicators.
    • Criteria extraction: Morphing SVGs to represent extracted rules.
    • Bidder evaluation: Cards that flip and stack dynamically.

8. Non-Functional Requirements

  • Performance: The system must process a tender document and 10 bidder submissions within 5 minutes.
  • Scalability: Support up to 100 concurrent users.
  • Security: Ensure data encryption during upload, storage, and processing.
  • Auditability: Maintain a complete log of all actions and decisions.
  • Accessibility: Comply with WCAG 2.1 Level AA standards.

9. Tech Stack

Frontend

  • React.js with Material UI for responsive, interactive interfaces.
Page 7 of 7

Backend

  • Python with FastAPI for API development.
  • Tesseract or Google Vision API for OCR.
  • spaCy and Hugging Face transformers for NLP.

Database

  • PostgreSQL for structured data storage.

Deployment

  • Docker and Kubernetes for containerization and orchestration.

10. Assumptions and Constraints

  • Real tender and bidder data will not be available for initial development; mock data will be used.
  • The system will operate in a sandbox environment for testing and validation.
  • Government-mandated platforms or data centers may impose additional constraints.

11. Glossary

  • OCR: Optical Character Recognition, used to extract text from scanned documents.
  • NLP: Natural Language Processing, used to analyze and extract information from text.
  • GSAP: GreenSock Animation Platform, a library for creating animations.
  • WCAG: Web Content Accessibility Guidelines, standards for making web content accessible.

This document provides a comprehensive roadmap for the Clever-Tender project. Let me know if you'd like to refine or expand any section further!

Landing design preview
Landing: View Features
Login: Sign In
Dashboard: View Overview
Tender: Upload Document
Criteria: Review Extracted
Criteria: Edit Criteria
Bidders: Upload Submissions
Evaluation: View Results
Evaluation: Review Verdicts
Reports: Download Report
Reports: View Audit Trail