OpenAI 2026 hackathon

TenderGraph

An agentic tender operating system that helps bidder teams find the right opportunities, prepare compliant bids, track every change, and keep final submission under human control.

Solo project by Daslav Ríos · 0 likes · 0 comments

Archive position — measured, not model output

0 likes on Devpost

2,264 of the 7,856 archived projects have more likes, and 5,592 share exactly 0 — so this project's #7,188 place in the like-ranked listing is a tie-break inside that group, not a ranking.

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Likes on Devpost. ▲ marks this project's group.

Show the figures
LikesProjectsShare of archive
05,59271.2%
11,75822.4%
22853.6%
3–41321.7%
5–9751.0%
10+140.2%
Devpost like counts for all 7,856 archived projects, captured when this archive was built.

Executive Summary

What the company appears to be

TenderGraph is described as an "agentic tender operating system" built for bid teams. The author states it helps with finding opportunities, preparing compliant bids, tracking changes, and maintaining human control over final submissions.

What changed

This project was submitted to the OpenAI 2026 hackathon, suggesting a recent development or prototype phase. No evidence of prior traction, revenue, or customer adoption is provided.

Single most important open question

Is there any evidence that TenderGraph has moved beyond a hackathon prototype into actual use by bidder teams? The description provides no indication of product-market fit, real users, or commercial viability.

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What The Product Actually Is

The description states: "An agentic tender operating system that helps bidder teams find the right opportunities, prepare compliant bids, track every change, and keep final submission under human control."

  • Product nature: A software platform described as an “operating system” for managing public procurement bidding processes.
  • Functionality claimed:
    • Finding relevant tender opportunities
    • Preparing compliant bids
    • Tracking changes in bid documents
    • Maintaining human oversight of final submissions

Not evidenced The actual technical architecture, user interface, or core features beyond this high-level description. No screenshots, demos, or functional specifications are provided.

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Positioning & Claim Evolution

The author positions TenderGraph as an “agentic” system — implying automation or AI-assisted workflows within the tender process.

  • Key positioning element: It is framed as a tool for "bidder teams" rather than individual bidders.
  • Evolutionary claim: The term “operating system” suggests a comprehensive platform, not just one feature or tool.
  • Agent-based functionality: Implies AI or automated assistance in parts of the bidding lifecycle.

Not evidenced No evidence of prior versions, roadmap, or how this differs from existing tools. The description does not indicate whether this is a new concept or an evolution of something already available.

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Target Customer & ICP

The author states: “helps bidder teams find the right opportunities, prepare compliant bids, track every change, and keep final submission under human control.”

  • Target customer: Bidder teams — likely those involved in public procurement or government contracting.
  • ICP (Ideal Customer Profile): Teams that:
    • Regularly engage with tender processes
    • Require compliance tracking
    • Need to manage multiple bid submissions

Not evidenced No evidence of specific industries, company sizes, or geographic focus. No mention of existing customers or use cases.

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Business Model & Pricing Evidence

The description does not contain any information about pricing models, monetization strategies, or business model assumptions.

  • Business model: Not stated.
  • Pricing evidence: None provided.

Not evidenced No indication of whether TenderGraph is intended for subscription, one-time purchase, freemium, or other structures. No mention of revenue streams or customer acquisition costs.

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Technical & Delivery Signals

The author lists technologies used:

  • Built with: cheerio, codex, ffmpeg, github, lucide, mammoth.js, nextjs, node.js, openai, pdf.js, pipertts, playwright, react, typescript, vercel, zod
  • Tech stack: Indicates a full-stack web application using React/Next.js frontend, Node.js backend, and integration with OpenAI APIs.
  • Delivery signals: The project was submitted to a hackathon, suggesting early-stage development or prototype status.

Not evidenced No evidence of deployment, scalability, performance metrics, or production readiness. No mention of infrastructure, data handling, or security practices.

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Traction & Maturity Signals

The description provides no traction indicators:

  • No revenue figures
  • No customer base
  • No usage statistics
  • No product adoption data

Not evidenced The project is described as a hackathon submission, implying it may be in early development. There is no evidence of market validation or user feedback.

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Competitive Context

The description does not mention competitors or the broader marketplace for tender management tools.

  • Competitive landscape: Not described.
  • Differentiation claims: Not made.

Not evidenced No indication of existing solutions in this space, nor how TenderGraph would compare to them. No evidence of competitive advantage or market positioning.

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Key Risks & Red Flags

  • Prototype risk: Submitted to a hackathon; likely not yet mature for commercial use.
  • Unproven market demand: No evidence of traction, customers, or validated need.
  • Unclear monetization path: No business model or pricing structure described.
  • Limited team size: Only one member listed (Daslav Ríos), raising questions about execution capacity.

Not evidenced No evidence of product-market fit, scalability, or long-term viability. The lack of any commercial or user-facing detail is a major red flag.

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Diligence Questions To Ask The Founders

  1. What specific tender processes does TenderGraph aim to automate or assist with?
  2. How does it ensure compliance in bid preparation?
  3. Has there been any real-world testing or feedback from bidder teams?
  4. What is the intended business model and monetization strategy?
  5. Is there a plan for scaling beyond the current prototype?
  6. What are the key technical challenges in moving from prototype to production?

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Investment/Partnership Verdict

Not evidenced No basis for assessing investment or partnership potential.

  • Investment viability: Not demonstrated.
  • Partnership opportunity: Not evident.
  • Commercial readiness: Not shown.

The description is limited to a hackathon submission with no evidence of traction, customers, or commercial viability. Any future value would depend on significant development beyond this initial stage.

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Source

Submitted to the OpenAI 2026 hackathon on Devpost. Project home on DevPost.

The analysis above was generated by a language model from the project's own one-line description. It is not independent research and contains no verified traction, revenue or customer data.