OpenAI 2026 hackathon

RotaBot

RotaBot uses AI and optimisation to automatically generate the most efficient staff rotas, reducing labour costs while balancing demand, contracts, availability, and legal requirements.

Solo project by bq2qbdymgq-spec Langmead · 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 #6,464 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

RotaBot is a self-reported scheduling tool built as a hackathon project by one founder. The description states it uses AI and mathematical optimisation to automate staff rotas in hospitality, aiming to reduce labour costs while balancing demand, availability, contracts, and legal requirements. It was submitted to the OpenAI 2026 hackathon.

The author claims RotaBot is now used at their workplace for live rota generation, replacing manual scheduling. It integrates with a mobile app and uses Google OR-Tools, Python, React, Supabase, and SwiftUI.

Key open question: Is there evidence of traction or commercial adoption beyond the founder's own workplace?

This analysis is based entirely on self-reported information from the project description. No independent verification or data on revenue, customers, usage, or market validation is available.

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What The Product Actually Is

The description states that RotaBot:

  • Uses AI and mathematical optimisation to generate staff rotas
  • Balances employee availability, contracted hours, labour demand, skills, business rules, breaks, and legal constraints
  • Produces legally compliant, demand-driven schedules in minutes
  • Replaces manual rota creation for day-to-day scheduling

It is described as a system that:

  • Has an optimisation engine powered by Google OR-Tools (CP-SAT)
  • Includes a Python backend exposing a REST API
  • Features a frontend built with Lovable and React
  • Stores data in Supabase
  • Deploys on Render and Vercel
  • Includes a mobile app built using SwiftUI

Inference: The product appears to be a scheduling platform designed for workforce optimisation, likely targeting small-to-medium businesses in hospitality or similar sectors.

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Positioning & Claim Evolution

The author states:

  • RotaBot was inspired by the founder's experience as a General Manager manually creating rotas
  • It aims to reduce labour costs and eliminate overstaffing/understaffing issues
  • The tool is described as replacing manual rota creation with automated, efficient scheduling

Inference: The positioning evolved from solving a personal pain point (manual scheduling) into a product that claims to automate workforce optimisation for businesses.

There is no evidence of prior versions or iterative development beyond the hackathon submission. No mention of market feedback, customer interviews, or competitive positioning beyond the author’s own experience.

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Target Customer & ICP

The description states:

  • RotaBot was built for use in hospitality
  • It targets businesses seeking to reduce labour costs and improve scheduling efficiency
  • The founder's workplace is cited as a current user

Inference: The initial target customer appears to be small-to-medium businesses in hospitality, where manual scheduling is common.

There is no evidence of:

  • Specific customer segments beyond hospitality
  • Market research or customer validation
  • Targeted go-to-market strategy or ICP definition beyond the founder’s own use case

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Business Model & Pricing Evidence

The description does not state:

  • How RotaBot generates revenue
  • Whether it is sold as a SaaS product, freemium, or other model
  • Any pricing information or monetisation strategy

Inference: No business model or pricing evidence is provided. The project appears to be in early development and lacks commercial traction.

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Technical & Delivery Signals

The description states:

  • Built with Google OR-Tools (CP-SAT) for optimisation
  • Python backend exposing a REST API
  • Frontend built with Lovable and React
  • Data stored in Supabase
  • Deployments on Render and Vercel
  • Mobile app built using SwiftUI

Inference: The technical stack suggests a modern, scalable architecture with cloud infrastructure and mobile support. However, no evidence of production deployment or performance data is provided.

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Traction & Maturity Signals

The author states:

  • RotaBot is now used to generate live staff rotas at their workplace
  • It consistently matches staffing demand and ensures employees reach contracted hours
  • It has replaced manual rota creation for day-to-day scheduling

Inference: There is limited evidence of traction beyond a single use case. No data on:

  • Number of users or businesses using the tool
  • Customer retention or feedback
  • Product usage metrics or adoption rates

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Competitive Context

The description does not mention:

  • Competitors in the workforce scheduling space
  • Market size or competitive landscape
  • Differentiation from existing tools

Inference: No evidence of competitive analysis or awareness of existing solutions. The project appears to be self-contained and unvalidated in a broader market context.

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Key Risks & Red Flags

Key risks and red flags based on the description:

  • Single-founder project: Only one team member is listed, raising questions about scalability and execution
  • No commercial traction: No evidence of revenue, customers, or adoption beyond the founder’s own workplace
  • Unverified claims: All statements are self-reported without independent verification
  • Limited market validation: No evidence of customer interviews, feedback, or product-market fit
  • Hackathon origin: The project was submitted to a hackathon, suggesting early-stage development

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Diligence Questions To Ask The Founders

  1. What specific constraints or rules does RotaBot currently support? How flexible is the constraint engine?
  2. Are there any known limitations in performance or scalability with larger datasets or more complex scheduling problems?
  3. Has the tool been tested across different industries or business sizes beyond hospitality?
  4. What are the current plans for monetisation and go-to-market strategy?
  5. How does RotaBot handle edge cases or unexpected scheduling requirements?
  6. Is there any feedback from users outside of your own workplace?

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Investment/Partnership Verdict

Not evidenced: No evidence is provided to support a commercial due-diligence read beyond the founder’s own account.

The project is described as a hackathon submission with limited real-world usage and no verified traction, revenue, or customer base. The author's claims are self-reported and unverified.

Confidence level: Low. The analysis is based entirely on self-reporting with no external corroboration or data to support commercial viability or scalability.

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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.