OpenAI 2026 hackathon

SchemaLab: AI for electrical and ELV design

AI-assisted for electrical and ELV design coordination: turn room requirements into circuits, panel schedules, diagnostics, and technology-system reports.

Solo project by mburukabai Mburu · 0 likes · 0 comments

View the project on Devpost

Archive position — measured, not model output

0 likes on Devpost

2,263 of the 7,850 archived projects have more likes, and 5,587 share exactly 0 — so this project's #6,557 place in the like-ranked listing is a tie-break inside that group, not a ranking.

Projects (log scale)

1
10
100
1k
10k
05,587
11,758
2284
3–4132
5–975
10+14

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

Show the figures
LikesProjectsShare of archive
05,58771.2%
11,75822.4%
22843.6%
3–41321.7%
5–9751.0%
10+140.2%
Devpost like counts for all 7,850 archived projects, captured when this archive was built.

User Stories BotA demo video on every pull request

Get a demo video on every new feature

GitHub App that watches your pull requests and records a narrated, captioned demo.
Save time on demo prep and make informed decisions faster with a video walkthrough of every new feature.

Try User Stories Bot FREE (opens in a new tab)

Executive Summary

What the company appears to be

SchemaLab is an AI-assisted tool for electrical and ELV (Electronic Low Voltage) design coordination. It claims to automate tasks such as turning room requirements into circuits, panel schedules, diagnostics, and technology-system reports.

What changed

The project was submitted to the OpenAI 2026 hackathon, indicating it is in an early-stage development or prototype phase.

Single most important open question

Is there evidence of any traction, revenue, customers, or adoption beyond the hackathon submission?

Back to contents

What The Product Actually Is

The description states that SchemaLab is "AI-assisted for electrical and ELV design coordination". It claims to turn room requirements into circuits, panel schedules, diagnostics, and technology-system reports.

Evidence

  • The author describes the product as AI-assisted for electrical and ELV design coordination.
  • It is said to convert room requirements into circuits, panel schedules, diagnostics, and technology-system reports.

Inference (not evidenced)

  • The tool likely involves some form of AI or LLM integration, given the use of terms like "AI-assisted" and references to GPT-5.

Back to contents

Positioning & Claim Evolution

The author states that SchemaLab is for "electrical and ELV design coordination", with a focus on automating workflows from room requirements to technical documentation.

Evidence

  • Tagline: “AI-assisted for electrical and ELV design coordination: turn room requirements into circuits, panel schedules, diagnostics, and technology-system reports.”

Inference (not evidenced)

  • The positioning suggests a niche B2B SaaS or engineering tool targeting architects, engineers, or contractors in the building services domain.

Back to contents

Target Customer & ICP

The description does not specify target customers or ideal customer profiles.

Evidence

  • No mention of specific customer personas, industries, or use cases beyond "electrical and ELV design".

Inference (not evidenced)

  • Likely targets professionals in electrical engineering, building services, or construction sectors.

Back to contents

Business Model & Pricing Evidence

There is no evidence in the description of a business model or pricing structure.

Evidence

  • No mention of revenue streams, pricing tiers, subscriptions, or monetization strategy.

Inference (not evidenced)

  • If this is a commercial product, it may be sold as SaaS or a one-time license; however, no evidence supports this.

Back to contents

Technical & Delivery Signals

The author lists several technologies used in the development of SchemaLab.

Evidence

  • Built with: .NET 8, C#, WPF, XAML, MVVM, OpenAI, Codex, GPT-5, QuestPDF, building-services, electrical-engineering, ELV-systems, diagnostics.

Inference (not evidenced)

  • The use of GPT-5 and OpenAI suggests integration with large language models for AI capabilities.
  • The UI is likely built using WPF and XAML, indicating a desktop application.

Back to contents

Traction & Maturity Signals

There is no evidence of traction or maturity beyond the hackathon submission.

Evidence

  • Submitted to the OpenAI 2026 hackathon.
  • Team size: 1 member (mburukabai Mburu).

Inference (not evidenced)

  • The project appears to be in early development, possibly a prototype or proof of concept.

Back to contents

Competitive Context

There is no evidence of competitive analysis or market positioning beyond the product description.

Evidence

  • No mention of competitors, market size, or differentiation strategy.

Inference (not evidenced)

  • The space of AI-assisted electrical design tools may be niche but could include existing CAD and BIM platforms with AI integrations.

Back to contents

Open Questions & Risks

Several key risks and open questions are evident from the lack of evidence:

Evidence

  • No revenue, customers, or adoption.
  • Only one team member.
  • Submitted to a hackathon — not a commercial product.
  • No business model or pricing information.

Inference (not evidenced)

  • Risk of being a prototype with no path to commercialization.
  • Lack of team scale may indicate limited execution capability.
  • No market validation or customer feedback.

Back to contents

Questions the description leaves open

  1. What is the current stage of development? Is this a prototype, MVP, or early product?
  2. Have you validated the need for this tool with potential customers in the electrical or ELV design space?
  3. What is your go-to-market strategy and how do you plan to monetize this tool?
  4. How does SchemaLab differ from existing tools in the market?
  5. Are there any early adopters or pilot users?

Back to contents

Overall Assessment

Not evidenced.

The project description provides no evidence of traction, revenue, customers, or a clear business model. It is presented as a hackathon submission with no indication of commercial viability or progress beyond the prototype stage.

Confidence Low — based on thin self-reported evidence only.

Back to contents

User Stories BotA demo video on every pull request

Get a demo video on every new feature

GitHub App that watches your pull requests and records a narrated, captioned demo.
Save time on demo prep and make informed decisions faster with a video walkthrough of every new feature.

Try User Stories Bot FREE (opens in a new tab)

Source

Submitted to the OpenAI 2026 hackathon on Devpost. Project home on Devpost. Back to the full gallery on Devpost.

This competitive analysis was produced by top-founders.com (opens in a new tab)

Generated by a language model from this project's public Devpost page — its tagline and any built-with tags listed there, since the author published no write-up — as captured in August 2026. Short quotations are the author's words. It is an opinion on the idea as a business, not on the people behind it, and it reflects the description as published then. If the project has moved on, request a correction below. The analysis is not independent research and contains no verified traction, revenue or customer data.

An independent guide by KFG International LLC — not affiliated with, endorsed by, or connected to Devpost or OpenAI.

Are you the author of this project? Request removal or a correction (support@kfginternational.com). Requests from authors are acted on within 48 hours, and in any case within seven days. Privacy notice.