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 #6,563 place in the like-ranked listing is a tie-break inside that group, not a ranking.
Projects (log scale)
Likes on Devpost. ▲ marks this project's group.
Show the figures
| Likes | Projects | Share of archive |
|---|---|---|
| 0 | 5,592 | 71.2% |
| 1 | 1,758 | 22.4% |
| 2 | 285 | 3.6% |
| 3–4 | 132 | 1.7% |
| 5–9 | 75 | 1.0% |
| 10+ | 14 | 0.2% |
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?
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.
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.
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.
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.
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.
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.
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.
Key Risks & Red Flags
Several key risks and red flags 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.
Diligence Questions To Ask The Founders
- What is the current stage of development? Is this a prototype, MVP, or early product?
- Have you validated the need for this tool with potential customers in the electrical or ELV design space?
- What is your go-to-market strategy and how do you plan to monetize this tool?
- How does SchemaLab differ from existing tools in the market?
- Are there any early adopters or pilot users?
Investment/Partnership Verdict
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.
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.