Build Your
AI/ML Tools MVP.
In Oxford.
Why Oxford is the
Perfect Launchpad
Oxford is a biotech and deep tech powerhouse, with strong university spinout culture. The city excels in life sciences, AI, and quantum computing startups.
Thriving Ecosystem
Oxford-grade innovation, startup execution
- Experience with complex scientific and technical products
- Understanding of university spinout requirements and timelines
- Access to Oxford's investor and advisor network
- Ability to translate research into commercial products
Why AI/ML Tools MVPs Fail
And How To Avoid It
We've seen dozens of founders in ai/ml tools make these mistakes.
Training custom models
Building ML infrastructure instead of applications.
Our SolutionOpenAI, Anthropic, and others have better models than you'll train. Build the application layer on top of their APIs.
AI-for-AI's-sake
Adding AI because it's trendy, not because it solves a problem.
Our SolutionAI is a tool, not a feature. What workflow does it improve? If you can't answer that, don't add AI.
Ignoring the interface
Focusing on prompts and models while the UI is an afterthought.
Our SolutionChatGPT won because of the interface, not the model. Your AI's value is in how humans interact with it.
What Your MVP Actually Needs
Don't waste budget on features you don't need yet. Focus on validation.
Must Haves
- Clear use case and workflow
- LLM integration (OpenAI/Anthropic API)
- User interface for input/output
- History and saved outputs
- Basic usage tracking
Wait For Later
- Custom model training
- Complex prompt chains
- Multi-model comparisons
- Enterprise AI governance
Built to Scale.
Not Just a Demo.
We use the same stack as billion-dollar startups. Next.js + Supabase + OpenAI/Anthropic APIs. Vercel AI SDK for streaming. Focus on the workflow, not the model.
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Core Features We Build
Ready to Displace the
Incumbents?
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