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AI MVP development · United Kingdom Prove the AI product before you scale it.

AI MVP development for UK startups and product teams: a focused first release of an AI product, with an evaluation set, running-cost visibility and a plan to grow from evidence.

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UK-registered company. International engineering team.

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AI MVP development for UK businesses.

An AI product idea is easy to demo and hard to ship. The gap is filled by unglamorous work: representative examples, an evaluation set, a cost model, permissions, a fallback when the model is unsure. Krapton builds AI MVPs for UK founders that close that gap early, so the first release is something real users can rely on.

We scope the MVP around the assumption you most need to test, build it on proven components (models from OpenAI, Anthropic or Google, retrieval over your data, a Next.js or mobile front end), and instrument it so the next decision is made on usage rather than hope.

  • OpenAI and Anthropic APIs
  • Retrieval-augmented generation
  • Next.js
  • React Native
  • Node.js and Python
  • Supabase and PostgreSQL
  • Stripe
  • Vercel

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Where it earns its keep.

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    AI assistant products

    A vertical assistant for a profession or task, grounded in curated knowledge and priced per seat or per use.

  • 02 /

    Document understanding

    Extract, classify and summarise contracts, forms or reports for a specific industry.

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    Workflow copilots

    Draft, check and route routine work inside an existing process, with a human confirming the result.

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    AI search and discovery

    Natural-language search over a catalogue, archive or knowledge base with cited answers.

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Built to be trusted in production.

Evaluation before release, guardrails by design and people in control of the decisions that matter.

AI development overview

  1. 01

    Discovery sprint

    The user, the task, the riskiest assumption and the examples that define a good result. A prioritised scope and a fixed quote.

  2. 02

    Evaluation set first

    A test set built from real examples, so every prompt, model or data change can be measured before users see it.

  3. 03

    Build the thin product

    The core journey, onboarding, billing where needed and the AI feature, shipped in short cycles with weekly demos.

  4. 04

    Launch and learn

    Usage, quality and cost dashboards from day one, then a roadmap decided by evidence.

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The decisions that shape the build.

Model running costs scale with usage, so pricing and rate limits are designed with the product. Personal data in prompts and outputs falls under UK GDPR; we agree data flows and retention with you before launch.

What every AI engagement includes

  • A use case with a measurable result and representative examples
  • An evaluation set rerun on every prompt, model or data change
  • UK GDPR-aware data flows, with UK or EU hosting where residency matters
  • Human review and defined permissions for anything consequential
  • Cost and quality monitoring after launch

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Applied AI, connected to a real product.

Krapton’s own AI products, then a success story from our international portfolio describing the work delivered.

Dental.AI project visual

Healthcare AI

Dental.AI

Dental.AI set out to give patients instant, AI-driven insight into their oral health from a single dental image — which meant pairing a clinical-grade analysis pipeline with a web experience patients actually trust.

Read the success story

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AI MVP development: questions UK teams ask.

How much does an AI MVP cost in the UK?

It depends on the platform, the data work involved and the integrations. A grounded assistant over a curated knowledge base is a smaller project than an agent that acts in several systems. We quote a fixed scope in GBP after discovery.

How long does it take to launch an AI MVP?

A focused AI MVP typically launches within a few months of discovery. Data readiness and the evaluation work are the main variables, so we start them in week one.

Which model should our product use?

The one that performs best on your evaluation set at an acceptable cost and latency. We compare models from OpenAI, Anthropic and Google, and open-weight options where private hosting matters, and recommend on evidence.

How do we keep running costs under control?

By measuring cost per request from the first prototype, caching and retrieval strategies that reduce tokens, model routing for simple versus complex tasks, and product limits that match your pricing.

Can the MVP become the full product?

Yes, if it is built properly. We use an architecture and code quality that lets the MVP grow, rather than a throwaway prototype that has to be rebuilt after fundraising.

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Connect the capabilities.

Let’s scope your MVP AI product.

Bring the task, the examples and the systems involved. We’ll tell you what an evaluation set, a prototype and a first release would look like.

What are you thinking?