Judged on hand-picked inputs
The founder’s ten favourite examples go through beautifully. The first hundred real users send blurry scans, half-finished questions and edge cases nobody tried.
AI MVP development
AI MVP Development Tested on Real Examples First
For products that only work if the AI does. We prove the model can handle your real, messy inputs, at a cost per task your pricing can carry and with a fallback for when it cannot — then build the first release around it.
- Evaluation set agreed before the build
- Cost per task modelled from the start
- A non-AI fallback in every first release
Why AI MVPs stall
The demo worked. The product did not.
An AI-first product carries a risk an ordinary MVP does not: the core feature might not be good enough, cheap enough or safe enough on real inputs. Most stalled AI launches found that out after the product was built.
What an AI MVP has to prove
Demand still matters. An AI-first first release also has to answer four questions of its own, with evidence:
- The model does the job on real, messy inputs
- Each task costs less than it earns
- Wrong or uncertain answers are caught before they do harm
- Users trust the output enough to rely on it
- The model can be swapped without rebuilding the product
Unit economics nobody checked
Model fees, retrieval, retries and human review add up per task. If a customer on the entry plan costs more to serve than they pay, growth makes the problem worse.
No plan for the wrong answer
Every model is sometimes wrong or unsure. Without a designed fallback, users either see the mistake or see nothing — and either way they stop trusting the product.
Built around one model’s quirks
Prompts and parsing tuned to one provider’s model, with no tests to show what breaks. When prices change or a better model arrives, switching means starting again.
What we build
The AI first, then the product around it.
We de-risk the model before we spend your budget on screens. Once the AI clears the bar you agreed, the product work follows — and the evaluation set keeps running on every change after that.
Model proof on real examples
Before any product work, we run candidate models and prompts on a sample of genuine inputs and score the output against what an expert in your field would accept.
Discuss this
Evaluation sets and pass marks
A set of real cases with agreed good answers, and a pass mark set with you. It decides go or no-go, then runs on every prompt or model change after launch.
LLM integration
Cost-per-task modelling
Model fees, retrieval, retries and review time worked out per task and per user, then reduced with caching, smaller models or batching where quality allows.
Discuss this
Fallbacks and human review
A defined path when the model is unsure: a review queue, a simpler rule, or a form the user completes. The product keeps working even when the AI steps back.
AI workflow automation
The product around the model
Sign-up, onboarding, billing, admin and the screens users actually work in — built to production standard once the model has earned its place.
MVP development in the UK
Quality and cost tracking live
Every AI call logged with its cost, user corrections captured, and the cases users reject added to the evaluation set so the next release is measured on them.
Startup services
How we work
How an AI MVP earns its build budget.
The seven stages we use on every product, reordered around the AI risk: the model is tested on real examples, with a go or no-go decision, before the main product build starts.
- 01
Discovery
We pin down the one task the AI must do, for whom, and what a good result looks like to that user. Then we gather real examples — anonymised where they contain personal data.
A task definition and real examples
- 02
Strategy
We try two or three approaches — a hosted model, an open model, retrieval, or classic machine learning — against the evaluation set, and work out the cost per task of each.
A go or no-go on the model
- 03
UX & architecture
We design for uncertainty: how confidence is shown, how users correct an answer, and what happens when the fallback takes over. Data flows and provider terms are fixed here too.
Journeys for right and wrong answers
- 04
Development
Two-week sprints on the product around the model, with the evaluation set run on every prompt or model change so quality never drifts silently.
A working product, scored every sprint
- 05
Testing
Evaluation results, awkward and hostile inputs, prompt injection attempts, personal data handling, and cost at the volumes you expect in the first months.
An evaluation and cost report
- 06
Launch
Release to first users with cost caps, rate limits, logging and an easy way to flag a bad answer — so you learn about quality from data, not from complaints.
Live, with quality and cost tracked
- 07
Optimisation & support
User corrections feed the evaluation set, cheaper or better models are tested against it as they appear, and the next release is chosen on what the numbers show.
An evaluation set that grows with use
Technology
What AI MVPs are made of.
A swappable model layer, an evaluation harness and mainstream product technology — so you can change models, hire engineers and raise money without a rewrite.
Models
OpenAI GPT modelsAnthropic ClaudeGoogle GeminiLlama and Mistral (open models)
Retrieval and data
Embeddingspgvector and vector searchDocument parsing and OCRStructured output
Evaluation
Evaluation sets from real casesModel-graded checks with human spot checksRegression runs on every changeAdversarial test prompts
Product
Next.js and ReactNode.js or Python backendPostgreSQLStripe billing
Hosting
VercelAWSDockerQueues for long-running jobs
Cost and observability
Cost per task and per userResponse cachingRate limits and spend capsTracing of every AI call
Model and API fees are a running cost paid directly to the provider, separate from our fees. We estimate them per task before the build, and show you how they change with volume.
Use cases
AI-first products worth proving early.
Different products, same question underneath: can the AI do this job well enough, and cheaply enough, for people to pay for it?
An assistant for one profession
Drafts of reports, letters or assessments for a specific trade or profession, which the professional reviews and signs off — the AI saves time, they keep responsibility.
Documents in, data out
Customers upload invoices, forms or contracts and get structured, checked data back, with low-confidence fields sent to a person before anything is exported.
Listings and matching
Listing drafts written from a few photos and notes, or requests matched to suppliers, with the marketplace team able to see and correct what the model did.
A tutor inside the app
Practice questions, feedback and explanations generated around your own course material, with guardrails on what the tutor will and will not answer.
A new AI-led service
Test an AI-based proposition with a small group of existing customers, kept apart from core systems until the evidence says it deserves a place there.
One AI feature, shipped as an experiment
A single AI feature released to a subset of users behind a flag, with uptake, corrections and cost measured before it goes to everyone.
Relevant work
Products we have designed and built.
Dental.AI is a product built around a custom computer vision model: the model flags findings on dental images, and the dentist makes every clinical call.
All case studiesWhy Techsleight
What working with us is actually like.
No inflated numbers — just how we run projects, and what you can hold us to.
Product engineering, not ticket-taking
We ask what the software is for before we estimate it — and we will tell you when something should not be built, or should be bought instead.
AI where it earns its place
LLM features, retrieval and automation built with evaluation, guardrails and cost controls, and plain software where that is the better answer.
Full-stack under one roof
Design, frontend, backend, mobile, cloud and QA in one team, so nothing falls between suppliers.
UK-focused delivery
UK business hours, estimates in pounds, and a contract with a UK company. Our engineers are based in the UK and India.
Flexible engagement
A fixed-scope project, dedicated developers or a monthly retainer — and you can move between them as the work changes.
You own everything
Code, IP, cloud accounts and documentation are yours from day one. We sign an NDA before discovery if you need one.
Support after launch
We stay on for fixes, upgrades and new features, or hand over cleanly to your in-house team with the documentation to match.
FAQs
Questions we get asked.
Straight answers on scope, cost, timelines and how we work. If yours is not here, ask us directly.
What makes an AI MVP different from a standard MVP?
A standard MVP mainly tests demand. An AI MVP also has to prove the AI can do the job on real inputs, at a cost per task the business can carry, with a safe fallback when it is wrong. So we test the model on real examples first, and build the rest of the product only once it clears the bar you agreed.
What is an evaluation set, and why build it first?
It is a collection of real cases with answers your experts agree are good, used to score the AI. Built first, it turns “the demo looked good” into a measured result, gives a fair way to compare models, and catches quality regressions every time a prompt or model changes later.
How do you work out the AI cost per task?
We run the candidate approach on the evaluation set and record the tokens, retrieval calls, retries and any human review time each task needs, then multiply by the volumes you expect. That gives a cost per task and per user to set against your pricing, before the build begins.
What if the model cannot do the job well enough?
Then you find out early, for the cost of a discovery sprint rather than a full build. Often there is still a product: a narrower task the model does handle, a version where a person reviews every output, or a plan to revisit when models improve. Sometimes the honest answer is not yet, and we will say so.
What is a non-AI fallback, and does every AI MVP need one?
It is what the product does when the model is unsure or unavailable — route the case to a person, apply a simpler rule, or ask the user for the missing detail. Every AI-first product needs one, because every model is sometimes wrong, and providers occasionally have outages.
How soon will we know whether the idea works?
The model question comes first, so you get evidence on it early in the plan. As a typical illustration, a focused MVP is often in the region of 6–10 weeks from discovery to launch; that is an example, not a promise, and your dated plan follows once the evaluation results and scope are agreed.
Which AI model should our MVP use?
The cheapest one that passes your evaluation set with enough margin. We usually compare a large hosted model, a smaller hosted model and, where data must stay in your cloud, an open model. The results decide — and because the model layer is swappable, the choice can change later.
Can we avoid being locked into one AI provider?
Yes. We keep prompts, parsing and provider calls behind one internal interface, and the evaluation set shows what changes when you switch. Moving provider becomes a tested change of configuration and prompts, not a rebuild of the product.
How do you handle user data sent to AI providers?
We send only what the task needs, redact personal data where the model does not need it, and check the current data terms of the chosen provider for retention and training use. Where data cannot leave your environment, we can run an open model in your own cloud account. UK GDPR sign-off stays with your DPO or legal adviser.
What does an AI MVP cost to build and run?
It starts with a discovery sprint, from £2,000, which includes the model proof on your real examples. The product build is then quoted as a fixed price against the agreed scope. Model and API fees are a separate running cost, paid to the provider, which we estimate per task before you commit.
Start a project
Have an AI product idea to prove?
Tell us the task the AI has to do and who it is for. We will suggest how to test it on real examples, what a pass would look like, and what the first release would involve.
What happens next
- 1A senior engineer reads your brief within one working day, and replies with questions or a first view.
- 2A 30-minute call to understand the goal, constraints and what good looks like — no sales script.
- 3A written proposal with scope, milestones, team and a GBP estimate you can take to your board.
Techsleight Labs is a trading name of Krapton IT Consultancy.
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