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AI MVP development that proves the idea before you bet on it.

For founders and product leaders with an AI idea, a budget and a lot of unanswered questions. We find the assumption most likely to sink it, build a thin but real product around the AI core, score that core on your own examples and hand you a clear go or no-go with the path to production.

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A person replies within one business day
A plan you can act on
You own every asset

Time to first results: Proof of concept in 2 to 4 weeks
Time to MVP: Real users in 6 to 12 weeks
Engagement: Fixed-scope sprints
Best for: Founders and product teams testing an AI idea

Reviewed by Noor KhanUpdated November 2026

AI MVP· Eval set to go decision

Sample

Real examples gathered
AI core scored on them
Thin app meets real users
Go, backed by evidence

Illustration with sample data. Your version uses your accounts and numbers.

Brands Noor has worked with

Overview

What is AI MVP development?

AI MVP development is building the smallest working version of an AI product that real users can try, so you learn whether the idea holds up before funding a full build. It's more than a demo: the AI part is tested against real examples from your business, and the product around it records how people actually use it. A proof of concept answers whether the AI can do the job at all, and an MVP answers whether people will use it. Noorscale scopes the riskiest assumption first, builds a web app with the AI core inside, measures both, and gives you a written go or no-go with a plan for production.

See it in action

What does an AI MVP look like when it's working?

When it's working, you see four changes: an answer in weeks, not quarters; evidence your board can check; an MVP that can grow into the product; and money saved on the wrong idea. The live sample below is built with illustrative data. Your version runs on your own accounts and your numbers.

Site rebuild · speed and quality

Sample

Desktop previewLCP 4.9 s

yoursite.com

Larchwood PhysioTreatmentsPricingAboutBook a visit

Back to full strength, sooner.

Same-week assessments with senior physiotherapists.

Book a visit

Next available, Thursday

9:001:304:15

Same-week slots open

Sports injuries

Back and neck pain

Post-surgery rehab

Lighthouse, mobile

Template and page builder

Performance

Accessibility

Best practices

SEO

LCPMain content visible: 4.9 sPoor
INPResponse to a tap: 380 msNeeds work
CLSLayout shift: 0.21Needs work

Route to a bookingContact page, 7-field form

Page load waterfall

Request Largest element

HTML document48 KB, no caching
Stylesheets6 files, 410 KB
Web fonts4 files, loaded late
JavaScript780 KB of builder code
Hero imageJPG, 1.9 MB
Third-party tagsChat, slider, pixels

LCP 4.9 s0s1s2s3s4s5s6s7s

Illustrative lab figures for a sample site on a throttled mobile connection. Your audit measures your own pages and real-user data.

Sound familiar?

What problems does an AI MVP solve?

An AI MVP solves three common problems: “The demo impressed everyone, then real data broke it”, “We can't tell if this is worth a full build” and “Our prototype can't become the real product”. Each one has a fix.

The problem

“The demo impressed everyone, then real data broke it”A prompt that works on five hand-picked examples falls apart on messy documents, odd phrasing and edge cases. Nobody measured accuracy, so nobody saw it coming.
“We can't tell if this is worth a full build”The board wants a number, the team wants to start coding, and nobody has tested the one assumption that decides whether the product works.
“Our prototype can't become the real product”A notebook or a no-code mock-up proved the idea, but it has no sign-in, no data model and no logging. Moving to production means starting again.

What changes

An answer in weeks, not quartersYou learn whether the AI can do the job on your data before committing to a full build or a full team.
Evidence your board can checkTest scores, usage data and cost per task replace gut feel when you decide to fund, change course or stop.
An MVP that can grow into the productTyped code, a real data model and logging mean a go decision builds on what exists instead of starting over.
Money saved on the wrong ideaIf the riskiest assumption fails, it fails early and cheaply, and you keep the data, tests and findings for the next idea.

Compare your options

Internal demo, full product build or an AI MVP first?

Each option suits a different situation: internal prototype or demo (showing the idea internally), full production build (the idea is already proven) and AI MVP by Noorscale (a key assumption is still untested). Here's how they compare on the points buyers ask about most.

Internal demo, full product build or an AI MVP first?
Compared on	Internal prototype or demo	Full production build	AI MVP by Noorscale
Time to an answer	Internal prototype or demoFastest to show something	Full production buildSlowest to real feedback	AI MVP by NoorscaleProof of concept in 2 to 4 weeks
Tested on your real data	Internal prototype or demoUsually hand-picked examples	Full production buildOften only after launch	AI MVP by NoorscaleScored on your examples first
Shows whether users want it	Internal prototype or demoRarely, few real users	Full production buildYes, but late and costly	AI MVP by NoorscaleYes, through a supervised pilot
Spend if the idea fails	Internal prototype or demoLowest spend	Full production buildHighest spend	AI MVP by NoorscaleLimited, you can stop after any stage
Can grow into the product	Internal prototype or demoUsually rebuilt from scratch	Full production buildYes, built for production	AI MVP by NoorscaleYes, typed code and a real data model
Best when	Internal prototype or demoShowing the idea internally	Full production buildThe idea is already proven	AI MVP by NoorscaleA key assumption is still untested

On the free call, Noor will tell you which option suits your stage, even when it's one of the others.

What's included

What does an AI MVP include?

It includes six deliverables: riskiest-assumption brief, evaluation set from your real examples, the AI core, a thin but real web app, product analytics and AI traces, and go or no-go report and production plan. Each one is working material your team can use, not a slide deck.

Book a free AI MVP call

Scope · AI MVP Development6 deliverables

01
Riskiest-assumption brief
The one or two questions that decide whether the product works, how we'll test them and what result counts as a pass, agreed before we build.
02
Evaluation set from your real examples
Real documents, questions or records with the right answers marked, so the AI core is scored on your data and not on a demo script.
03
The AI core
The model calls, retrieval, prompts and tool use that do the actual job, built so the model can be swapped as better or cheaper ones arrive.
04
A thin but real web app
Sign-in, the core flow and the screens users need to reach a result, in TypeScript on Postgres, so the MVP can grow into the product.
05
Product analytics and AI traces
Every user step and every AI call logged, with cost, latency and user feedback, so you see where people succeed and where the AI slips.
06
Go or no-go report and production plan
Test scores, usage findings, running cost per task and a plain recommendation, plus the architecture, scope and risks for the production build.

Tools and platforms we work in

OpenAI
Anthropic
Google Gemini
LLlama
LangGraph
Vercel AI SDK
PostgreSQL with pgvector
Langfuse
Next.js
Python
PostHog

Process

How long does an AI MVP take?

The work runs in five steps, starting in weeks 1 to 2. Every step ends with something you can use, so you always know what's landed and what comes next.

Time to first results: Proof of concept in 2 to 4 weeks

Step 01Weeks 1 to 2
Scope the riskiest assumption
We map the idea, the users and the data, pick the assumption most likely to fail, and collect real examples to test it against.
You get: Riskiest-assumption brief and real test examples
Step 02Weeks 2 to 4
Prove the AI core
We build the AI part on its own and score it on the evaluation set. If it can't reach the agreed bar, you find out here, before paying for a product.
You get: AI core scored against the agreed pass bar
Step 03Weeks 4 to 10
Build the thin product
We wrap the AI core in a working web app with sign-in, the core flow, analytics and feedback capture, demoed at the end of every sprint.
You get: Working web app demoed at every sprint
Step 04Weeks 8 to 12
Put it in front of real users
A small group uses it on real work while we watch traces, fix failures and add every miss to the evaluation set.
You get: Pilot usage data, traces and a growing evaluation set
Step 051 to 2 weeks
Decide and plan production
We review the scores and usage with you, then write the go or no-go and, if it's a go, the scope and plan for the production build.
You get: Written go or no-go and production plan

Reporting

How will you know an AI MVP is working?

You'll know an AI MVP is working from four measures on a live dashboard: evaluation score, activation rate, cost per task, and user feedback and return rate. You also get a written review every month: leading indicators first, then the numbers your finance team cares about.

Monthly reportSample

Evaluation scoreHigher is better: How often the AI core gets your real examples right, against the pass bar we agreed before building.
Activation rateHigher is better: Share of test users who reach the key result in their first session, tracked in product analytics.
Cost per taskLower is better: Model and hosting cost for each completed job, so you know the unit economics before you scale.
User feedback and return rateHigher is better: Thumbs up or down on AI outputs plus whether people come back, so you judge usefulness, not novelty.

Free toolSERP Snippet PreviewFetch a page's real title and description, then preview and fix how Google shows it on desktop and mobile.Use it free

15 minutes with Noor

Noor Khan, Founder & CEO

Free

You'll leave with

Your biggest growth opportunity, in plain terms
The first two or three moves worth making
A clear plan for your first milestone

Noor listens first, then shows you the fastest route to the result you want.

Useful to have with you

Your website address
The goal or problem you want to solve
Any numbers you already track

See available times

Booking opens on Cal.com. Or send a call request

Who it's for

Who is an AI MVP for?

It works best for founders validating an AI product before raising or hiring, product teams testing an AI feature before adding it to the roadmap and two more listed here.

Founders validating an AI product before raising or hiring
Product teams testing an AI feature before adding it to the roadmap
Companies with a clear use case and real examples to test on
Leaders who need evidence for a build or buy decision

Engagement

How is an AI MVP scoped and priced?

Pricing is shared after the free call, in a written scope with deliverables, timeline and fee. Never a package, never a surprise.

Book a free AI MVP call

Monthly retainer
Ongoing work against a 90-day plan, for channels that compound month over month.
- Monthly report and strategy call
- Priorities reset every quarter
- No long lock-in
Typical for this service
Fixed-scope project
A defined scope, timeline and fee for builds, audits, migrations and launches.
Usually: Fixed-price proof of concept, then fixed-scope sprints for the MVP
- Deliverables agreed in writing
- Milestones you can check
- Optional care plan after launch
Focused sprint
A short, time-boxed piece of work on one priority, to prove the approach first.
- One clear goal
- Working output, not a deck
- Roll into a retainer or stop

Every engagement includes

Riskiest-assumption scope and evaluation set
AI core scored against your real examples
Thin web app with sign-in, analytics and AI traces
Supervised pilot with real users
Go or no-go report with a production plan

What shapes the scope

How hard the AI task is
Data cleaning and connections needed
Number of user flows in the MVP
Industry rules that add review steps

You'll see how each one applies to you in the written scope.

Who leads the work?

Your AI MVP, led by Noor Khan

A software engineer turned AI and growth strategist. Noor sets the strategy and architecture on every engagement, reviews every plan before it ships and built the method the whole team works by.

FAQ

Questions about AI MVP Development

Something else on your mind? Email grow@noorscale.com and a person will reply within one business day.

How much does an AI MVP cost?

The proof of concept is priced as a fixed fee, then the MVP in fixed-scope sprints, so you can stop after any stage. Pricing reflects how hard the AI task is, how much of your data needs cleaning or connecting, how many user flows the MVP needs and whether your industry's rules add review steps. You get a written scope and fee after a free call.

How long does it take to build an AI MVP?

A proof of the AI core usually takes 2 to 4 weeks, and an MVP with real users takes 6 to 12 weeks in total. Cutting scope is the fastest way to learn sooner, and we'll push back on features that don't help answer the main question.

What's the difference between an AI prototype, a proof of concept and an MVP?

An AI prototype shows what the product could look like, often with sample data. A proof of concept tests whether the AI can do the job, scored on your real examples. An MVP wraps that AI in a product real users can try, so you also learn whether people want it and how they use it. We often run the proof first and only build the MVP if it passes.

What do you need from us to start?

A clear description of the job the AI should do, real examples such as documents, questions or records, and someone who can judge whether an answer is right. Access to a few real users for the pilot matters too. We handle the rest, including cleaning and labelling the examples with your team.

Is our data safe while you build?

Yes, we use business API terms under which model providers don't train on your inputs, and we work in cloud accounts you own wherever possible. Sensitive fields can be masked in test data, and in-region hosting or open-weight models are options when your industry's rules require them.

What happens if the AI doesn't pass the test?

You find out early and you've spent the least possible. We'll show you where it failed, whether a different model, more data or a narrower task could fix it, and whether it's worth another round. Sometimes the honest answer is to stop, and the report says so.

Can the MVP become our production product?

Yes, that's why we build it in TypeScript on Postgres with sign-in, a real data model and logging from day one. The production plan lists what needs hardening, such as scale, security reviews and admin tools, and we can build it or hand it to your team.

Do we own the code, prompts and test data?

Yes, you own all of it. The repository, cloud accounts and model API keys sit in your name, and the prompts, evaluation set and documentation are handed over with the code.

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What pairs well with AI MVP Development?

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