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AI MVP Development – Proof of Concept for AI Applications

Working AI application prototype in 4-8 weeks. Concept validation, core features, ready to show investors and first users.

Author: Paweł Wiszniewski · Updated:

Key takeaways

Working AI application prototype in 4-8 weeks. Concept validation, core features, ready to show investors and first users.

  • ›Working prototype in 4-8 weeks
  • ›Integration with top LLMs (OpenAI, Claude, Gemini) configured for your specific use cases, not a generic demo.
  • ›Source code in your GitHub repository with documentation

Price: from €4,500

SERVICE DETAILS

AI MVP development is the fastest path from idea to working product. I design and build an AI application prototype in 4-8 weeks — functional enough to validate your business hypothesis with real users, solid enough to show investors. Stack: React/Next.js on the frontend, Python (FastAPI) or Node.js on the backend, integration with OpenAI, Anthropic (Claude) or Google (Gemini) models through standardized APIs. Deployed on Vercel or Render with automated CI/CD from day one. I deliver source code in your GitHub repository with documentation so your engineering team can take over seamlessly.

› INVESTMENT:

from €4,500
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Paweł Wiszniewski · my approach

What I do differently

I build MVPs the way I built my own products, such as AnswerLyzer — a SaaS platform for monitoring brand visibility in AI models with a job queue, Supabase and structured model outputs. Those projects taught me that the most expensive thing in an MVP is building features nobody needs. So I start with one hypothesis to test, cut everything that doesn't serve it, and track model cost per user from day one — in AI products that number decides whether the business works.

Case study: AnswerLyzer AI Analytics

My take and a real-world example

My take

I think in an AI MVP the code is the smallest risk. The biggest risk is building a product that works brilliantly and nobody wants to pay for.

From the market

In a March 2026 report, CB Insights analysed 431 VC-backed startups that shut down since 2023. Running out of capital was cited in 70% of cases, but that's usually the final stage rather than the root cause — the most common deeper cause was poor product-market fit (43%). That's why I design MVPs around one hypothesis to test with real users, not around a feature list.

Sources: CB Insights: top reasons startups fail (2026)

Who this is for

  • Founders who want to test an AI product idea before hiring a team.
  • Companies testing a new AI-based line of business.
  • Startups preparing a demo for investors or an accelerator.
  • Corporate innovation teams that need a fast proof of concept.

What you get

  • A scoping workshop: the key hypothesis, the user and the minimum features to test it.
  • A working web app with LLM integration, authentication and a database.
  • Prompts, evaluations and guardrails against the most common model failures.
  • Production deployment with analytics on first-user behaviour.
  • Code and documentation ready for your team to build on.

How results are measured

  • Whether the product hypothesis was validated with real users.
  • Activation and return rate of first users.
  • AI answer quality on a test set.
  • Model cost per active user.

Key Benefits

Working prototype in 4-8 weeks — fast enough to validate the idea before committing your full production budget.

Integration with top LLMs (OpenAI, Claude, Gemini) configured for your specific use cases, not a generic demo.

Source code in your GitHub repository with documentation — ready to hand over to your engineering team at project completion.

Deployed on Vercel or Render with CI/CD from day one — your MVP is live at a real URL, ready to demo to investors or pilot users immediately.

Architecture designed for scale — even the MVP is built so it can grow into a full application without a complete rewrite.

The Process

1

Discovery and scope

Over 3-5 days we map the core MVP features together: what it must do, what it doesn't need to do, and what success looks like after 8 weeks. I establish the stack, AI integrations, and sprint plan.

2

Prototype and core AI

In the first 2-3 weeks I build the foundation: data architecture, LLM integration, and the first working product loop. You have something to click through after the first sprint.

3

Feature development and UI

In the middle phase I add further layers: React/Next.js user interface, authentication, database, and full end-to-end flow. We sync progress regularly via Slack or Notion.

4

QA, deploy, and handover

Automated and manual testing, production deployment, monitoring setup (Sentry, Datadog), and a handover session — full documentation of code, environment variables, and architecture.

Dedicated MVP vs no-code platform vs in-house team from scratch

CriterionDedicated MVPNo-code platformIn-house team from scratch
Time to launch4-8 weeksDays for simple appsMonths, including hiring
AI integration depthCustom LLM logic for your caseLimited to platform featuresFull, but slow to build
Code ownershipFull — yours in GitHubLocked to the platformFull
ScalabilityBuilt to grow without rewriteHits platform ceilingsDepends on team skill
Upfront costFixed project scopeLow subscriptionHigh — salaries and overhead
Investor readinessLive demo at a real URLOften visibly templatedSlow to first demo
Best forValidating an AI product fastVery simple internal toolsFunded teams building long-term

Common mistakes

  • Building a full product instead of the simplest version that tests the hypothesis.
  • Ignoring model call costs until the first big bill arrives.
  • No AI answer evaluation — quality judged on a few lucky examples.
  • MVP code that can't be extended because it was built without tests or documentation.

When this service is not worth it

  • You don't yet know what problem you're solving and for whom — start with customer interviews.
  • A no-code tool or existing SaaS would achieve the same thing.
  • You need a full-featured system for thousands of users from day one.

Frequently Asked Questions

What is the difference between an MVP and a proof of concept?

A PoC is a technical experiment — proof that something can be built. AI MVP development produces a working product with core features you can show to real users and collect feedback from. I deliver MVPs, not PoCs.

Will you continue developing the product after the MVP?

Yes, if you want. The MVP is a starting point — after validation we can plan further sprints and add features together. You can also hand the code to your own engineering team with full documentation.

Which AI models can be integrated?

OpenAI (GPT), Anthropic Claude, Google Gemini, Mistral, Llama 3 (self-hosted), or your own models via API. Model selection depends on use case, token budget, and data privacy requirements.

Will I own the code?

Yes, 100%. I deliver the code to your GitHub repository — you have full rights to the project and can develop it with anyone you choose. No vendor lock-in.

How long does an AI MVP take to build?

Usually a few weeks, depending on scope, integrations and whether data needs preparing. We set the scope in a workshop so the MVP tests the most important hypothesis as quickly as possible.

What do I need to prepare before the MVP starts?

A description of the problem and target users, sample data or documents the AI should work on, and someone on your side who makes scope decisions. Early customer interviews or mockups speed things up.

Is the MVP good enough to show investors?

Yes. It runs in production with real users and data, so you can show not just a demo but first engagement metrics and the cost of serving a user.

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BIAŁYSTOK, PL
+48 732 022 086pawel.wiszniewski95@gmail.com