Measured AI MVP development
Our AI MVP development services put an LLM, retrieval or agent feature in front of real users, measured against an evaluation set. Builds take 4–8 weeks, and you see the numbers before you scale.
Our approach
An AI MVP has to answer two questions at once: do users want it, and is the AI reliable enough to trust? We plan the build around both. You end up with a product people can use and a measured answer on accuracy, running cost and failure cases.
What you have at the end of an AI MVP
Every AI MVP ends with something users can touch and a written record of how well it works.
- A working product, deployed to your cloud account, that your first users can sign in to.
- An evaluation set and results: where the model is right, where it fails, and how often.
- Running-cost figures per request, so you can model what scale will cost you.
- A short list of what a production version needs: security, monitoring, data agreements, governance.
- The code and documentation, owned by you.
How long does an AI MVP take?
Four to eight weeks from kickoff to first users. The model is tested on your real data in the first weeks, so a weak idea fails early and cheaply. Every build ends with an evaluation report you can show a board, an investor or an auditor.
4–8 week build
From kickoff to first users, with a go or no-go on the model in the early weeks.
Evaluation set from week 1
Real examples the model is judged against, rerun after every change.
Handover pack
Evaluation report, running-cost figures, production checklist, code and docs.
A typical 4–8 week build
Shorter when the data is ready and the scope is one workflow. Longer when data needs work or several systems are involved. Every build runs under our ISO/IEC 20000-1 certified service processes.
After the first call you get a written proposal within two working days.
Week 1: discovery and evaluation set
The use case, the users, the success criteria and a set of real examples the model will be judged against.
Early weeks: model proof of concept
Prompting, retrieval or fine-tuning tested against the evaluation set. Go or no-go before the product build starts.
Middle weeks: product build
Interface, integrations, guardrails and logging, in short sprints with a demo each time.
Final week or two: first users
Release to a small group, watch real usage, fix what breaks, measure again.
Handover
Evaluation report, running-cost figures, the production checklist, code and documentation.
What drives the cost of an AI MVP?
We don't publish prices, because the same idea can be a four-week build or an eight-week one depending on these factors.
| Cost driver | Why it matters |
|---|---|
| Model approach | A hosted LLM API is quick to start but costs per request. A self-hosted or fine-tuned model costs more to set up and less to run at volume. |
| Data readiness | Clean, accessible data shortens the build. Data that needs collecting, cleaning or labeling adds weeks. |
| Integrations | Each system the MVP reads from or writes to (CRM, ERP, document stores) adds build and test time. |
| Compliance scope | Personal data, financial data or a regulator in the loop means more controls and documentation from day one. |
| Squad size | A smaller squad over eight weeks or a larger one over four. We'll tell you which suits your deadline. |
Pricing is scoped and quoted after a free 30-minute discovery call, with a written proposal within two working days.
Measured, not demoed
The evaluation set is agreed in week one and rerun after every change, so "it works" means a number, not a good demo.
Governance from the start
Logging, access control and documentation are built in, which makes later work on the EU AI Act, NIST AI RMF or Nepal Rastra Bank guidance far easier. See AI governance and security.
A path to production
When the MVP proves itself, we scale it with MLOps and AI infrastructure or a dedicated team.
AI MVP or something else?
| Model | Team size | Start time | Pick it when |
|---|---|---|---|
| Staff augmentation | 1–5 engineers | 5–10 working days | You already have a team and a lead, and need more hands or one skill you lack. |
| Dedicated team | 3–15+ engineers | 2–4 weeks | A product that will keep changing for a year or more, and you want the same people on it. |
| Project-based | Scoped to the work | 1–3 week discovery | The scope is clear enough to fix, and you want us accountable for delivery against milestones. |
| PoC / MVP | Small squad | 4–8 week build | You need to prove an idea works, or put a first version in front of users, before committing more. |
Pricing is scoped and quoted after a free 30-minute discovery call, with a written proposal within two working days. Dedicated teams and staff augmentation run on 30 days' written notice; projects end on milestone acceptance.
When this isn't the right fit
- You only need to know if the model can do the task. A narrower proof of concept answers that faster.
- You don't have the data yet, or it isn't labeled. Start with data labeling or data engineering.
- You need a regulated production system from day one. That's a full build with governance work, not an MVP.
- The problem doesn't need AI. If a rules engine or a good search box solves it, we'll say so.
Certifyi AI SaaS GRC platform
A product we built with AI-driven automation at its core:
We built Certifyi, an AI SaaS platform for governance, risk and compliance, from product architecture to deployment: Go, Next.js and PostgreSQL on AWS, with CI/CD in GitHub Actions. It maps 100+ compliance frameworks in one system and automates evidence collection. The case study reports up to 3x less manual compliance work for its users.
Further reading
Ready to test your AI idea?
Book a free 30-minute call. You get a written proposal within two working days.
