AI MVP Development
AI MVP development that survives real data.
Most AI MVPs are a demo over a prompt: impressive in a week, then real data shows up and there is nothing underneath. We build the 80 per cent that actually decides whether an AI product works: the data engineering that feeds the model, and the evaluation that proves it is good enough to ship. We have been doing exactly that under production systems for over a decade. AI just changed what the output layer is called.
- Your data, evaluated, not just a prompt
- Fixed price, you own the code and IP
- Senior engineers who have shipped for JPL missions
A US company (Spicule Inc), with a staffed office in Alexandria, Virginia, and cover across US hours, 8am to 10pm ET.
Why AI MVPs stall
The demo is 20 per cent. The other 80 per cent is where AI products live or die.
The data
A model is only as good as what you feed it. Most of the work is getting the right data, clean, current, and correctly shaped, into the model. That is data engineering, and it is what an AI readiness engagement exists to fix.
The evals
"It looked right in the demo" is not a quality bar. We define what good enough means, build a harness that measures it on real examples, and wire it into the pipeline so regressions get caught before your users find them.
The guardrails
Cost, latency, and the failure modes that matter when the answer is wrong. Retrieval over your own data, budgets that hold at scale, and a human in the loop where being wrong is expensive.
We wrote the long version of this argument in why your AI project is actually a data project.
Two fixed-price ways to start
Fixed scope, fixed price, agreed before we start. Most founders start with the diagnostic. A full AI MVP is typically two to three sprints, and we tell you the realistic total before you commit to the build.
Start here if the idea is still forming
10-Day AI MVP Diagnostic
$8,000 / 10 business days
Whether the AI part is real or a wrapper, what data you actually need, and a fixed-price plan to build it.
- A data and AI readiness read: what you have, what is missing, what it costs to close the gap
- An architecture and evaluation plan (how you will know the model is good enough to ship)
- A 3-sprint build roadmap with a fixed price and a realistic total for the full MVP
Start here if you know what to build
3-Week AI Build Sprint
$40,000 / 3 weeks
A working AI slice, deployed and evaluated, on your real data, ready for real users.
- A deployed slice: one core AI workflow live on your data, with the data pipeline under it
- An evaluation harness so quality is measured, not vibes, and regressions get caught
- Guardrails, cost and latency budgets, and a roadmap for the sprints that finish the MVP
We have done the hard part before
Not AI demos. Production systems where the data was the problem and being wrong was expensive.
NASA JPL
PIXLISE: data pipelines and analysis tooling for Mars rover science, used by researchers worldwide.
Financial crime
Entity resolution and sanctions screening at bank scale, where a false negative is a headline and a false positive is a cost.
Open source
Saiku, an analytics platform used by 400-plus organisations, built and maintained by the same team.
Questions founders ask
What makes building an AI MVP different from a normal MVP?
The demo is the easy 20 per cent. A prompt over a foundation model looks like a product in a week, and then it meets real data and real users and falls over. The hard 80 per cent is the data engineering underneath (getting the right data, clean and current, into the model) and the evaluation (knowing the output is good enough to ship, and catching it when it regresses). We have been doing the data engineering under production systems for over a decade; the AI layer just changed what the output is called.
How much does an AI MVP cost?
Our starts are fixed: $8,000 for a 10-day diagnostic that produces a data and AI readiness read plus a buildable plan, and $40,000 for a 3-week sprint that ships one evaluated AI workflow on your real data. A full AI MVP is typically two to three sprints, so budget in the region of $80,000 to $120,000 depending on scope. We give you the realistic total in the diagnostic, before you commit to the build.
How do you stop it hallucinating, or shipping something that only works in the demo?
With evaluation, treated as a first-class part of the build rather than an afterthought. We define what "good enough" means for your use case, build a harness that measures it on real examples, and wire it into the pipeline so quality is a number you can watch and regressions get caught before users do. Guardrails, retrieval over your own data, and human-in-the-loop where the cost of being wrong is high.
Do you use our data, or public models?
Usually your data on top of a foundation model, because your data is the moat and the public model is a commodity. The work is getting your data into a state the model can use well: clean, current, correctly shaped, and governed. That is the same data engineering we do for AI readiness across the rest of our work.
Who owns the code, the models, and the IP?
You do, outright, from day one. The code, the pipelines, the evaluation harness, the repositories, and the deployment are yours, handed over with no lock-in. We build on boring, proven tech so any team can pick it up, exactly so you are never dependent on us to keep it running.
Can our own engineers work alongside you?
Yes, and we prefer it. Your team is in the demos, on the calls, and in the codebase, so knowledge transfer is built into the engagement. The goal is that when we leave, your team owns and can extend what we built.
Building something with AI in it? Let us pressure-test the hard 80 per cent.
Twenty minutes, no pitch. We will tell you whether the AI part is real, what data stands in the way, and which start fits.
Book a 20-minute fit call