AI MVP development, at a fixed price in 8–12 weeks
An AI MVP is the smallest version of a product in which the model does the job someone would pay for — and enough of the product around it that they can. We build both halves: the data work and the pipeline, and the app, the integrations and the evaluation that tell you whether it works. The closest thing we have shipped, a multilingual operations platform, went to production in 10 weeks.
$40,000–$120,000, 8–12 weeks, fixed price · a written scope inside 48 hours of the discovery call · or hire AI engineers onto your team from $4,000 a month
What goes into an AI MVP?
Six pieces of work. A demo needs two of them; a product people can rely on needs all six, and the distance between the two is where AI budgets go.
A data audit before any code
A genuine random sample of your documents — not the tidy examples — a definition of what a correct answer is, and an honest read on whether your data contains it.
The pipeline: retrieval, agent or both
Retrieval over your own data for most products, agent steps where the model has to act, and fine-tuning only where the RAG-or-fine-tuning test says it earns its cost.
The product around the model
Auth, roles, tenancy, billing and an admin. An AI SaaS is still a SaaS: we build it multi-tenant and subscription-ready from the first schema, as we did for ArendaOS, our own product.
Evaluation from week one
A golden dataset of real cases with known answers, built before the pipeline, so quality is a number you can regress against. The 99.2% on our case study is that harness’s number.
The integrations it has to reach
ERP, CRM, identity, storage — whatever the answer has to read from or write to. Integration count moves an AI quote more than any other single thing.
Production, not a demo server
CI/CD, monitoring, accuracy dashboards, cost alerts and model versioning, with the runbook handed over alongside the code.
How an AI MVP gets built in 8–12 weeks
The order the logistics platform was built in. Each step leaves something you can check, rather than a status report.
Discovery and scope
The data audit and the definition of a correct answer, then a written scope with one fixed price inside 48 hours of the call.
Golden dataset
Built in week one, across your languages and document types, so every later change is measured against the same bar.
Architecture and cost per answer
Retrieval or fine-tuning, which model behind which interface, and what one answer costs at your volume — written down before the code.
One workflow, end to end
The highest-volume job first, from data to the screen a user works in. A narrow product that completes a real job beats a broad one that half-completes several.
The next slices
Two-week sprints with live demos, each slice measured against the golden dataset before it ships.
Launch and handover
Production deployment with monitoring and cost alerts. The code, the evaluation set and the runbook are yours from day one.
What an AI MVP costs, in one paragraph
The full breakdown has its own page; this is the short version.
An AI MVP — custom pipeline, frontend, backend and production infrastructure — runs $40,000–$120,000 over 8–12 weeks, quoted as one fixed price after a discovery call. Where a product lands inside that band is decided by how ready the data is and how many systems the answer has to reach, rarely by the model. Running it is a separate and much smaller number: about $0.011 per answer on the platform behind this page, passed through at cost.
Where the money goes inside the band · AI development cost calculator · What it will cost to run
Yarvixo delivered our AI platform in 10 weeks — on time, on spec, and with architectural decisions I could not have made better myself. Genuinely impressive team.
When an AI MVP is the wrong first step
Three situations where a different, smaller purchase gets you further, and we would rather say so on the first call.
You already have the product
Then it is an LLM feature, not an MVP: AI integration into the product you run, $15,000–$30,000 over 2–4 weeks.
Nobody knows if the data can carry it
Then the first purchase is discovery: usually a week, and the most useful thing it can produce is a no before the budget is spent.
You have a product owner and a backlog
Then the gap is capacity, not a scope: AI engineers on your team at $4,000–$7,000 per engineer per month.
Common questions
What founders and product leads ask before an AI MVP starts.
What this has shipped
The two halves of an AI MVP, each shipped and published under its own case study.
The AI half: an enterprise AI platform
14 data sources in 5 languages, a custom RAG layer, a review workspace and ERP integration — 87% less manual processing, in production in 10 weeks. Client anonymised.
Read case study →The product half: ArendaOS
Our own multi-tenant B2B SaaS: twelve modules, seven roles, PDF contracts and overlapping bookings refused by the database. Live at arendaos.com.
Read case study →Have an AI product to get in front of users?
Tell us what it should do and where the data lives. We come back with a fixed scope and price in writing within 48 hours of the call.
Message received
We’ll review your enquiry and respond within one business day.