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Short answer: what is fixed price AI development?
Fixed price AI development is a contract where the vendor commits to a price and a deadline for an agreed result, and carries the risk if the work takes longer than planned. For AI work it is possible, and often the right choice, when the scope is one well-defined capability, the data is available, and "done" is measured on an evaluation set rather than described in adjectives.
With Lytvynov Production, the fixed-price format is the AI Sprint: in 4 weeks a senior team working with AI coding agents builds and deploys one working AI feature, agent or RAG assistant for $10,000. Our minimum project size is also $10,000. This guide explains how fixed-price AI projects work in general, where they break, and how to scope one so that it does not.
How does a fixed-price AI project work?
A fixed-price AI project works by fixing three things and leaving one flexible. Price and time are fixed. The capability and its quality bar are agreed in writing before work starts. What stays flexible is the exact shape of the solution: which model, which prompts, how retrieval is set up, how much of the edge case handling is automated versus routed to a person.
The typical sequence looks like this:
- Scoping call. You describe the problem, the data and the systems. The vendor proposes one capability that fits the budget and the time.
- Written scope. A short document lists what the capability does, which inputs it handles, which systems it touches, what "done" means and what is explicitly out of scope.
- Delivery with weekly results. Working software every week, not status reports, so problems surface early.
- Acceptance against the evaluation set. The capability is checked on real examples agreed at the start, then deployed and handed over.
The difference from fixed-price web development is the quality bar. A login form either works or it does not. An AI feature answers correctly 80 or 95 percent of the time, and which number is acceptable must be agreed up front.
What does a fixed-price AI project cost?
What a fixed-price AI project costs depends on the scope the price covers, so compare scopes, not totals. The table shows our own prices and ranges, as published on our service pages. They are our numbers, not market statistics.
| Scope | Typical duration | With Lytvynov Production |
|---|---|---|
| AI proof of concept running on real data (one capability) | 4 weeks | $10,000 fixed (one AI Sprint) |
| AI feature in an existing product | 4 to 8 weeks | from $10,000 |
| Single-workflow AI agent | 4 to 8 weeks | from $10,000, typically $10,000 to $15,000 |
| Multi-tool business agent | 8 to 16 weeks | $10,000 to $30,000 |
| MVP of a new AI product | 8 to 14 weeks | typically $10,000 to $20,000 |
| Complex AI product with fine-tuning or RAG memory | 4 to 8 months | $60,000 to $150,000 |
In our experience, a typical US or UK agency quotes roughly 2.5 to 4 times more for the same scope; our AI agent development cost guide shows that comparison by agent type. Model and hosting costs come on top in every case and should be estimated separately.
When you compare fixed quotes for an AI proof of concept, check what "proof" means. A demo on hand-picked examples and a deployed feature measured on 50 real cases are both called a PoC, and they are not the same purchase.
When does fixed price work for AI?
Fixed price works for AI when uncertainty is about implementation, not about whether the goal exists. Four conditions make it safe:
- One capability with edges. "Classify inbound requests into these 8 categories and route them" can be fixed. "Use AI in operations" cannot.
- Data that exists and can be accessed. The documents, records or examples are available on day one, with permission to process them.
- A measurable definition of done. An evaluation set of real inputs with expected outputs, agreed before the work starts.
- A known integration surface. The systems the feature reads from and writes to have APIs or at least a stable export.
When all four are true, most remaining risk is engineering risk, which an experienced team can price. Senior engineers working with AI coding agents is what lets us commit to a working result in one month rather than a quarter.
When does fixed price break?
Fixed price breaks when one of the three AI-specific risks is hidden at signing: scope, data quality or evaluation.
Scope drift. AI features invite "while you are at it" requests: another document type, another language, another system. Each one is reasonable and each one changes the price. A fixed-price contract needs a list of what is out of scope, and the discipline to move new ideas into the next phase.
Data quality. Scanned PDFs, missing fields, inconsistent naming and duplicate records are found in the first week, not in the sales call. If the scope assumed clean data and the data is not clean, either the scope shrinks or the price changes. The honest fix is to look at real samples before signing.
Evaluation. Without an agreed test set, "the AI is not good enough" becomes an opinion, and opinions are where fixed-price projects turn into disputes. With one, quality is a number both sides can see.
There is also a fourth case: research. If nobody knows whether current models can do the task at all, a fixed price is a bet, not a plan. Run a short, time-boxed experiment first and fix the price of the build afterwards.
Fixed price vs time and materials for AI projects
Neither model is better in general; each fits a different kind of uncertainty.
| Fixed price | Time and materials | |
|---|---|---|
| Who carries schedule risk | Vendor | Client |
| Budget certainty | High, known before start | Low to medium, known as work progresses |
| Handles changing scope | Through a new phase or sprint | Naturally, week to week |
| Best for | One defined capability, a proof of concept, a budget that cannot move | Research, evolving products, long-running teams |
| Main failure mode | Disputes over quality or scope if "done" was vague | Open-ended spend without a clear finish |
| What you must prepare | Scope, data access, evaluation set | Priorities, a product owner, regular reviews |
A common pattern is to combine them: a fixed-price sprint to prove the capability on real data, then either further fixed sprints or a monthly team once the direction is clear. Our AI integration projects often start exactly this way.
How to scope a 4-week AI sprint
A 4-week AI sprint is scoped by choosing the one capability that matters most and cutting everything else. This is how we do it in a 30-minute scoping call and a short written scope:
- Name the job, not the technology. "Draft replies to warranty claims from our policy documents", not "build a RAG chatbot".
- List inputs and outputs. Which documents, records or messages come in; what exactly comes out and where it goes.
- Collect 30 to 100 real examples. These become the evaluation set, and they also expose data problems before the sprint starts.
- Set the quality bar and the fallback. What accuracy is acceptable, and what happens when the AI is unsure: a human review queue, a "not sure" answer, or no action.
- Fix the integration surface. Which systems are touched, with which access, and which are out of scope for this sprint.
- Write down what is not included. Large data cleanup, extra languages, new user roles. These become the next sprint if they are still needed.
The sprint itself then runs week by week: data access and a first end-to-end version in week 1, quality work on the evaluation set in week 2, integration with permissions, cost limits and logging in week 3, deployment and handover in week 4. You see working software every week.
What should a fixed-price AI deliverable include?
A fixed-price AI deliverable should include more than code that runs. At the end of our sprints you get the working feature deployed to your environment or ours, the source code in your repository, a short architecture note, the evaluation set used to check quality, and an estimate of the monthly model and hosting cost. You own everything.
We have shipped AI to real users as a product owner, not only as a vendor: our own AI Resume Master was built in about three months and reached 50,000 monthly active users. The team is based in Ukraine, has more than 15 years of experience, and holds a 5.0 rating with 100% Job Success on Upwork.
Next step
If you have one AI capability in mind, book a 30-minute call on our contact page. We will tell you honestly whether it fits into one fixed-price sprint, needs two, or needs a short experiment before anyone should fix a price.
