Generative AI development that survives contact with real users
Generative AI is easy to demo and hard to ship. The demo is a prompt and a model. The product is the prompt, the model, the data it may draw on, the things it must never say, the test that proves it still works after the model changes underneath you, and the bill that has to stay predictable at real volume. We build the product. Text, code, structured output, images and audio where they fit, always with the surrounding system that decides whether the feature is trusted or switched off after a month.
Who this is for
- - A product team adding a generative feature, such as drafting, summarising, extraction or search, to an existing application and needing it to behave inside a real codebase.
- - A company whose generative pilot produced impressive outputs and unacceptable errors, and now needs the version that can be trusted.
- - A team choosing between model providers, fine tuning and retrieval, and wanting a decision based on their data rather than a vendor’s pitch.
- - A business that needs structured, machine readable output from unstructured input, at a defined accuracy, at scale.
What you get
- - The right approach for the job, chosen on evidence: a general model with retrieval, a fine tuned model, or structured extraction, tested on your data before committing.
- - Grounding and guardrails designed in, so output stays within what you have approved and refuses what it should.
- - An evaluation set built before the model work starts, run before every change, including when the provider retires a model.
- - Cost per request and latency budgets agreed in writing, with the caching and routing that keep them.
- - Everything in your accounts: prompts, pipelines, evaluation data, infrastructure configuration.
What it costs
There is no honest fixed price before discovery, and anyone who gives you one is guessing. What we can say in writing after two weeks: the build cost, the running cost per month at your expected volume, and what moves each. The build is driven by how many systems the work must touch and how strict the accuracy, latency and compliance bars are. The running cost is model calls plus infrastructure, and it can differ by ten times depending on the choices made in week one. How we price AI work.
How long it takes
Discovery, two weeks: we map the problem, the systems involved and the success criteria, and put the numbers in writing. Pilot, four to six weeks: one capability, live, on a slice of real traffic, measured against those criteria. Then production hardening and scale out. Each stage is a separate agreement with no minimum commitment, so you can stop after discovery with a plan you own. How we work.
What you own afterwards
Everything. The code, the prompts, the evaluation set, the infrastructure configuration and the documentation, in your accounts, under your name. We do not hold anything hostage and we do not license our own platform to you. The evaluation set matters most: it is the thing that lets your team, or any other team, change the system later without breaking it. The deliverables clause.
Questions buyers ask
What does a generative AI development company deliver beyond the model?
The parts that make a feature trustworthy: the data it may draw on and how it is retrieved, the guardrails on what it may produce, an evaluation set that proves it works and keeps proving it after provider changes, and the cost and latency controls that keep the bill predictable at real volume. The model call is the smallest part.
Should we fine tune a model or use retrieval?
Almost always retrieval first. Fine tuning changes how a model behaves, not what it knows, so it does not teach a model your documents. It earns its cost for consistent style, format or classification behaviour at volume. We test both on your data in discovery before recommending either.
How do you choose which model provider to use?
We do not standardise on one. Different tasks in the same product suit different models on cost, latency, quality and data terms, and providers change models underneath you. The system is built so the model is a configuration choice behind an evaluation set, not a dependency, and switching is routine rather than a rewrite.
How much does generative AI development cost?
The build is driven by how many sources and systems the feature touches and how strict the accuracy and compliance bar is. The running cost is per request and depends heavily on the choices made in week one: model tier, caching, routing and output size can move it by ten times. After two weeks of discovery both numbers go in writing.
Before you decide
If you want the technical background first, read how generative AI systems work. Then, from our engineers:
Tell us what the feature needs to produce
Two weeks of discovery puts the cost, the timeline and the plan in writing before you commit to anything. Or start with the free audit.