Generative AI Development
We build generative AI into real products, not demos
Most generative AI projects die in the same spot: the demo. It dazzles once in a notebook, everyone’s impressed, and then it meets real users, messy data and the edge cases nobody mentioned, and quietly falls over. We build the kind that survives that. Generative AI that lives inside a product people use every day, where the boring parts (testing, guardrails, watching the bill, catching failures before your customers do) are part of the build, not a “phase two” that never comes.
Most of what we make comes down to a few patterns. A copilot sitting inside your app that handles one narrow job really well. A retrieval (RAG) system that answers from your own documents and tells you exactly which page it pulled the answer from. Tools that draft, rewrite and summarise piles of text without a person babysitting every line. Or a single generative feature dropped into software you already run. Different shapes, same rule: it has to earn its place.
The code, the prompts, the provider accounts: yours, from day one. We pick the model to match the job and what you’re willing to spend, then keep it behind a thin layer so swapping it later is a config change, not a rewrite (and it will change; this field moves fast). If a plain if-statement would do the job better than a language model, we’ll say so before you’ve paid us to build the clever version.
What we build
Copilots and in-app assistants
A focused helper built into your product: it drafts, explains, searches or walks a user through something, and it does that one job well. Not a generic chat bubble stuck in the corner hoping someone talks to it.
RAG over your own data
Answers pulled straight from your documents, with a citation so anyone can check the source. When the answer genuinely isn’t in there, it says “I don’t know” instead of inventing one, which is rather the point.
Content and document generation
Draft, rewrite, translate and summarise at a volume a person can’t keep up with, with someone signing off before anything actually goes out the door.
Structured data extraction
Hand it a mess (scanned PDFs, forwarded emails, half-filled forms) and get back clean, structured data your systems can read. The unglamorous work that quietly saves hours every week.
Generative features in existing software
You’ve already got an app. We add the generative part inside it (behind your own login, in your own stack) without tearing the thing down to start over.
Evaluation and guardrails
The part most people skip: test sets, scoring, input and output filters, rate limits, logging. It’s how you prove the thing works today, and how you catch the moment it stops.
How a project runs
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01
Pin down one job worth doing
We find a single task where AI clearly beats how you handle it now, and write down what “good” actually means (a number, not a vibe) before anyone touches code.
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02
Prototype on your real data
No polished demo on cherry-picked examples. A rough version running on your actual documents and inputs, so the quality, the cost per run and the odd edge cases surface now, while they’re cheap to fix.
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03
Evaluate, then harden
We score the output against that test set, add a human check wherever a mistake would hurt, bolt on the guardrails, and push the cost per request down until it makes sense at scale.
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04
Ship and watch
It goes live to a small slice of traffic first. We watch the quality and the spend, fix what the real world throws up, and only open the gates wider once the numbers hold.
When we’d tell you not to do this
- A template, a search box or a plain rule would already fix it. If the task is predictable, a model is just a slower, pricier, flakier way to get the same answer.
- There’s nothing solid to ground it in and no way to check what comes out. Give a model nothing to stand on and it’ll hand you confident nonsense with a straight face.
- You need it right every single time, on something high-stakes, with nobody checking. We’ll put a person in the loop, or we’ll pass on the project. We won’t pretend that risk away.
- You just want to “add AI” because everyone else is. Fair enough, but let’s find the real job first. That chat costs you nothing; a build you didn’t need costs plenty.
Questions people ask us
How is this different from your AI automation service?
Automation is about handing a repetitive, end-to-end workflow over to software so nobody has to touch it. Generative AI development is about building a feature or product (a copilot, a RAG assistant, content or extraction) usually inside something your team or your customers use. There’s plenty of overlap. Tell us the problem and we’ll point you at whichever one actually fits.
How do you stop the AI from making things up?
A few things, stacked. We ground answers in your own data instead of the model’s memory, make it cite where each answer came from, and box in what it’s allowed to say. Then we test it against real questions. When the answer isn’t in your data, a well-built system admits it rather than guessing, and that honesty is worth far more than a clever-sounding wrong answer.
Which model do you use?
Whatever fits the job, the quality you need and the budget: OpenAI, Claude, Gemini, or an open model like Llama or Mistral on your own servers when the data can’t leave the building. We don’t marry a single provider, because the “best” one changes every few months and you shouldn’t have to rebuild when it does.
Can you add a generative feature to our existing app?
Yes, and honestly it’s most of what we do. We add the generative feature inside your current app, behind the login you already have and in the stack you already run, without a rebuild.
What happens to our data?
We stick to provider plans that don’t train on your data, keep keys and secrets out of the code, and give each part of the system only the access it genuinely needs. If the data legally can’t leave your environment, we run open models on hardware you control instead.
How do you know it worked?
We agree on what success looks like up front (a test set and a target number) then measure against it. After launch we keep watching quality and cost per request against that same baseline, so “it feels better” is never the only evidence we’ve got.
Related services
Got a generative AI idea? Let’s poke holes in it.
Tell us the job you actually want it to do. We’ll tell you straight whether generative AI is the right tool, roughly what it would take to build, and where it might trip up, ideally before you’ve spent a penny on it.