Generative AI Development
Generative AI feature development
We build a generative feature into your product — drafting, summarising, or generating content — evaluated before it goes live.
The problem
Your team has agreed that a generative feature belongs in your product. Maybe it drafts a response, summarises a report, or generates a product description from a few fields. The idea is clear. What is less clear is how to build it so the output is actually good enough to ship — not good enough in a demo, but good enough when a real user is watching it produce something they will put their name on. Choosing a model is the easy part. Defining what good looks like, building an evaluation set, and iterating until the results are reliably useful — that is the work that usually gets skipped, and it is why generative features get pulled back after launch.
Your product does in seconds what took a human twenty minutes.
Built for: Product teams who have identified a specific generative use case and need it built properly.
What we deliver
Feature scoping. We define the exact generative task, the inputs it will receive, and what output quality has to look like before we write a line of code. That definition becomes the evaluation target.
Model selection. We test the models that are genuinely competitive for your task — not whichever one is currently in the news — and pick the one that balances output quality, cost per call and latency for your specific use case.
Evaluation before launch. We build a small, honest evaluation set from your real data and run every candidate output through it. You see pass rates, failure modes and edge cases before any user does.
Integration into your product. The feature ships inside your existing interface — not as a separate tool your users have to learn — with the same auth, the same data access and the same error handling as the rest of your app.
More in Generative AI
Generative AI Consulting
You stop guessing and start with a short, honest list of what to build first.
Generative AI Integration
Your existing tools get smarter without a migration project.
LLM Development
You know exactly what you are running, what it costs, and where your data goes.
Not sure which of these fits? See the whole generative ai practice, or read what we build for your industry.
Tell us what’s slow.
Describe the job eating your team’s day. We’ll tell you straight whether an agent is the right fix — and if it isn’t, we’ll say so.
Frequently asked questions
- Can you guarantee the output quality?
- No. Generative models produce different outputs each time, and quality depends heavily on how varied your inputs are. What we can do is define a measurable quality bar with you before we build, run the feature against real examples until it clears that bar, and tell you honestly if it cannot get there.
- Do you build for images as well as text?
- Yes. Image generation, captioning, and layout description are all things we have worked on. The evaluation approach is different — human review plays a bigger role than automated scoring — but the scoping and build process is the same.
- What if the feature is not good enough after we build it?
- That is why we define the quality bar before we start and evaluate throughout rather than at the end. If the model cannot reliably meet the bar your use case needs, we tell you mid-build rather than shipping something that will embarrass your product.