AI integration
We add AI features that users actually rely on: search that understands questions, assistants grounded in your own content, document processing, and personalised recommendations. We choose the model and approach that fit your data, budget and privacy needs.
What is included
Add language models, smart search and recommendations to an existing product.
- Use-case assessment
- Where AI saves your users time, and where it would only add cost.
- Retrieval and grounding
- Assistants and search that answer from your own documents, with sources shown.
- Model selection
- Hosted APIs or open-source models, chosen for accuracy, cost and data control.
- Evaluation
- Test sets and quality checks, so you know how the feature performs before users do.
How it works
From first call to launch.
Find the riskiest assumption
We agree on what the first version has to prove.
Prototype
A clickable prototype or proof of concept, tested with real users or real data.
Build
Production code in two-week sprints, each ending in a working demo.
Launch and measure
Release, instrument, learn, then plan the next iteration together.
What a typical project looks like
A typical AI integration adds one focused feature to a product that already has users: summarising long records, drafting replies, extracting fields from uploaded documents or answering questions from a help centre.
We start by defining what a good answer looks like and collecting real examples. The feature sits behind a single internal service that owns prompts, model choice and logging, so it can be tested, improved or switched off without touching the rest of the product. We measure acceptance and cost per use from day one. Most first features ship in three to eight weeks.
Timeline
Typical timelines. You get a fixed written estimate after scoping, before any work starts.
- Feasibility sprint
- One to two weeks.
- First AI feature in production
- Three to eight weeks.
- Model running costs
- Paid directly by you to the model provider. We estimate cost per use before launch.
A good fit if
- You have content, data or workflows that users struggle to search or process.
- Customers are asking for AI features and you need a credible plan.
- A quick AI demo impressed in a meeting and fell apart on real data.
Questions
Which AI models do you work with?
The major hosted model providers as well as open-source models. We choose per project, based on the task, cost and where your data can go.
Will our data be used to train models?
We configure providers so your data is not used for training wherever the provider allows it, and we explain the options in plain terms.
How do you deal with wrong answers?
Grounding in your content, clear fallbacks, human review where the stakes are high, and evaluation before launch.
Related services
Often paired with AI integration.
Further reading
- Blog
How to add AI features to an existing product without a rebuild
A practical approach to adding AI to a live product: choosing the right first feature, keeping costs predictable and shipping without rewriting what already works.
- Blog
Which business workflows should you automate first?
A simple way to choose the first workflows to automate in a startup: score them by frequency, time cost and consistency, then keep a person in the loop.
- Blog
How much does it cost to build an MVP?
What actually drives the cost of a startup MVP, how to estimate it before you talk to a development team, and where founders tend to overspend.
Have something to build?
Tell us what you are working on. We will come back with questions, not a sales script.