AI features your users will use.

We add AI to your app, SaaS product or Shopify store where it makes the product faster or clearer to use. Every feature has guardrails, usage limits and testing built in.

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Smarter in-app search
Faster user workflows
Controlled model costs

Models and tools

OpenAIClaudeGeminipgvectorSupabasePostgreSQLNext.jsNode.jsVercel AI SDKShopify

Many AI features are added for a launch announcement, then ignored because they do not solve a real task. We start from what your users struggle with, prototype quickly, and only ship features that pass testing.

AI features worth building.

Each feature is chosen because it saves your users time or effort, then built with limits that keep costs and risks in check.

Smart search

Users search in plain language and find the right record, product or document, even when they do not use the exact words.

Summaries

Long threads, reports, calls or documents are condensed into short summaries with links back to the source, so users can check the detail.

Recommendations and tagging

Products, articles or next steps are suggested from behaviour and content, and new items are tagged and categorised automatically.

Writing help

Users draft descriptions, emails or replies inside your product in their chosen tone, then edit before saving.

Document extraction

Uploaded PDFs, images and forms are read and turned into structured fields, with anything uncertain highlighted for the user to confirm.

In-app assistant

An assistant that answers questions about your product and the user's own data, limited to what that user is allowed to see.

Done properly.

The details that decide whether AI in your product pays off.

Guardrails built in from day one.

Each feature has clear limits on what it can see, say and do. We filter inputs and outputs, defend against prompt injection, set usage caps per user and log every request so problems can be traced.

AI feature checks

Working

Define allowed inputs and outputsDone
Add prompt injection defencesDone
Set per-user usage limitsDone
Run evaluation test setLive
Review results with youNext

Measured against real examples.

We build a test set of real inputs and expected outputs, then score each model and prompt against it. A person reviews the results, and the same tests run again whenever a model or prompt changes.

Evaluation summary

Example

The new prompt gives clearer summaries on long support threads and handles mixed-language messages better. It still misses refund requests buried in long replies. Suggest adding examples of these before release.

Checked by a person before it goes out

How it works.

Clear steps, and you approve each one before we move on.

01
Discover
We find the tasks where your users lose the most time.
02
Prototype
We build a quick working version and test it on real data.
03
Build
We add guardrails, limits, logging and a clean interface around it.
04
Evaluate
We test against real examples and recheck whenever models change.

Example project

A property management platform with hard-to-search records

A property management platform holds years of tenant notes, maintenance logs and documents. Staff struggle to find past issues for a property, and support spends time answering how-to questions about the product.

What we would do

Index notes and documents with access rules
Add plain-language search across each account
Summarise a property's history on one screen
Add an in-app help assistant

An illustration of how we would approach it, not a client result.

What you would get

Search that understands plain questions
Property history summaries in one click
A help assistant inside the product
Usage limits and request logs

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Related work.

Projects where we did this kind of work.

Own product · iOS and Android app

RepCam AI

A fitness app that counts your reps through the camera, with challenges and daily drops.

Read the case study

Own product · Web app

InfinityFX

Forex market analysis with AI-written explanations and Stripe subscriptions.

Read the case study

More workSee everything we've built.Client projects and our own products, with what we did on each.See all work

Questions, answered.

Which AI models do you use?

We work with OpenAI, Claude and Gemini models and choose based on the task, cost, speed and where data is processed. Features are built so the model can be swapped later without rewriting your product.

What happens to our users' data?

We use API settings where data is not used to train models, send only what each request needs and keep data separated by account. We document every data flow so you can meet your UK GDPR obligations.

How do you control AI costs?

We pick the smallest model that does the job well, cache repeated requests and set usage limits per user or plan. A simple usage report shows what each feature costs, so spending is easier to predict.

How much does an AI feature cost to build?

Each feature is quoted after a short call, with a fixed price agreed before we start. Model usage is billed by the provider, and ongoing tuning can run on a fixed monthly fee.