AI Integration for Existing Products and New Builds
Adding AI to a product is a product problem before it is a model problem. We work out where a model genuinely removes work for your users, build that feature properly, and leave the rest alone.
What we actually build
The AI features we ship are ordinary product features that happen to call a model. Generating a listing from a photograph and a short description. Turning a booking confirmation email into a structured trip. Drafting a reply to a customer message for a human to approve. Forecasting demand from historical sales.
Not all of that is a language model. Demand forecasting, anomaly detection and customer lifetime-value prediction are better served by classical statistical and machine-learning methods — Prophet and scikit-learn — than by prompting. Choosing correctly between the two is most of the value we add.
Common integrations
- Assistants and drafting, built on the Claude API
- Document and email parsing into structured data
- Content and listing generation from minimal input
- Demand forecasting and anomaly detection (Prophet, scikit-learn)
- Customer lifetime-value prediction
- Retrieval over your own data, so answers cite your content
Where AI does not help
A discovery sprint with us includes an honest assessment of where AI adds cost and no value. Plenty of workflows are better served by a well-designed form, a saved filter or a scheduled report. A model that is right eighty per cent of the time is unhelpful in a flow where being wrong is expensive, and no amount of prompt work changes that.
We would rather tell you this in week one than bill you for it in month three.
Running it in production
An AI feature in production has to behave when the model is slow, rate-limited or unavailable, and it has to cost a predictable amount per user. We handle timeouts and fallbacks, cache what can be cached, keep prompts in version control, and put a ceiling on per-request spend so a traffic spike does not become an invoice surprise.
Where output quality matters, we build an evaluation set early, so a prompt or model change can be measured rather than guessed at.
Common questions
Can you add AI to an app we already have?
Yes, and it is a common request. We read the existing codebase, identify where a model would remove real work, and integrate the feature without a rewrite. The assessment is part of the discovery sprint.
Which models do you use?
Mostly the Claude API for language tasks, and classical machine learning — Prophet, scikit-learn — for forecasting, anomaly detection and prediction. The choice follows the problem rather than the fashion.
What does an AI feature cost to run?
It depends on volume and how much context each request carries. We estimate per-request cost during discovery and design the feature with caching and spend limits, so the running cost is known before launch rather than discovered afterwards.
Will our data be used to train a model?
Not by us, and not by the API providers we use under their standard commercial terms. Where data sensitivity is a concern we scope what leaves your infrastructure before any code is written.
Tell us what you are building
Most projects start with a one-week discovery sprint. You get a scope, a technical plan and a costed roadmap — yours to keep, whoever builds it.