AI & Machine Learning
Bring intelligence into your product with AI-powered features. From recommendation engines to computer vision, NLP, and predictive analytics, we make AI practical and production-ready.
Overview
AI is only valuable when it ships and earns its keep. We cut through the hype to build features that move your metrics, a support assistant that deflects tickets, search that actually understands intent, a recommendation engine that lifts conversion. We focus on production reliability, cost control and evaluation, not demos that fall over in the real world.
What you get
- AI features that move real metrics, not just impressive demos
- Production-grade reliability, evaluation and cost control
- LLM and ML integrated cleanly into your existing product
- A pragmatic roadmap from quick wins to deeper capability
Everything in the engagement
LLM integration & retrieval-augmented generation (RAG)
AI chat assistants & copilots
Computer vision & image processing
Recommendation & personalisation systems
Predictive analytics & forecasting models
Evaluation, guardrails & cost optimisation
A clear path from idea to launch
Discover
We dig into your goals, users and constraints to define exactly what success looks like before a line of code is written.
Design
We map flows and design the experience, validating direction with prototypes so there are no surprises later.
Build
We ship in two-week sprints, so you see working software early and steer the build as it takes shape.
Launch & Scale
We deploy, monitor and harden for production, then keep improving as real usage and feedback roll in.
Tools & technology
Common questions
Not anymore. Modern foundation models (like Claude) deliver strong results with little or no training data using prompting and retrieval. Where custom models help, we'll tell you what data you'd actually need.
We build evaluation suites, guardrails and fallbacks around every AI feature, monitor quality in production, and design graceful degradation so a model hiccup never breaks the experience.
Yes. We optimise model selection, caching, prompt design and routing to keep inference costs predictable and proportionate to the value the feature delivers.