AI · Data · Research
We design and engineer artificial intelligence, data science, web and mobile products - from first experiment through to the systems your business depends on.
Four practice areas that are usually engaged together - a model is only useful once something is built around it.
Language models and agents applied to real workflows, with the evaluation and guardrails needed to trust them in production.
Turning raw and scattered data into models and decisions, including the pipelines and monitoring that keep them accurate over time.
Web products taken from discovery through to a deployed, maintainable system - not a prototype that stalls before launch.
Native and cross-platform apps built around how people actually use them: intermittent networks, real devices, real release cycles.
Questions we work on between client engagements, and that feed back into what we build.
Retrieval, tool use and agent orchestration - how far these hold up outside a demo, and where they need constraining.
Measuring model behaviour on the tasks that matter, so a change can be shipped on evidence rather than impression.
Improving results by improving data - labelling, coverage and drift - before reaching for a larger model.
Interfaces where people stay in control of automated systems: review, correction and escalation paths.
A short discovery phase to establish what success would look like and whether AI is the right instrument at all.
A narrow, measurable pilot on real data before committing to a build, so the decision to continue rests on evidence.
Documented, tested systems your own team can operate. The engagement should end without the software becoming fragile.