For teams whose AI features are in front of users — and now need to be more accurate, cheaper, faster or simply more predictable.
AI systems change after launch. Usage patterns shift, data drifts, model providers update their models and costs creep up. Without a way to measure quality, teams find out about problems from their users.
We start with an audit of the system as it runs today — architecture, prompts and models, data flows, evaluation, costs and failure modes — and give you a prioritised list of improvements. Then we help you deliver them, and put evaluation and monitoring in place so quality stays visible.
We support systems we built and systems we didn't, including LLM features, retrieval pipelines, agents and classical machine learning models.
A structured review of your AI system end to end — architecture, models and prompts, retrieval, data pipelines, evaluation, security and cost — measured against how it behaves on real traffic.
Targeted work to make the system better and cheaper: prompt and retrieval tuning, model selection and routing, caching and batching, and fine-tuning or distillation where it pays off.
The measurement layer many AI products lack: curated test sets, automated and human-reviewed evaluations, and dashboards that show quality over time.
A retainer for teams who want senior AI engineers on hand: monitoring, incident response, model and provider upgrades, and regular quality reviews.
We review the system, its data and its traffic, and agree a baseline for quality, cost and latency.
A ranked list of improvements with expected impact and effort, agreed with your team.
We ship the changes with your engineers, measuring each one against the baseline.
Evaluation, monitoring and — if you want it — ongoing support to keep the gains.
Yes. Much of our support work starts with someone else's system. The audit is how we get up to speed quickly and safely before changing anything.
Read access to code, configuration and logs for the audit — ideally in a staging environment. We agree access levels up front and work within your security policies.
Usually as an audit first, followed by either a scoped improvement project or an ongoing retainer — whichever suits your system and your team.
No. We support LLM features, agents and retrieval systems, as well as classical machine learning and deep learning models.
Agentic AI, machine learning, NLP & LLMs and deep learning — designed, built and operated by senior practitioners, guided by a principal AI architect from the first framing conversation to production.
View serviceAI-assisted golden image creation, fleet deployment and OS hardening — build once, lock it down, and keep every endpoint drift-free against a hardened baseline.
View serviceAI-augmented offensive security testing paired with senior human validation — continuous attack-surface discovery, not a once-a-year PDF.
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