AI & Automation
Automation that removes real toil, wired into the systems you already run — with evaluation rigorous enough to trust. Pragmatic AI, not a demo.
The gap between an impressive AI demo and a system you can depend on is mostly evaluation, observability, and integration — the unglamorous parts. That is exactly where we work. We target genuine toil, wire solutions into your existing systems, and measure whether they actually help.
From retrieval over your own knowledge to agents that take bounded actions to pipelines that keep models honest in production, we treat AI as software that happens to be probabilistic: tested, monitored, and reversible.
Anyone can demonstrate an AI feature; far fewer can prove it still works tomorrow. We invest disproportionately in evaluation harnesses — golden sets, regression suites, drift detection — because they are what turn a demo into a system the business can trust. If we cannot agree how to evaluate it, we will not ship it.
Token bills and tail latency are not afterthoughts. We design for both from the first prompt: caching layers, smaller models where they suffice, retrieval over fine-tuning where it earns the right, and observability that surfaces a regression in either dimension within hours rather than weeks.
What we deliver
Workflow automation
Identification and automation of high-toil, high-volume workflows, with humans kept in the loop where judgement matters.
Retrieval & agents
Retrieval over your own data and bounded, auditable agents wired into the tools your team already uses.
Evaluation & MLOps
Evaluation harnesses, monitoring, and the operational scaffolding that keeps a model trustworthy after launch.
Outcomes
- Toil removed where it actually accumulates
- AI integrated into real systems, not a sandbox
- Evaluation you can defend to a sceptic
How we engage
We begin with a value-targeting workshop: where toil actually accumulates, where decisions are slowed by retrieval, where existing automation is brittle. The output is a shortlist of candidates with effort, value, and risk scored honestly — and the candidates we recommend not to pursue.
Builds run in tight iterations with evaluation in place from the first prototype. Each release passes a quantitative bar before it ships, and observability is wired in alongside the feature, never as a follow-up.
Operations matter as much as the build. We hand over evaluation suites, monitoring dashboards, and the practices that keep a model honest as data and prompts drift. Or we operate it for you, with quarterly reviews that include human-in-the-loop measurements alongside the technical ones.
Frequently asked
Frontier models or open weights?
Both, and we are not religious about it. Frontier models for capability-bound problems; open weights where data residency, cost at scale, or fine-tuning earn the operational complexity. The decision is made per workload, not per programme.
Are agentic systems ready for production?
Bounded agents in narrow domains are ready and useful. Open-ended agents at the edge of the model's competence are mostly demoware. We will help you find the line and stay on the right side of it.
How do you handle data and IP concerns?
Carefully, and with the legal and security architectures in writing before any data leaves your environment. Retrieval-first designs, contractual data controls, and the option of fully self-hosted inference are part of every engagement involving sensitive material.
Begin a conversation → about ai & automation, or speak with a senior engineer about where it fits your wider estate.