We build bespoke agents and AI products where an off-the-shelf tool doesn’t fit the workflow, or the workflow is your edge. Everything runs on infrastructure and licensing you control.
Trusted by
Every build starts from a measured baseline and stays inside your infrastructure the whole way through.
Each agent is composed of documented skills that read your systems through the connector layer, with a human checkpoint wherever a mistake would cost something. We measure the baseline first, so you know exactly what the build has to beat.
You get:
• Agents built from skills your team can inspect and reuse
• A human checkpoint placed where a mistake would actually cost you
• Internal and customer-facing builds handled by the same approach
Evaluation sets test behaviour before volume, monitoring covers quality as well as uptime, and the model has explicit rules for when to refuse, escalate, or hand a task back to a person.
You get:
• Behaviour tested against known cases before it reaches real volume
• Monitoring on quality, not just whether the system is up
• A rollback path, so a bad skill version is a five-minute fix
We model inference, human review, storage and retry rate at your measured volume, before the build starts. Where a smaller model, a cached path, or a deterministic script gets the same result for less, we build that instead.
You get:
• A cost-per-execution figure you can check against reality
• The cheaper option built when it does the same job
• Candidates dropped at the design stage when the economics don’t work
Code, prompts, skills, configuration, evaluations, and documentation all live where your team can reach them, on your cloud and your licensing route. Your engineers work alongside ours from the start, and we train the team that owns it once we’re done.
You get:
• Everything in your own repositories, with no lock-in to us
• Our engineers working alongside yours
• A trained owning team and a runbook
Permission model, human control specification, and audit logging move in parallel with the build rather than after it. Each use case is classified against the AI Act risk tiers as it’s designed, with the reasoning handed to your counsel.
You get:
• Governance work running alongside the build
• An AI Act risk classification with the reasoning attached
• A transparency classification decided before anything customer-facing ships