Services
AI systems, built and run inside your company
Eight service lines, one way of working. Each engagement ends with a system in production, the evals that show it works, and a team that can run it without us.
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01
AI agents in production
We design, build and run AI agents that work inside your systems. They read, decide, call tools and hand the case to a person when a rule says so.
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02
Agentic workflow automation
We rebuild back-office processes as workflows. Fixed steps stay as code, agents handle the steps that need judgment, and people approve the exceptions.
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03
AI software factory
We set up coding agents across your delivery pipeline, from spec to merge. Your engineers own the specs, the review gates and the releases.
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04
Claude and OpenAI team enablement
We roll out Claude, ChatGPT, Claude Code and Codex to your teams, then teach each team to use them in its daily work, with admin setup, data rules and a library of tested workflows.
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05
Legacy code modernization
We use coding agents to document, test and migrate legacy systems in small, verified steps, from COBOL and old Java to aging .NET and monoliths.
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06
MCP and systems integration
We connect agents to your systems through Model Context Protocol (MCP) servers and APIs, with real authentication, scoped permissions and audit logs.
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07
Evals, security and AI governance
We measure whether your AI systems work, test how they fail and document them for auditors, with evals, red-team tests, tracing and compliance mapping.
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08
AI strategy and operating model
We help leadership pick the AI work that pays off, decide what to build or buy, and set up the operating model that keeps AI systems owned and measured.
Where to start
Find the service for your problem
Most clients come to us with a problem, not a service name. This is how they usually map.
- A queue or inbox that never empties AI agents in production
- A back-office process with many manual steps Agentic workflow automation
- Engineers who spend their days on routine changes AI software factory
- Claude or ChatGPT licences that few people use Claude and OpenAI team enablement
- A legacy system that few people understand Legacy code modernization
- Agents that cannot reach your data safely MCP and systems integration
- A pilot nobody can prove works, or an auditor asking Evals, security and AI governance
- Many ideas and no clear first project AI strategy and operating model
Standards
What every system we ship includes
These are part of the build, not extras. If one is missing, the system does not go live.
- 01 Evals
- A suite of real cases runs on every change to prompts, tools or models.
- 02 Tracing
- Every step, model call and tool call is recorded in OpenTelemetry format.
- 03 Approvals
- Actions that move money, change records or contact customers need a person until the data says otherwise.
- 04 Permissions
- Each tool has a written scope. Agents act with the user's rights or a narrow service identity.
- 05 Budgets
- Cost, token and latency limits per run, with alerts before they are reached.
- 06 Hosting
- Your cloud account or VPC by default. Open-weight models when data must stay on your hardware.
- 07 Models
- Chosen per task on measured quality, latency and cost. Switching is a configuration change.
- 08 Ownership
- Code, prompts, eval sets and infrastructure live in your repositories from the first day.
- 09 Handover
- A runbook, training for your team and a written exit plan in every engagement.
Tell us which process you want to hand to an agent
A 30-minute call with an engineer. We will tell you whether an AI system is the right tool for it, and what it would take to run it in production.