AI agents & automation
We design and build AI agents that take real actions inside your systems — scoped, evaluated, secured and cost-engineered. Agents are one of our two strongest application areas, alongside document intelligence.
Where agent projects fail
Capabilities
We start from the workflow, not the framework: which steps an agent should own, which stay human, what tools it may call and where the hard limits sit.
Agents wired into the systems work actually happens in — CRMs, ERPs, ticketing, internal APIs and databases — with scoped permissions and full audit trails.
Confidence thresholds, approval checkpoints and clean escalation paths, so an uncertain agent asks instead of guessing.
Task-level test suites, regression checks on every prompt or model change, and monitoring that tells you when quality drifts in production.
Prompt injection defence, permission boundaries, data protection and red teaming before an agent gets access to anything that matters.
Model routing, caching, batching and smaller models where they suffice — measured against real traffic, not benchmark numbers.
Application areas
Both start the same way: a workflow worth automating, a measurable definition of done, and an honest read on what should stay human.
Internal agents that operate inside your existing controls: least-privilege access, logged actions, reviewable decisions and predictable failure behaviour.
Repetitive, rules-plus-judgement processes automated end to end, with exception handling for the cases that still need a person.
Questions
Next step
Bring the workflow, the constraints and the data. We will tell you what is realistic, what it costs and how we would build it.
