Services
AI Agents & Automation
Agentic systems that execute real work inside your operations, with human oversight where it matters.
Most "AI agent" demos never leave the sandbox because nobody built the boring half — evaluation, guardrails, and a clear answer to what happens when the agent is wrong. The industry data backs this up: the large majority of agent pilots never reach production, and even among the ones that do, most organisations still lack the formal governance to run them safely at scale.
The point of failure is rarely the model itself. Research into failed agent deployments traces most of it back to unclear success criteria agreed before the build started, agents given insufficient access to the tools and data they actually need, and evaluation coverage that quietly drifts once the agent goes live — not the underlying AI being incapable of the task.
We build agentic systems that take real actions inside your business — processing documents, reconciling data, orchestrating multi-step workflows across your existing tools — with evaluation, guardrails and human review built in from the first commit. Confidence thresholds route uncertain cases to people. Every agent action leaves a full audit trail.
Every engagement starts the way an evaluation harness would: one high-value workflow, a defined success metric and a measured baseline, before a line of orchestration code gets written. We run a bounded pilot against that baseline, scale only once it clears the threshold, and keep the same evaluation suite running in production afterward — so "it works" stays a number you can check, not a demo that quietly degrades once real traffic hits it.
Frequently asked
How long does an agent build typically take?
Most agent projects run three to six months from discovery to production, with working software demonstrated every two weeks — the first two to three of those spent on the evaluation harness and success metric, before orchestration work begins in earnest.
What stops an agent from making a costly mistake?
Evaluation harnesses that measure output against defined thresholds before launch, and confidence scoring that routes uncertain cases to a person rather than letting the agent decide alone. Both stay live in production, not just at launch, so drift gets caught rather than discovered by a customer.
Do you build on our existing tools or replace them?
Almost always on top of what you have. Agents connect to your CRM, ERP or internal systems rather than requiring a migration first.
Why do most agent projects fail to reach production?
Rarely the model. The failures we see trace back to unclear success criteria agreed before the build started, agents without real access to the systems and data they need, or an evaluation suite that stops running once the agent goes live — all three are fixable by defining the metric, the access, and the ongoing check upfront, which is exactly what goes into the first sprint.