Agentic AI Implementation. Production agents, embedded.
Our agents run inside your actual systems, governed, auditable, and gated by your people.
AGENTS · GOVERNED WORKERSWhat this engagement is
The difference between an AI demonstration and AI in production is governance: what an agent may access, when it must stop, who owns its exceptions, and how its work is verified. These are architectural questions, and we answer them in the architecture itself: the same platform we deploy for clients runs eight agents in production across our own operations.
Every agent operates from your Company Brain, acting on governed data, bound by your policies, and subject to human approval before anything reaches production. Its work is fully observable: every run records its inputs, its cost, and its own assessment of confidence.
Deployment follows a proven sequence: a single high-volume workflow is taken to production, measured against agreed outcomes, and then extended across the function.
The numbers behind it
What ships
Agent design & skills
Your experts' judgment captured as reusable, testable skills.
Tool integrations
Typed, scoped actions into your systems, with every call logged and revocable.
Gate placement
Escalation paths and approval thresholds defined before launch.
Run observability
"How this was made" panels for every run: inputs, confidence, cost.
Evaluation harness
Golden sets and regression suites so agent changes ship like code.
Production operations
SLOs, cost budgets and drift monitoring from day one.
How the engagement runs
Six phases from one selected workflow to agents that run and write back.
Proof from production
From hours of BIM drudgery to minutes
“Agents grounded in the project ontology now run the BIM automation engineers used to do by hand, compressing hours of modelling work into minutes, with humans gating every release.”
Questions teams ask
Which models do the agents run on?
Whichever fits the job. The skills, policies and index sit above model choice, so models can be swapped without rewriting the agent. You own the model decision, not the vendor.
How do you handle hallucinations?
Grounding plus gates. Agents retrieve from your governed index and carry a traceable rationale for every run; anything consequential passes a human checkpoint before it acts. Trust is a process, not a model setting.
Do we own the agents at the end?
Yes. Skills, policies, evaluation suites and platform configuration are handed over as versioned artifacts, with no licence dependency on us to keep them running.
How do agents respect our permissions?
The index carries permissions with the data. An agent answering on someone's behalf can only see what that person could see, because enforcement happens below the agent rather than inside its prompt.
Pick your function. Own the intelligence behind it.
Discover one opportunity, engineer one capability and deliver one measurable outcome, then scale.