AI AGENTS
AI that does the work, not just answers.
A chatbox produces output. An agent performs work — it connects to your systems, runs the steps itself, and logs everything it touches.
What we build
- Agent design We write down what the agent does and where it stops: inputs, tool set, decision points, halting conditions.
- Tool connectivity ERP, CRM, ticketing, databases, file stores, email. We standardize the connection layer with MCP servers.
- Permissions and approvals Each agent gets its own identity and role-based scope. Irreversible steps require a human in the loop.
- Memory and context Access to institutional knowledge through RAG, with source and entitlement preserved in the index — an agent cannot retrieve a document its caller may not read.
- Evaluation and observability A test set built from real cases runs on every release. In production we track step-level traces, failure rate and cost per task.
Who it is for
- Operations teams with repetitive, rule-heavy work that never quite fit classic automation
- Support and back-office functions where ticket and document volume outgrew headcount
- Finance, procurement and HR processes that move data between systems by hand
- Organizations whose data cannot leave, and who need the agent running on their own infrastructure
How we work
- 01 Discovery We watch the process as it actually runs and separate automatable steps from risky ones.
- 02 Prototype A working agent running one process end to end, in 2-3 weeks.
- 03 Integration Real system connections, permission model, approval flows, traceability.
- 04 Production Live use with a limited team, measurement, then staged rollout.
- 05 Evolution New tools, new processes; the evaluation set grows with every addition.
Frequently asked
What happens when an agent gets it wrong?
Its scope is explicit: it can only call the tools we give it. Irreversible actions — payments, deletions, outbound messages — sit behind human approval. Every step is written to an audit trail you can replay.
Which model do you use?
It depends on the constraints. Where data cannot leave, we run the agent on an on-premises AI server system; where there is no such constraint, API models are an option. The choice is made in discovery and can be revisited later.
Do we have to change our existing software?
Usually not. The agent connects through your existing APIs and MCP servers. For legacy systems without an API, we write a thin access layer first.
How long does it take?
A working prototype for a single process is typically 2-3 weeks. The path to production depends on integration count and approval requirements, and is fixed during scoping.
We do not leave agents in the demo. We ship them.
No deck needed. 20 minutes. The rest is up to you.