01 / AI agent development
AI agents built around real work.
We design, integrate, evaluate, and deploy AI agents for defined business workflows, with human approvals, monitoring, and clear ownership.
Start with the workflow, not the model.
An agent is useful when it can do a defined job inside a real system. We map the inputs, decisions, tools, exceptions, owners, and success measures before choosing a model or writing integrations.
- Repetitive operational work with clear volume
- Work crossing CRM, support, documents, or databases
- Decisions with explicit approval points
- Data and system owners who can test the result
What arrives with the system.
- 01Workflow map and success criteria
A bounded first workflow, its owners, exception paths, and the measure that should move.
- 02Architecture and integration plan
A clear system boundary across models, tools, data, permissions, and existing software.
- 03Working agent for one workflow
A focused implementation that can be evaluated before it is trusted with live work.
- 04Evaluation set and acceptance criteria
Representative cases, expected behaviour, failure paths, and regression checks.
- 05Guardrails, monitoring, and handover
Human approvals, logs, runbooks, operating limits, and ownership your team can keep.
02 / Process
Small scope.
Visible proof.
One workflow gives the system somewhere real to earn trust before it expands.
- 01
Discover the workflow
Map inputs, decisions, tools, exceptions, owners, and the current cost of the work.
- 02
Define the first scope
Choose one production-shaped workflow instead of promising a general-purpose agent.
- 03
Build the evaluation set
Agree on representative examples and what good, unsafe, and uncertain behaviour look like.
- 04
Integrate and implement
Connect the necessary systems and keep permissions narrow, explicit, and observable.
- 05
Deploy and hand over
Observe the system in operation, document it, and make ownership visible from day one.
The standard is operational.
We treat the agent as part of a production system, not as a personality layered over a demo.
- 01Bounded access
Data boundaries and tool permissions are explicit.
- 02Failure paths
Uncertainty, fallbacks, rate limits, and human review have a defined route.
- 03Evaluation before release
Representative cases are tested before consequential work is automated.
- 04Ownership after handover
Logs, runbooks, cost controls, and source ownership remain with your team.
03 / Fit
A useful first job is specific.
Clear fit creates a better system and a shorter path to evidence.
Good fit
- One measurable workflow
- A real process owner
- Usable data and systems
- Willingness to test and review
Not a fit
- “Add AI” without a workflow
- Fully autonomous high-risk decisions
- Unspecified data access
- Guaranteed accuracy requirements
Questions worth answering early.
What is a custom AI agent?
Software that uses a model, tools, data access, workflow logic, and human handoffs to complete a defined business process.
How is an agent different from a chatbot?
A chatbot mainly responds. An agent can inspect records, call approved tools, route work, request approval, and log what it did.
How do you evaluate reliability?
We define representative cases, expected behaviour, edge cases, and release criteria before the system touches consequential work.
Who owns the code and infrastructure?
The engagement is designed for handover. Ownership, access, operating limits, and documentation are made explicit before launch.
Bring us one workflow worth improving.
Tell us where the work starts, where it gets stuck, and what a better outcome would make possible.
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