AI agent and workflow automation development
Give the agent one accountable job before giving it autonomy.
Build a useful workflow around trusted sources, representative tests, controlled actions, and a clear path back to a person.
- Typical first release
- A bounded pilot commonly takes 4–8 weeks; production expansion follows evaluation evidence and integration access.
- Engagement
- Paid workflow discovery and pilot, followed by evidence-gated production expansion.
The problem
Define the customer or operating decision first.
Generic assistants sound capable in a demonstration but fail when sources conflict, permissions matter, an action has consequences, or the request should be refused. The workflow needs boundaries before it needs a personality.
Good fit for
- Customer enquiry qualification and approved knowledge retrieval
- Internal document search, summaries, classification, and routing
- Structured extraction from recurring documents or conversations
- Controlled actions into CRM, ticketing, calendar, or reporting systems
What is delivered
A clear scope with practical handoff.
- 01
Workflow and source boundaries
- 02
Retrieval and orchestration
- 03
Structured outputs and validation
- 04
Evaluation set
- 05
Logs, cost, and latency visibility
- 06
Human handoff and runbook
Delivery path
Four stages from scope to release.
- 01
Bound the job
Define trigger, trusted inputs, expected output, actions, exceptions, and accountable human owner.
- 02
Build the evaluation
Create ordinary, missing-data, conflicting, adversarial, multilingual, refusal, and escalation cases.
- 03
Connect the loop
Implement retrieval, reasoning, validation, permissions, integrations, and visible handoff.
- 04
Observe
Launch beside the current process and measure corrections, routing, cost, latency, and completed workflow outcomes.
Related system or proof
CIOS conversation orchestration
A productized deployment pattern for approved knowledge, structured lead fields, routing rules, and human escalation.
Open the case or product explanation →Questions before scoping
Useful answers before a sales call.
Which model or provider do you use?
The choice depends on the task, language, privacy, latency, tool use, and cost. We keep business rules and evaluation separate from a single model where practical.
Can the agent write into our CRM or book meetings?
Yes where the platform API and permissions allow it. Write actions are scoped separately from read actions and may require validation, confirmation, idempotency, and audit logs.
How do you reduce hallucinations?
We constrain sources and actions, validate structured output, evaluate representative cases, expose uncertainty, and design refusal and human handoff. No implementation makes a language model infallible.
Start with context
Bring the goal, constraints, and useful context.
We will ask focused questions and recommend a practical first step.