Workflow discovery
Choose a bounded task and define success, escalation and failure conditions. Establish a baseline before deciding whether an agent or a simpler automation fits the work.
From answering questions to getting work done.
A useful agent needs more than a conversation. It needs a defined task, reliable access to the right tools and clear limits on what it can do. We design AI workflows around those boundaries, starting with a measurable business problem.
Discuss your projectWhat we can build together.
Tools and knowledge
Connect approved APIs, documents and business systems. Define which data the agent can read, what it can change and how access is controlled.
Human checkpoints
Separate preparation from consequential action. Include approvals, validation, error recovery and activity records so people can review and intervene.
Evaluation and rollout
Test realistic cases, edge conditions and tool failures. Track task outcomes, latency and usage costs, then release gradually with monitoring and a clear fallback.
From brief to working product.
We map the workflow and identify its risks before building a prototype with representative data. We agree evaluation cases, permissions and approval points, then test the agent against those cases. Production access follows an agreed rollout plan with monitoring and a responsible owner.
Try the delivery concierge demonstration: it reads a sample order, checks a simulated slot and waits for your approval. It does not access a real customer system or change a real delivery.
Try the approval-based agent demoBefore we begin.
How is an agent different from a chatbot?
A chatbot primarily exchanges information. An agent can use tools to carry out a defined workflow. That added capability requires explicit permissions, validation and approval rules, rather than unrestricted access.
Can an agent work with our CRM?
Where suitable APIs and permissions exist, yes. We scope the specific records and actions required, test with representative data and define what needs human approval before enabling changes.
Can you guarantee every AI response is correct?
No. We design around model uncertainty using constrained tasks, validation, evaluations and human escalation. Suitability depends on the consequence of an incorrect action and the available checks.
How do you estimate an AI project?
We separate discovery, implementation and ongoing operating costs. Model usage, connected tools, evaluation requirements and support all affect the estimate. Custom agent work is scoped separately from website packages.