Define useful results with real examples
Review representative work, the intended users and the consequences of an incorrect result. Agree the information the system may use and examples of useful outputs, unacceptable mistakes and unresolved cases.
AI workflows and agents
We develop AI features, workflows and agents that interpret information and carry out permitted work. The software around the model governs access, actions, state and operating controls.
How a request becomes
an authorised action.
AI can interpret a customer’s message. The application checks the account, the available information and the actions your policy permits. Here is a delivery-address example.
Ready to update
Update the order
The North office matches an approved delivery location.
The account can change its delivery location, and the order has not been dispatched.
Access
Only the approved customer records and order tools are available to the model.
Checks
The application verifies the account, order status and destination before accepting a change.
Your approval policy
You decide which changes can proceed automatically and which need a person’s decision.
Where this fits
AI extends what an application can do with documents, language and less structured information. A model can extract fields, retrieve relevant material or draft a response. An agent can investigate a request and choose its next tool as results arrive.
- Document extraction and classification
- Knowledge retrieval and assisted drafting
- Multi-stage processing and review workflows
- Agents that investigate requests through approved tools
The work involved
Choose the appropriate execution pattern
Use a model task for a defined interpretation or generation step, a workflow for prescribed stages, or an agent when choosing the next step is part of the work. A single application can combine all three.
Build the application and agent tools
Develop instructions, retrieval, output formats and tool connections. Enforce permissions in application code. You define which actions can execute directly and which require approval; we implement that policy, including the access needed to change it.
Evaluate and prepare for operation
Test complete runs against agreed examples, including missing information, unsuitable requests and connected-service failures. Record relevant actions and results, with completion, escalation and stopping conditions.
What you receive
- Integrated AI feature, workflow or agent
- Tool interfaces, instructions and operating configuration
- Evaluation examples and recorded results
- Review controls, usage visibility and operator guidance agreed in scope
How we assess the work
Application checks and AI evaluation address different questions. We verify permissions and system behaviour, then assess whether model-assisted work meets the criteria for its intended use. Test examples and thresholds are agreed for the task; there is no single accuracy figure that covers every application.
Review and acceptanceWhat we need from you
- Representative examples and approved reference material
- A process owner able to judge results and exceptions
- Decisions on permitted data access and actions
- Expected usage, supported languages and review requirements
Dependencies and scope
Model behaviour, provider availability and usage costs are operating dependencies. We define the intended task, supported information and permitted actions in the scope, together with the handling of cases the system cannot complete.
Discuss an implementation.
Tell us what you want to achieve and what is getting in the way. We’ll review the next steps with you.