One clearly defined task
Instructions and task boundaries agreed around a specific workflow.
AI agents, built
for your workflow.
Turn questions and documents into useful next steps, with clear boundaries and a person in control.
Start with one task. Learn what works before extending it.
See how one request
moves through the system.
An illustrative invoice question shows where the agent helps and where a person takes over.
Example request
Where should I send this supplier invoice?
What the numbers show
- 1Receive the questionIdentify the request and check whether the person has access to the relevant guidance.
- 2Find approved guidanceFind the invoice policy and show the source behind the answer.
- 3Prepare a next stepSuggest the correct route. Any action in another tool stays within agreed permissions.
- 4Ask when it’s unclearIf guidance is missing or contradictory, explain the gap and hand over to a person.
What we build
around your task.
We agree the scope together. These are the parts that make an agent useful in everyday work.
Useful when
- Answer repeated questionsHelp people find answers in approved guidance, with sources they can check.
- Read and summarise documentsExtract useful information, prepare a summary and flag details that need attention.
- Prepare work across toolsBring relevant context together and prepare the next action without giving the agent unrestricted access.
Approved data and tools
Access to approved information and tool actions, limited to what the task needs.
Review and handover controls
Sources, review steps and a clear handoff when the agent cannot proceed.
Tests and ongoing checks
Test examples your team can repeat, plus a plan to track quality, failures and running costs.
Start small.
Test the important parts.
We work through the uncertainty before expanding the system.
Agree on one useful task
Map the current flow, users and exceptions. Define what the agent can do and what stays with a person.
You leave withA scope with clear success criteria
Test with representative examples
Check useful answers, missing information and failure cases. Compare the approach with a simpler rules-based workflow.
You leave withEvidence to guide the build decision
Connect and prepare for use
Build the agreed integrations, review controls and monitoring. Document the system and agree how it will be maintained.
You leave withA release and support plan
Questions before we start.
Sometimes a simpler workflow is enough.If every step follows a clear rule, conventional automation may be easier to test and maintain. We’ll help you make that choice before building an agent.Compare AI and automationHow is an AI agent different from a chatbot?
A chatbot is a way to have a conversation. An agent may also retrieve information and use tools to carry out a defined task. The useful distinction is what the system is allowed to do, not the name on the interface.
Can an agent use our internal documents?
That can be part of the scope. We first review the document sources, access permissions and how information stays up to date. Answers should point to supporting material, and missing information should lead to a clear handoff rather than a guess.
Will it work without human review?
We decide that task by task. Actions with consequences may need approval, while a low-risk step can sometimes run automatically. We define the permissions, review points and stop conditions before implementation.
How do you check whether it is working well?
We agree on representative tasks and failure cases, then test answer quality, tool use and handoffs. Monitoring after release can help identify changes in behaviour, usage and running cost. The checks depend on the task rather than a blanket accuracy promise.
Can you add an agent to an existing product?
Yes, where the product’s interfaces and access controls support it. We review those constraints first and can begin with a contained workflow rather than changing the whole system.
Two ways in.
Both reach a person.
Both reach the studio directly.