AGENTIC INITIAL STUDIO
AI feasibility & prototype
Find a useful, testable AI use case before committing to a larger implementation.
Who this helps
Start with a recognisable problem.
For teams with a specific bottleneck and questions about data, model quality, costs or how much autonomy to allow.
Deliverables
Know what you will receive.
A bounded prototype with representative examples
An evaluation summary, cost assumptions and a go / revise / stop recommendation
Working process
From one task to a usable result.
- 01
Define success
Choose a measurable task and a baseline that already exists.
- 02
Inspect the inputs
Review data permissions, coverage and examples of difficult cases.
- 03
Test a prototype
Try a narrow workflow with explicit human review and failure handling.
- 04
Make a decision
Compare quality, run cost and operating effort before recommending next steps.
Before starting
What we need from you
- One task, its current process and a named reviewer
- Permitted sample data and examples of unacceptable outputs
- Budget boundaries and permitted model providers
Acceptance
How the result is checked
- Compare outputs with the agreed baseline on a held-out example set
- Record failure cases and when a human must intervene
- Document model, token-cost assumptions and deployment dependencies
Practical questions
Before defining the scope.
Does the prototype include a production system?
No. Production access controls, monitoring, scale, support and operating responsibilities need their own agreed implementation scope.
Will you guarantee model accuracy?
Quality is evaluated against task-specific examples and thresholds agreed before testing. A universal accuracy guarantee would not describe how these systems behave.