A clear path.
A focused first step.
Define the problem, test the solution, and evaluate the result before expanding.
Start focused.
Build with confidence.
Understand
Clarify your business problem or model task, constraints, and success criteria.
Design
Define the workflow, data requirements, review points, and delivery scope.
Pilot
Test a focused implementation or sample dataset before expanding.
Evaluate & improve
Review results against agreed criteria and plan the next iteration.
Agree on success before implementation.
For business AI, that may mean a completed workflow, useful answers, or less manual handling. For training data, it may mean label consistency, coverage, and conformance to an agreed schema. The criteria should reflect your actual task.
Keep review points visible.
Identify approvals, exceptions, and sensitive data early. Document the boundaries of an AI workflow and the decisions that remain with your team.
Use a pilot to resolve uncertainty.
Begin with a bounded workflow or sample dataset. Review edge cases, confirm the delivery format, and agree what must change before the next stage.
What to bring to the first discussion
Your goal, existing tools or data sources, a representative example without confidential information, expected timeline, and any access or quality requirements.
Prepare a project brief ↗A clearer starting point.
Where should we start if we're new to AI?
Start with one repeated task and a clear definition of success. An initial discovery can identify the available data, tools, risks, and whether automation is appropriate before committing to a wider rollout.
Can we discuss AI implementation and data services together?
Yes. A project may need both a business workflow and better underlying data. The scope can cover both, with separate deliverables and evaluation criteria for each.
How is training data quality defined?
Quality depends on the task. Agree on annotation guidelines, sampling methods, review procedures, coverage, and acceptance criteria before production. A sample batch helps validate those requirements.
What about sensitive or proprietary data?
Data handling, access, permissions, retention, and any confidentiality requirements should be agreed before sharing sensitive material. Please keep confidential data out of the initial project brief.