Workflow discovery
Understand the business objective, task, inputs and outputs. Define complexity, edge cases, data needs, volume, deadlines and acceptance criteria.
Delivery approach
Managed AI data operations require clear instructions, prepared contributors, quality checks and agreed acceptance criteria.
Our proposed delivery approach follows six stages.
Understand the business objective, task, inputs and outputs. Define complexity, edge cases, data needs, volume, deadlines and acceptance criteria.
Turn the workflow into instructions, examples, decision rules and escalation procedures. Resolve ambiguity with subject-matter experts where needed.
Select and train contributors for the task. Use examples and practice exercises, then assess against task-specific readiness criteria.
Assign and track work against the agreed process. Escalate unclear instructions, exceptions and unusual cases.
Check outputs using controls suited to the task. Categorise, correct or escalate errors under agreed rules.
Submit deliverables against acceptance criteria. Use feedback and rejection reasons to identify recurring errors, training gaps and unclear instructions.
Start small. Measure before scaling.
Measure actual quality, throughput, rework, turnaround and cost per accepted output.