Delivery approach

A defined workflow. A measurable output.

Managed AI data operations require clear instructions, prepared contributors, quality checks and agreed acceptance criteria.

Our proposed delivery approach follows six stages.

01

Workflow discovery

Understand the business objective, task, inputs and outputs. Define complexity, edge cases, data needs, volume, deadlines and acceptance criteria.

OutputA scoped workflow and pilot plan.
02

Task design

Turn the workflow into instructions, examples, decision rules and escalation procedures. Resolve ambiguity with subject-matter experts where needed.

OutputTask guidelines, evaluation criteria and quality checks.
03

Workforce preparation

Select and train contributors for the task. Use examples and practice exercises, then assess against task-specific readiness criteria.

OutputReadiness assessment and production allocation plan.
04

Controlled execution

Assign and track work against the agreed process. Escalate unclear instructions, exceptions and unusual cases.

OutputCompleted work with traceable task status.
05

Quality assurance

Check outputs using controls suited to the task. Categorise, correct or escalate errors under agreed rules.

OutputQuality-reviewed deliverables and documented exceptions.
06

Customer acceptance and improvement

Submit deliverables against acceptance criteria. Use feedback and rejection reasons to identify recurring errors, training gaps and unclear instructions.

OutputDeliverables that meet criteria and an improvement plan where needed.

Start small. Measure before scaling.

Use a pilot to establish what the workflow can deliver.

Measure actual quality, throughput, rework, turnaround and cost per accepted output.

Scope a Pilot →