Not every task needs AI. Exact rules may be more reliable for deterministic decisions. Models are useful with unstructured text when the result can be checked.

What is included

A process map, data requirements and responsible owners.
A pilot using agreed examples and quality criteria.
Integrations, execution records and failure handling.
Access controls, usage budgets and escalation to a person.

How we work

Measure manual work and recurring sources of error.
Compare rules, existing services and AI on representative examples.
Run a bounded pilot and assess quality with the team.

How we estimate cost

Implementation and ongoing API, infrastructure, support and review costs are estimated separately. The decision depends on total cost and output quality, not just generation speed.

What to bring to the conversation

Bring anonymised examples, your current systems and data-handling rules. Identify who can confirm whether the result is correct.

Before we start

Can a process run without human involvement?

That depends on the task and the consequences of mistakes. Important decisions and external actions should include review and explicit approval.

Do we need a custom model?

Not necessarily. We first assess existing tools and ordinary rules against quality, data, latency and cost requirements.

How do we measure value?

Compare handling time, correction rates and total cost before and after the pilot. Agree metrics and measurement methods upfront.