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AI workflow automation

We take one high volume workflow end to end into production, wired into your real systems, with evaluation, human review points and a rollback path.

Format
Fixed scope build
Duration
6 - 12 weeks per workflow
Best for
Teams drowning in high volume, rules-heavy, document-driven work

You leave with

  • One workflow live in production
  • Measured accuracy before it carries load
  • Your team operating it without us

What production looks like

The demo is easy. A model reads a document, pulls out the fields, and everyone claps.

Production is different. Production has the supplier who sends a photo of a page. The credit note that arrives before the invoice. The job code that only exists in one person’s head. The month where volume triples.

We build for that version. Most of the work is defining the exception paths, deciding where a human has to sign, and instrumenting the thing well enough that you can see it drifting before your customers do.

How we scope it

A build starts with a workflow definition, written in plain language, that answers four questions:

  • What comes in, in every form it actually arrives in
  • What has to be true for the output to be correct
  • What happens when the system is not confident
  • Who is accountable for the output once it leaves

If we cannot answer those, the workflow is not ready and we say so before you spend money on it.

Where the human stays

A reviewer sits where an error is expensive.

We place review points by the cost of an error. A misfiled internal document is cheap and can run unattended. A payment to a new bank account is expensive and irreversible, so it gets a human every time, forever, regardless of how good the model gets.

Teams that put a reviewer on every step end up with a workflow that is slower than the manual process. Teams that put a reviewer nowhere end up with an incident.

Handover is the deliverable

You end up owning this. That means an operator on your side who can read the logs, adjust the rules, retire a prompt and run the evaluation set after a change.

We train that person, write the runbook with them rather than for them, and stay on call while adoption settles.

Questions

Which workflows are the good candidates?

High volume, document or message driven, with rules that a competent new hire could learn in a fortnight, and where a wrong answer is caught downstream rather than being catastrophic. Invoice and quote handling, claims triage, supplier onboarding, inbound qualification, compliance checks.

Do you replace people with this?

In practice most clients redeploy rather than reduce. The honest version is that these builds remove a chunk of low judgment work, and what you do with the freed hours is a decision for you rather than for us.

What accuracy should we expect?

It depends entirely on the workflow, and any number quoted before we have seen your data is a sales number. What we commit to is measuring it on your historical cases before anything goes live, and telling you if it is not good enough.

What happens when a model gets deprecated?

The workflow is built with the model behind an interface and the evaluation set retained, so swapping models is a test run rather than a rebuild. That is part of the handover.

Next step

We map where your hours go and price the work that is worth automating.