The pilot never reached the operation
Something was bought or demonstrated, but it sat beside the process instead of inside it, and use decayed to nothing within a quarter.
AI workflows
Automation for the work nobody should do by hand
One defined task, automated inside a process that already exists, measured against whatever it replaced.
We build a specific workflow into an existing process to remove a task people currently do manually: extracting from documents, classifying records, matching, drafting or triage. The judgement stays with your team and the volume work does not. Every workflow is baselined before we start and measured after, in cycle time or cost per item, so the result is arguable in front of a board.
Discuss this buildWhen this comes up
What this usually looks like from the inside.
Skilled people spend their week keying data
Reading documents, matching records and chasing exceptions absorbs the capacity that should be going to judgement.
Nobody can say what it saved
Without a baseline the conversation stays theoretical, the spend is impossible to defend, and there is no case for doing it again.
The work
The work, step by step.
Scope varies with the state of your systems. These are the parts that recur.
Task selection
We identify the high-volume tasks where automation genuinely beats a person, and rule out the ones where it does not.
The build
The extraction, classification or matching step goes into the existing process, with a human check retained wherever it earns its place.
Controls and fallback
Confidence thresholds, escalation routes and defined behaviour when the model is wrong, so the process fails visibly rather than silently.
Measurement
We baseline the task beforehand, measure it afterwards and report the difference in cycle time or cost per item.
What you leave with
Everything you keep.
You own all of it: the code, the written definitions and documentation aimed at whoever maintains this after us.
- An assessment of the task and its expected saving
- The workflow running inside your live process
- Confidence thresholds and escalation rules
- A before-and-after measurement against the baseline
How this runs
Scope, timescale and what happens afterwards.
Roughly the first week
We establish which commercial question is worth answering here, and what your data, systems and people can realistically support.
The bulk of the engagement
We build it, rather than specifying it for somebody else to build. Reconciled continuously against numbers your team already accepts.
The final fortnight
Documentation, definitions and a runbook, plus a walkthrough where your team drives and we watch.
Only if you ask
Ongoing technical support is quoted separately from the build, so declining it costs you nothing.
Worth asking
Questions this tends to answer.
- Which tasks eat capacity without needing judgement?
- What happens when the model gets it wrong?
- What did this save, in numbers?