AI-Enabled Automation
Rule-based automation handles the path everyone described in the workshop. The cost lives in the exceptions nobody mentioned — the supplier whose invoice layout changed, the case with a missing field, the request that does not fit any category. Automating the happy path is the easy tenth of the problem.
Where automation quietly fails
Three patterns recur. Bots built against screen positions break the moment a vendor ships a UI change, and the maintenance burden grows with every workflow added. Automation is applied to a process that should have been simplified first, which encodes the dysfunction and makes it harder to change later. And exception handling is treated as a phase-two concern, so the system automates seventy per cent of volume and hands the remaining thirty to a team with no tooling, no queue and no context — frequently costing more in aggregate than the manual process it replaced.
The three calls that decide the outcome.
Made explicitly, with the trade-off written down, before anything gets built.
Template extraction or layout-aware models
Template-based extraction is accurate and cheap when documents genuinely share a fixed layout, and it fails completely on the first variant. Layout-aware models generalise across formats at the cost of more setup and a confidence distribution you have to manage. The deciding question is how many distinct senders and formats you actually see, and how often new ones appear — a stable set of ten suppliers is a different problem from an open inbox.
Where the confidence threshold sits
The important design choice is not the model's accuracy but what happens in the uncertain band. A single automate-or-block threshold forces you to trade false positives against missed volume. Routing high-confidence cases straight through, low-confidence cases to review and the middle band to a queue with the extraction pre-filled turns the model into an accelerator rather than a gate. We built this pattern into the moderation service, where each surface sets its own threshold and grey-band content goes to a human queue instead of being auto-blocked — which is what keeps false positives low enough for the team to trust it.
Mine the process before automating it
Process mining against system logs routinely shows that the documented process and the real one diverge, that most volume follows a small number of variants, and that a meaningful share of steps exist to compensate for an upstream problem. Automating before you know this encodes the workaround permanently. Often the highest-return finding is that a step can be removed rather than automated.
The capabilities we bring on day one.
Each engagement assembles from this menu, sized to your scope, paced to your calendar.
Layout-aware extraction
Document intelligence that generalises across formats instead of breaking on the first new sender.
Confidence routing
Straight-through, review queue or pre-filled middle band — sized to your tolerance for each error type.
Process mining first
The real process from system logs, so you automate the workflow that exists rather than the documented one.
Exception tooling
The queue, context and audit trail for the cases that do not automate, designed in phase one.
Oversight that is real rather than nominal
Where automated decisions affect people, both GDPR's provisions on automated decision-making and the EU AI Act's human-oversight requirements assume a person who can genuinely intervene — not a reviewer approving a queue of pre-approved outcomes at a rate that makes real scrutiny impossible. That has design consequences: the reviewer needs the evidence behind the recommendation, enough time, and a route to disagree that is actually used. We instrument override rates specifically, because an oversight step nobody ever overrides is usually evidence that the oversight is not real.
From kickoff to live in four phases.
Each phase has named deliverables, named owners and a named gate, and every one respects the systems you already have in place.
Discover
Process mining against real system logs, volume by variant, and where exceptions and rework actually accumulate today.
Design
What to remove versus automate, extraction approach, confidence thresholds per decision type, and the human oversight model.
Build
Extraction and decision services, exception queue and tooling, and integration with the systems of record that hold the outcome.
Run & hand off
Monitoring on straight-through rate, override rate and exception ageing, plus retraining as new formats and patterns appear.
What a good fit looks like.
Stated up front, so neither of us spends a call finding out this was the wrong conversation.
Engagement shape
A scoped sprint of roughly eight weeks on one process, sized so straight-through rate can be measured against a real baseline.
From your side
System logs or process data for mining, a representative document sample including the awkward ones, and the operations owner of the process.
When not to hire us
If the process is about to be redesigned or the underlying system replaced within the year, wait. Automating a workflow that is scheduled to change is the most reliable way to waste the budget.
AI-Enabled Automation questions, answered.
Classical RPA follows deterministic rules against screen positions or fixed layouts, which is why it breaks on UI changes and cannot handle documents it has not seen. Adding models lets the system read documents that vary, classify requests that do not match a rule, and route uncertainty to a person — so the exceptions become manageable instead of becoming someone's full-time job.
They go to a designed exception path, not a spreadsheet. High-confidence cases run straight through, low-confidence cases route to review, and the middle band goes to a queue with the extraction pre-filled so the reviewer is correcting rather than re-keying. Exception tooling is phase-one work; treating it as phase two is the most common way these programmes end up costing more than the manual process.
If you have the system logs, it usually pays for itself. It reliably shows that the documented process and the real one differ, that most volume follows a few variants, and that some steps exist only to compensate for an upstream problem. Automating before you know that encodes the workaround permanently — and sometimes the best finding is that a step can be deleted rather than automated.
By giving the reviewer the evidence behind each recommendation, enough time to use it, and a route to disagree — then measuring the override rate. Where an oversight step is never overridden, that is usually evidence the oversight is nominal rather than real, which matters under both GDPR's automated decision-making provisions and the AI Act's human-oversight requirements.