Document-driven back office
Invoices, claims, applications and onboarding packs read, validated and routed, with the unclear ones sent to a person.
AI Capabilities & Services
Process Automation & RPA
Automate repetitive workflows end to end with software robots and AI.
The problem
Robotic automation is easy to demonstrate and hard to keep alive. A bot that clicks through three systems works beautifully until a screen changes, a credential expires or the input arrives in a format nobody anticipated — and then it fails silently, or worse, keeps going. Meanwhile the saving that justified it often never appears, because the automated step was not the bottleneck and the manual work simply moved somewhere else.
Who this is for
- Shared services and back-office operationsVolume handled without adding headcount, reliably enough to depend on
- Process ownersExceptions to reach a person quickly instead of disappearing
- CFOs and transformation leadsA saving that shows up in the numbers rather than in a slide
What people use it for
System-to-system transfer
Moving and reconciling data between systems that have no integration and are not going to get one.
Periodic reporting and reconciliation
The monthly assembly that takes three people two days and is the same every month.
AI-assisted steps inside a workflow
Where a step needs reading, classifying or summarising rather than a fixed rule — with human review where it matters.
What it needs to work
- The process as it actually runs, including the exceptions people handle without mentioning them
- Volume, handling time and error cost — the numbers that decide whether it is worth automating
- System access with proper service accounts, not a person's credentials
- A named owner who will keep it alive after go-live
How it works
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Trigger
An event starts it — a document arrives, a record changes, a schedule fires — with duplicates handled at the door.
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Validate the input
Before anything is processed. Most automation failures are bad input accepted as good, and the check is cheap.
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Do the work
Extract, transform, decide and write, with an AI step where the task needs reading rather than a rule.
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Route the exception
Anything unclear goes to a person with the context attached — an exception path is the part that decides whether this survives.
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Make retries safe
Idempotency, so a retry after a timeout does not create a second invoice. This is the failure that costs money.
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Log and alert
Including on silence — a workflow that processed nothing for a week should raise an alarm, not go unnoticed.
How we deliver it
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Process selection
Volume, stability and exception rate. A process whose rules change monthly costs more to maintain than it saves.
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Map it as it really runs
Observed, not as documented. The undocumented exceptions are where automation projects fail.
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Build with the exception path first
The happy path is the easy half. Building the failure route first is what makes it production-grade.
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Run in parallel, then cut over
Alongside the manual process until the numbers match, then cut over with monitoring and a rollback.
Where it runs
- On your automation platform, or built as services where no platform fits
- API integration wherever an API exists; screen automation only where none does
- On-premises or private cloud, following the systems it touches
Security and governance
- Service accounts with least privilege — never a named person's credentials
- Every run logged with what it touched and what it decided
- Human review on any step that affects a customer or a payment
- Logs scoped so a debugging aid does not become an uncontrolled copy of personal data
Timeline
One process, built with its exception path and run in parallel until the numbers agree. Parallel running is the phase clients most want to shorten and the one we most recommend keeping, because it is where you find the cases nobody mentioned in the mapping session.
What you receive
- A working automation with a defined exception and human-review path
- Idempotency, retries and error handling, tested against real failures
- Monitoring including throughput alerting, not only error alerting
- Parallel-run evidence showing the automated and manual results agree
- Documentation and handover to a named owner
Related work
Published projects where we did this.
What this does not do
We will not automate a process nobody owns, because it will break within a year and stay broken. We prefer APIs to screen automation and will say when screen scraping is the only option and what that costs in fragility. And automation does not fix a bad process — automating something unnecessary just makes it happen faster.
Questions we are asked
RPA or proper integration?
Integration where an API exists — it is more robust and cheaper to keep. Screen automation is a legitimate answer for systems that offer nothing else, but it should be a deliberate choice with its fragility acknowledged, not the default.
What happens when a system changes?
It should fail loudly and stop, not carry on producing wrong output. That is a design decision made at build time, and it is why monitoring covers silence as well as errors.
About the figures on this page
This page describes capability and method. It does not publish accuracy figures, throughput numbers or delivery dates, because those depend on your data, your systems and your scope — and a number published here would be wrong for most readers. You get them, in writing and against your own data, at scoping.
A first call is a technical conversation, not a pitch: what you have, what you need, and whether this is the right approach at all.
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Process Automation & RPA
Automate repetitive workflows end to end with software robots and AI.