Service 03
The manual step, removed.
Most businesses run on a handful of invisible tasks somebody does by hand every day — retyping an invoice, sorting an inbox, chasing a number for a Monday report. Individually trivial, collectively a full-time job.
- Timeline
- 4–8 weeks
- Suited to
- Operations with a high-volume repetitive task
Typically starts at
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Start a ProjectSee what each package includes01The problem
Why the manual work survives
It is never quite painful enough to fix and never quite simple enough to automate with rules. The data is messy, the exceptions are real, and traditional automation breaks on the first unexpected format.
Rules cannot cope with the input
Invoices in forty layouts, emails in free text, forms filled in wrongly. Deterministic parsing handles the clean 60% and dumps the rest on a person.
The systems do not speak
The data exists in three places and a human is the integration layer, copying between them and introducing a typo every hundred rows.
Nobody trusts a black box
Automation that cannot show its working does not get adopted, because the first unexplained mistake ends the experiment.
02Our approach
How we approach it
We use models for the judgement — reading messy input, classifying, extracting — and ordinary deterministic code for everything else. Most of a reliable smart system is not AI, and pretending otherwise is how these projects fail.
Measure before automating
We time the task and count the volume first. Some of what people ask us to automate is not worth automating, and we would rather say so.
Judgement where it earns its place
A model reads the document and classifies the case; validated code does the arithmetic, the writes and the routing.
Confidence-gated
High-confidence cases flow straight through. Anything uncertain goes to a review queue with the reason attached, so exceptions are handled rather than hidden.
Auditable end to end
Every record keeps its input, its decision and its confidence, so you can answer 'why did it do that' months later.
Messy input — invoices in many layouts, free-text email, imperfectly filled forms — is read and classified by a model. Everything after that point is ordinary deterministic code: the extracted values are validated, then a confidence gate splits the flow. High-confidence cases pass straight through to your systems. Anything uncertain goes to a review queue with the reason attached, so exceptions are handled rather than hidden. Every record keeps its input, decision and confidence score in an audit log.
03Deliverables
Exactly what you receive.
No vague line items. This is the list that goes into the agreement.
Analysis
- Workflow mapping with timings and volumes
- Sample set covering the real edge cases
- Accuracy targets and confidence thresholds
- Honest assessment of what is not worth automating
System
- Extraction and classification pipeline
- Integrations with the systems involved
- Review queue for low-confidence cases
- Audit log with decisions and confidence scores
- Alerting on failure, drift and backlog
Handover
- Accuracy report against the sample set
- Operating dashboard for volume and exceptions
- Runbook and rollback procedure
- Training for the team running it
04How it runs
Five stages, each with a clear exit.
- 01
Mapping
We follow the work as it actually happens, including the undocumented steps people invented to cope. The real process is never the one on the slide.
- 02
Sampling
A representative set including the awkward cases, labelled so accuracy can be measured rather than asserted.
- 03
Build
Pipeline, integrations and review queue, evaluated against the sample throughout.
- 04
Shadow run
It runs alongside the humans without touching anything, so you can compare outputs before trusting it.
- 05
Cutover
Switched on with monitoring, thresholds and a rollback path, then tuned once real volume arrives.
05Related work
How this looks in practice.
06Questions
Intelligent Automation, answered.
We set the target during scoping and report measured accuracy against your labelled sample before cutover. Where the target is not reachable, the confidence gate sends the remainder to review — which is usually still a large saving, and an honest one.
In our experience it removes the tedious fraction of several roles rather than a whole one. We scope it as capacity recovered, and we will tell you plainly if a task looks like a headcount decision rather than an automation one.
It alerts, stops rather than guessing, and falls back to the manual path. You get a runbook and a rollback procedure, and the shadow run means you have seen it work before it touches anything.
Usually. If a system has an API we integrate with it; if it does not, we will tell you what that constraint costs before you commit rather than after.
Intelligent Automation
Let us look at your project.
Send the brief — or just the problem. We will tell you what we would do and what it would cost.