Service
AI and process automation
We take over a process end to end — not a tool your team has to operate, but work that stops arriving on their desk.
The situation
Every process stops at a person
Not because the work is difficult, but because somewhere in the chain something has to be read, compared or judged. An invoice against an order. A request against a procedure. A delivery note against what was actually specified. The process queues at that step, and the queue has a name and a desk.
Conventional integration moves data between systems that already understand it. What it cannot do is handle input that arrives unstructured, or complete a step that requires an assessment. Those are precisely the steps that remain manual after every automation project.
A model closes that gap. The system reads what arrives, compares it against what it should be, completes the case where the comparison is clean, and escalates where it is not. The person stops seeing the routine and starts seeing only the exceptions.
The question is never whether a model can perform the step. It is which part of the process you are prepared to let it finish, what happens in the cases where it should not, and how you would reconstruct a decision six months later if someone asks.
Most processes a client asks us to automate do not need a model. They need a rule, a mandatory field, or an integration. We establish which is which during the assessment, and we do not build what is not required.
Application
Where it applies
Documents arriving
Supplier invoices, orders received by email, contracts and delivery notes read, extracted into fields, matched against the order, and entered into the system that was waiting for them.
Requests and correspondence
Incoming messages classified and routed, the record opened with the customer data already attached, and the standard cases answered without a person composing the reply.
Internal knowledge
Procedures, contracts, technical documentation and past projects made answerable, so that finding a clause from three years ago takes a minute rather than half a day.
Reporting
Figures queried from the database, the narrative written against them. The model never produces the numbers — it explains the numbers the system produced.
Document drafting
Offers, specifications and technical documents assembled against the organisation’s own precedents rather than from a blank page.
Conformity checking
A document compared against a checklist or a set of requirements, with each finding traceable back to the clause and the page it came from.
Autonomy
How far the system is allowed to go
This is the decision that determines the risk, the cost and the benefit. It is taken per process, written into scope, and not left implicit.
Assists
The system produces a result and a person decides. Fastest to deploy, lowest risk, and the appropriate starting point where the consequence of an error is significant.
Completes, with exceptions
The system carries the process through and escalates only what falls outside the agreed pattern, with the reasoning attached. This is where most of the value sits and where most engagements settle.
Completes without review
No person in the loop. Appropriate only where an error is cheap to identify and cheap to reverse, and never as a starting position.
The level can be raised after the pilot has produced evidence that supports it. It is not raised on the strength of expectation.
Process
How the engagement runs
Assessment
Your processes inventoried: volume, time consumed today, feasibility, realistic autonomy level and expected benefit — including the ones not worth touching. Delivered as a document, and commissioned on its own.
Pilot
One process, with the success criteria agreed before it starts: what proportion should complete without intervention, what error rate is acceptable, what time is recovered. Run in parallel with the manual process and compared.
Production
The manual step is retired only once the measured results support it. The exception queue, the monitoring and the alerting go live at the same time, not afterwards.
Handover
Documentation, access, and a working session with the people who will own it. Periodic review of the exception rate, because processes drift and so do the documents feeding them.
Control
What makes it safe to hand over
An automated process that cannot be inspected is a liability, whatever it saves. These are built in from the first pilot, not added once something goes wrong.
Where the models run
External provider, infrastructure within the EU, or on your own hardware — decided by what your data permits and your obligations require, and written into scope before anything is built.
The decision log
Every automated action recorded with the data it had available, what it decided and on what basis, so that a decision taken six months ago can be reconstructed rather than assumed.
The exception queue
Cases the system should not complete go to a person with the reasoning attached. Nothing fails silently, and nothing is quietly approved because no rule covered it.
Reversibility
Every action the system takes in your systems can be identified and undone. Automation without a way back is not automation, it is exposure.
Provider independence
The model is a component of the architecture, not the architecture. It can be replaced when pricing, availability or capability changes, without rebuilding the process around it.
Measured, not asserted
Exception rate, error rate and time recovered are reported against the baseline agreed at the start. If the figures do not support the next autonomy level, we say so.
We audit IT projects for a living. The requirement that a decision be explainable after the fact is not something we added to this service — it is how we build.
Fit
Who this is for
A good fit
Organisations with a process that is high in volume, repetitive, and currently waiting on a person to read or compare something. Particularly where that person is a bottleneck, where the volume is growing faster than headcount, or where the same checking work is done in several departments.
Not a fit
Customer-service chatbots, which return little and damage the brand when wrong. Training models of your own, which is almost never justified at this scale. Forecasting, where the historical data is not clean enough to support the prediction. And strategy workshops with nothing built at the end of them.
Next
Where to start
Name the process that currently waits on someone, and roughly how many times a week it happens. The assessment establishes whether it is worth automating, and says so plainly when it is not.