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The parts of this work that can go wrong quietly: what a system decides on its own, what waits for a person, and what gets written down so you can go back and check.
Applied from the first look at your documents through to what happens months after it is live.
A system that reads your paperwork or answers your phone will get things wrong. The question is whether it gets them wrong somewhere a person notices, or quietly, in a way that surfaces three months later in a billing dispute.
So most of the design work is about limits. What the system settles by itself, what it hands to a person, what it records, and how you check an answer you did not produce yourself. None of that is a product. It is how the build is put together.
Book a callA system takes typing off a dispatcher or a project engineer. It does not take the decision off them. Where being wrong is expensive, the step keeps a person in it by design, not as a courtesy.
Every automated decision keeps a record of what it read and which part of which document it read it from. If a figure lands in your project system, you can follow it back to the page it came off.
A system that learns from your history inherits your history, including the parts you would not want repeated automatically. I look for those patterns before the build, and I tell you when I find one I cannot design around.
Anything below the confidence threshold goes to a person instead of being guessed at. An illegible signature comes back as illegible, and accuracy is re-checked as your documents and the systems around them drift.
I build it, so the question of why it did something comes to me and gets answered by the person who wrote it. There is no committee between you and the explanation.
What the system may read, and what that data may be used for, is agreed in writing before anything is built. Access is limited to what the workflow needs, and the underlying records keep an access trail.
Most of this work is deciding what the system is not allowed to do. That gets written down while it is still cheap to change.
The design decisions that make a system explainable have to be made during the build. They cannot be added to a finished one.
I try to break it on purpose, on your material, while nothing depends on the answer.
It runs beside your existing process first, and nothing depends on it until the two agree.
A system nobody is watching degrades quietly. The monthly cost is what stops that.
A contractor wants submittals and invoices read automatically, but a wrong figure filed into the project record is worse than a slow one, and an auditor later has to see where each value came from.
Extract with a confidence threshold rather than a best guess, keep every value linked to the page and region it was read from, hold anything below the threshold for a person, and log who approved each record.
The design goal is a project record where every extracted field can be traced back to the document it came from, and nothing enters it without a named approval.
A service business wants after-hours calls answered, but a system that misreads an emergency as routine leaves someone without heat overnight, and one that escalates everything is just a pager.
Bias the urgency rules toward escalation, state plainly to the caller that they are speaking to an automated intake, keep a human escalation path open at every point, and review the misclassified calls weekly.
The design goal is triage whose failure mode is waking an on-call technician unnecessarily, never leaving a genuine emergency in a queue.
A carrier wants late and off-plan loads surfaced early, and the same telematics data would also support scoring individual drivers on behaviour they were never told was being measured.
Scope the data to the load rather than the person, agree in advance what the signals may and may not be used for, tell drivers what is collected, and keep an access trail on the underlying records.
The design goal is exception visibility for dispatch that does not quietly become a performance surveillance system.
These are practices applied during delivery using established, mostly open-source tooling. They are not products I sell, and nothing here is a platform you would license.
Model outputs are tested across the attributes that carry risk in your domain before anything reaches production, and again on a schedule afterwards.
Every decision a system makes can be traced back to the inputs that drove it, in terms a non-technical reviewer can follow.
Where data cannot or should not move, the architecture works around that constraint rather than asking you to relax it.
The paperwork a model needs to survive review: what it is, what it was trained on, who approved it, and what changed since.
Tell me what a wrong answer would actually cost in your operation, and we can work out where a person has to stay in the loop.
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