AI implementation
AI where it earns its place. Not everywhere.
The useful question is not "how do we use AI." It is "which parts of this work are hard for software and easy for a person," because that is the short list where AI changes the economics.
What AI is genuinely good at
The tasks worth pointing it at share a shape: the input is messy, the rules resist being written down, and a person currently reads something and makes a judgment.
- Reading unstructured input: invoices, emails, PDFs, forms, handwriting, and turning them into fields a system can use
- Classifying and routing: deciding what an incoming message is about and where it should go
- Drafting: first-pass replies, summaries, descriptions, and reports a person then edits
- Extracting: pulling specific facts out of long documents at a volume nobody would read by hand
What it is not good at, and where we say no
Anything requiring an exactly correct answer every time: arithmetic that must reconcile, compliance rules, anything where a plausible wrong answer costs more than no answer. That work belongs in ordinary deterministic software, which is cheaper to run and does not need watching.
A good AI implementation is mostly ordinary software with a small, well-chosen model call in the middle. If a proposal is all model, someone is selling you the interesting part instead of the working part.
Keeping a person in the loop
The systems that survive contact with a real business are the ones where AI drafts and a person approves, at least until the error rate is known. We build the review step in from the start and take it out later if the numbers earn it, rather than the other way round.
What it costs to run
We size the ongoing cost before building, not after. Model pricing changes constantly and a design that made sense at one price can stop making sense at another, so the architecture keeps the model swappable instead of welding your process to one vendor.
How it starts
A free 30-minute call, spent understanding the work rather than pitching a model. We will want to know what the input actually looks like, how often a person has to make a judgment about it, and what happens today when that judgment goes wrong.
Whether AI belongs anywhere near it is a question worth answering after that conversation rather than during it. Either way it is a cheaper way to find out than a project.
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