AI Implementation Consulting
This is the AI implementation practice: folding AI into a business that already works, starting at the workflow instead of the model. The revenue exists. The process exists. Somewhere inside it, competent people spend their best hours reading documents and moving them along. We find those steps. Then we build the narrow thing that does one of them and put it into production with a way to tell whether it’s helping.
Why AI Pilots Never Ship
In a business that already runs, there’s no shortage of places AI could go. What’s short is places where the change is worth the disruption. The usual sequence runs backwards. A tool gets chosen, a pilot gets run, and then someone goes looking for a workflow that fits it. What survives is a demo. It’s impressive in a meeting and unowned by Monday. The real cost sits in plain sight, in the hours a capable person spends reading a long document to pull a few fields out of it.
How An AI Rollout Runs
Every stage below exists to keep the build small enough to finish and specific enough to check.
We walk one workflow end to end, in the order it really runs instead of the order the org chart implies. Who touches the file, what they open, what they retype, where it sits waiting. The output is a map of steps with a cost written against each one. That cost is time spent, plus what it costs when the step is done wrong.
The candidates tend to be the same shape. A person is reading, extracting, classifying, routing or summarizing. We rank them by how repetitive the step is and how tolerant it is of an error. High volume and cheap to correct goes first. Rare and expensive to get wrong stays with a person, who gets better tooling instead.
One step, one system, one output you can measure. That usually means retrieval over company documents with citations back to the source, extraction from unstructured records into fields, classification and routing into the queues you already use, or drafting that stops at a review gate. It runs against your data inside your environment, and it’s code you can read.
Where an error is costly, the system’s job changes. It prepares the decision instead of making it. The draft arrives with its sources attached. The extraction arrives with the field and the page it came from, and the reviewer approves or corrects in a single action. We capture those corrections. A correction is the cheapest label anyone will ever hand you.
Every run is logged with its inputs, its output and what the reviewer did to it. That produces an evaluation set built from real traffic instead of invented examples. It also tells you whether the thing is still right this month, which is the only question that keeps mattering. Handover is the running system, the code, the prompts and the evaluation set, with an owner named on your side before we finish.
Evaluation, Retrieval & Build vs Buy
Before anything is built we assemble real cases with the right answer written down beside them, produced by the person who currently does the work. It’s slow. It’s also the easiest part to want to skip. Without it, "it seems better" is the only verdict available, and that verdict is worthless three months later when the model underneath has changed. The set outlasts the build. It’s how you check the system after handover.
The default is grounded retrieval against your own documents, with citations. We don’t start by training a model on your data. Training is expensive to repeat and hard to correct one fact at a time, and it hides where an answer came from. With retrieval, a wrong answer traces back to a wrong document. That’s a file someone can correct without retraining anything. We move off that default when the same task runs thousands of times a week and the definition of a right answer has stopped changing.
Not everything should be built, and saying so early is part of the job. Where a product already does the step properly, we configure it and move on. The case for building shows up when the workflow carries assumptions specific to you: your definitions, your exceptions, your record formats. A tool that doesn’t model those quietly imports someone else’s. A purchase is faster and someone else maintains it. Build when those assumptions are the reason the step is hard in the first place.
Contact
That step is enough to start with. No plan required, no budget line. The first conversation is with Daniel Laskowski, who scopes the work and stays on it. The team builds it, and the person you brief doesn’t change. Send the workflow and what it costs you now.
