Machine Learning Consulting.

Most companies that want machine learning do not need machine learning. They need a clean spreadsheet, a few rules, and a Monday meeting. We tell you which one you are, honestly, before anyone writes a line of training code.

The work starts with a hard conversation. Do you actually have enough data to train a model? Almost no SMB does. Is the prediction you want to make really a prediction, or is it a calculation? Most of the 'we need AI to predict churn' projects we get asked about are actually 'we need to look at the last 50 customers who left and write down what they had in common'. That is not a model. That is a spreadsheet and a Tuesday afternoon. We will tell you that, and we will save you the build cost.

The lie the consulting industry sells is 'every business needs a custom ML model'. Almost none do. The ones that genuinely do have one of three things: enough labelled data to train on (tens of thousands of examples, minimum), a prediction problem where the input is too complex for rules (image classification, document extraction, fraud detection at scale), and an output where being right 80 percent of the time is more valuable than being right 100 percent of the time by hand. If you do not have all three, the answer is rules, heuristics, or an off-the-shelf API call.

If you actually do need ML, the work runs in a strict order. We scope the problem, find or build the training data, pick the smallest model that could work (often a logistic regression or a gradient-boosted tree, rarely a deep neural network), and ship a working version into production behind a feature flag. We measure whether it actually moves the metric, and only then do we invest in scaling, monitoring, and the rest. Half the ML projects that get built never make it to production because nobody scoped them honestly at the start. We scope honestly.