Amid the generative-AI hype, it’s easy to forget that a lot of the highest-ROI AI in business is classic machine learning: using your own data to predict, classify, and optimize. Forecasting, scoring, anomaly detection — unglamorous, but they move real numbers. This page explains machine learning consulting and how dgm approaches it. (dgm implements osFoundry, a separate company’s platform — we are not osFoundry.)

ML vs generative AI: different tools for different jobs

It’s worth separating the two clearly, because they solve different problems:

  • Generative AI produces content and excels at language: drafting, summarizing, answering questions, assisting knowledge work. (See Generative AI Consulting.)
  • Machine learning, in the classic sense, finds patterns in your structured data to predict and classify: what demand will be, which lead is likely to convert, which transaction looks fraudulent.

Many businesses benefit from both. The mistake is reaching for a large language model when a well-built predictive model on your own data would be more accurate, cheaper, and more explainable — or vice versa. Honest consulting matches the technique to the problem.

Where machine learning delivers

ML tends to pay off on structured, data-rich problems with a clear decision to improve:

  • Forecasting — demand, sales, inventory, capacity.
  • Scoring — leads, credit, churn risk, prioritization.
  • Anomaly detection — fraud, quality defects, operational outliers.
  • Optimization — pricing, scheduling, routing, resource allocation.

If you have history and a repeated decision you’d like to make better, there’s often an ML solution worth evaluating.

The two things that actually determine success

A model in isolation is worthless. Two factors decide whether ML delivers:

  1. Data quality and access. ML learns from your data; if it’s messy, incomplete, or inaccessible, no model fixes that. This is why a data integration and readiness step usually comes first.
  2. Deployment into a real decision. A model that produces a prediction nobody acts on changes nothing. The value comes from wiring the output into an actual workflow or decision — which is an integration problem as much as a modeling one.

dgm focuses on both, because that’s where ML projects succeed or fail.

What dgm delivers, and what it costs

dgm keeps it simple and public:

  • Assessment + roadmap ($399, one-time). We evaluate whether your data and use case support an ML solution and lay out a plan — honestly, including when ML isn’t the right fit.
  • Full build + integration ($3,999/month). We prepare the data, build or select the model, validate it, and integrate its output into a real decision in your workflow — with ongoing optimization and no per-seat fees.

We use osFoundry to orchestrate and integrate the solution while keeping you model-flexible and your data under your control.

How dgm helps

dgm helps US businesses turn their own data into better decisions with machine learning — framed honestly, built on quality data, and deployed where it actually changes an outcome. If you’d rather explore the platform yourself first, you can go straight to osFoundry; if you want ML done right and put to work, that’s where dgm comes in.