Machine Learning

Machine Learning Consulting Built Around Production

There is a well-worn statistic in our field: most machine learning models never reach production. The reasons are rarely about the math. A data scientist builds a model that scores well on a held-out test set, hands it off, and discovers that no one owns the pipeline that would feed it live data, no one has agreed how its predictions enter a decision, and no one is watching for the day its accuracy quietly drifts. Interactive Intel is a machine learning consulting boutique that organizes the entire engagement around avoiding that fate. We are operator-led and founder-delivered, working out of Miami with growth-stage SMEs and modern healthcare practices, and we measure our success by whether a model is running and trusted, not whether it looked good in a notebook.

That bias toward production is why our 10/20/70 methodology fits machine learning so well. About 10 percent of the value comes from the algorithm you choose, 20 percent from the data engineering and infrastructure, and 70 percent from the people and process work that decides whether a prediction actually changes a behavior. A churn model that no one acts on saves nobody. So before we train anything, we get specific about the decision the model will inform, who makes that decision today, and what would have to change for them to trust a number coming out of a model instead of their gut.

Predictive Models That Fit the Business

Most of the machine learning we deliver is predictive and, deliberately, not exotic. Demand forecasting that helps a business hold less inventory without stocking out. Churn and risk scoring that tells a team where to spend its limited attention. Document and image processing that pulls structure out of paperwork or photos. These are problems where well-built classical and gradient-boosted models, trained on your own data in Python, outperform anything flashier. We are skeptical of complexity for its own sake: a simpler model that the team understands and can maintain will beat a black box that impresses in a pitch and rots in production.

As an illustrative example, a manufacturer might ask for a sophisticated defect-detection vision system. We would first check whether a focused model on a clean, well-labeled dataset of the few defect types that actually matter delivers most of the value at a fraction of the cost and risk. Scoping the problem down is itself a core part of good machine learning consulting, and it is where an operator's judgment beats a researcher's ambition.

MLOps: Keeping Models Alive

A model is not a deliverable; it is a living system. Our MLOps work builds the scaffolding that keeps machine learning healthy over time: versioned models and data so you can reproduce any result, automated retraining when fresh data arrives, monitoring that flags accuracy drift before it costs you, and deployment patterns on our usual stack of Python, Supabase, Vercel, and Next.js so predictions are available where the work actually happens. For healthcare clients, that includes integrating predictions responsibly with FHIR and HL7 systems and keeping a human in the loop wherever a prediction touches patient care.

Engagements Scoped to Ship

Clients typically start with an AI Readiness Audit ($5K–$15K) to confirm the data exists and is clean enough to support a model worth building. From there our flagship Agentic Workflow Sprint ($25K–$60K) takes one predictive use case all the way to production, with the MLOps in place to keep it running. Organizations with several models across regions move to Enterprise engagements from $150K. As the agentic-AI practice of Alton Group Worldwide, we keep the team small enough that founders deliver the work, and we would rather ship one model that earns its keep than prototype five that never leave the lab. That is what machine learning consulting looks like when it is built around production from day one.