Every missed pattern, delayed forecast, or manual judgment is a decision made by default. Taliferro builds machine learning systems that turn data into predictions, recommendations, and actions your team can use.
TL;DR
We help teams ship AI— from strategy to working models. Predict who will respond, when to follow up, what to recommend, and where to automate.
A prediction is only useful before the fact.
A churn forecast that arrives after the customer already canceled isn't a forecast — it's a postmortem. The bar for a model isn't accuracy on a slide. It's whether the prediction shows up in time to change what happens next.
A short overview of how this actually works.
Right now, decisions that could be predictions are being made by default: a rep guesses which leads to call first, a planner orders inventory based on last month's gut feeling, a support queue gets triaged in the order tickets arrived instead of the order they matter.
Not intuition — inconsistency. The same rep might prioritize leads well on a good day and poorly on a rushed one. A model applies the same judgment every time, on every lead, without getting tired by the two-hundredth one.
If nobody on your team can say why the model flagged something, they won't trust it, and they'll quietly stop using it. We build models people can question, not just consume.
We build toward a specific decision you're already making by hand — who to call first, what to flag for review, what to reorder — and design the model around that decision, not around the data for its own sake.
Propensity scoring · forecasting
Classification · summarization · RAG
Detection · OCR · quality checks
APIs · data pipelines · MLOps
Discover how other businesses are using machine learning to grow.
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The Momentum System gives AI and machine learning work a practical frame: what should happen next, who should act, what gets measured, and where momentum is breaking down.
Use AI consulting when you need strategy, use‑case discovery, and rapid prototypes. Use Machine Learning consulting when you’re ready to build predictive models (classification, regression, NLP, computer vision) that integrate with your systems.
Discovery 1–2 weeks, prototype 2–6 weeks, pilot 4–8 weeks depending on data quality and integration scope.
Yes. We start with what you have—CRM/email events, product usage, tickets, spreadsheets—and iterate. Perfect data isn’t required to begin.
Business outcomes first: reply rate, retention, conversion, hours saved. Model metrics (AUC, MAE) guide quality, but decisions focus on ROI.
