TL;DR
A model with 98% accuracy that nobody sees is worth less than an 85%-accurate one wired into the workflow where the decision actually happens. Taliferro ships machine learning into that workflow — predictive analytics, NLP, computer vision — not a notebook that proves a point and gets shelved.
See examples: explore our AI & ML case studies or learn why AI & ML matter.
Every business is different—so is our approach.
We don’t do one-size-fits-all. We align on outcomes, then build models that fit the workflow where decisions happen.
Intro: outcomes, case studies, and what we build.
Most machine learning projects don't die because the model was wrong. They die in the gap between a working notebook and a workflow someone actually checks. A prediction nobody sees never changes a decision — so we build for where the decision happens first, and treat model accuracy as the second question, not the first.
Small, confident steps—discovery, prototype, pilot—each removing doubt until the result feels obvious.
1–2 weeks
Discovery reveals constraints. The biggest wins usually surface in data access and decision timing, not model choice.
2–6 weeks
Prototype fast, measure sooner. We validate with offline backtests and lightweight A/Bs.
4–8 weeks
Pilot what ships. MLOps, APIs, and guardrails are part of the pilot so success scales beyond a demo.
Integration beats novelty.
Models are successful when decisions become simpler and results compound. We measure business lift first.
Value is the point.
Retail, healthcare, finance, logistics. The patterns differ; the principles do not. Shorten the loop between signal and action, and the system gets smarter with every decision.
See how this plays out in practice in our AI & ML case studies.
We focus on outcomes, integration, and measurable lift. Build the right thing. Ship value early. Measure what matters.
Propensity scoring · churn risk · demand forecasting
We identify the 20% of features that drive most of the lift and deploy where decisions happen.
Classification · routing · summarization · RAG
Small, precise datasets often outperform massive generic corpora.
Detection · OCR · defect spotting · quality checks
We use augmentation and semi-supervised learning to reduce labeling time and cost.
APIs · data pipelines · guardrails · MLOps
A 2% ROC lift is less valuable than reliable insights integrated into the workflow.
Next step
Email [email protected] or call 401.646.2662 to scope a 1–2 week discovery.
A walkthrough of how an AI-powered dashboard comes together — from raw data to a decision someone actually uses.
Use AI consulting for strategy, use-case discovery, and rapid prototypes. Choose Machine Learning consulting when you’re building predictive models (classification, regression, NLP, computer vision) and integrating them into workflows.
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.
Browse our AI & ML case studies and read why AI & ML matter.
Email [email protected] or call 401.646.2662. We’ll align on outcomes, data access, and a fast path to a working prototype.
Turn data into predictions and automated decisions.
Design, harden, and integrate APIs that don't break in production.
Find what's costing too much or breaking under load.
