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Machine Learning and your business: what to know

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.

See examples: explore our AI & ML case studies or learn why AI & ML matter.

Predictive analytics NLP Computer vision Integration + MLOps

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.

Approach

Build the right thing. Remove noise.

It’s not about building everything. It’s about building the right thing. We remove ornament, prefer clarity, and design models that feel inevitable—simple, elegant, and useful.

Where we focus

  • Predictive Analytics (propensity scoring, forecasting)
  • Natural Language Processing (classification, summarization, RAG)
  • Computer Vision (detection, OCR, quality checks)
  • Custom Models & Integration (APIs, data pipelines, MLOps)

What you may not know

  • Signal beats volume: 80% of predictive lift often comes from ~20% of features. We prune noise.
  • Labeling isn’t destiny: small, precise datasets with augmentation can beat massive generic corpora.
  • Integration beats accuracy: a 2% ROC lift matters less than getting insights into the workflow.
Process

Progress should feel calm and certain.

Small, confident steps—discovery, prototype, pilot—each removing doubt until the result feels obvious.

Discovery

1–2 weeks

Discovery reveals constraints. The biggest wins usually surface in data access and decision timing, not model choice.

Prototype

2–6 weeks

Prototype fast, measure sooner. We validate with offline backtests and lightweight A/Bs.

Pilot

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.

What you can expect

Measurable lift. Cleaner decisions.

Typical outcomes

  • Reply rate lift: +8% to +22% (prioritization + follow-ups)
  • Churn reduction: 5% to 15% (risk scoring + outreach)
  • Ops hours saved: 10–30% (classification, routing, automation)
  • Forecast error: ↓10–25% (demand, staffing, inventory)

Outcomes we see

  • Sales: better lead scoring, next-best-action, follow-up automation
  • Support: intent detection, triage, summarization, agent assist
  • Operations: demand forecasting, scheduling, anomaly detection
  • Marketing: audience building, content selection, send-time optimization

Value is the point.

Industries

Machine learning for every industry

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.

AI consulting services

Strategy to working models—delivered in small, certain steps.

We focus on outcomes, integration, and measurable lift. Build the right thing. Ship value early. Measure what matters.

Predictive analytics

Propensity scoring · churn risk · demand forecasting

We identify the 20% of features that drive most of the lift and deploy where decisions happen.

Natural Language Processing (NLP)

Classification · routing · summarization · RAG

Small, precise datasets often outperform massive generic corpora.

Computer vision

Detection · OCR · defect spotting · quality checks

We use augmentation and semi-supervised learning to reduce labeling time and cost.

Custom models & integration

APIs · data pipelines · guardrails · MLOps

A 2% ROC lift is less valuable than reliable insights integrated into the workflow.

Next step

Email info@taliferro.com or call 425.381.9986 to scope a 1–2 week discovery.

Video

Intro

Animated Taliferro Group logo video, introducing AI & Machine Learning case studies and outcomes.

FAQs

Common questions

Do we need AI consulting or Machine Learning consulting?

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.

How long does a typical project take?

Discovery 1–2 weeks, prototype 2–6 weeks, pilot 4–8 weeks, depending on data quality and integration scope.

Do we have enough data to start?

Yes. We start with what you have—CRM/email events, product usage, tickets, spreadsheets—and iterate. Perfect data isn’t required to begin.

How do you measure success?

Business outcomes first: reply rate, retention, conversion, hours saved. Model metrics (AUC, MAE) guide quality, but decisions focus on ROI.

Where can we see examples?

Browse our AI & ML case studies and read why AI & ML matter.

How do we get started?

Email info@taliferro.com or call 425.381.9986. We’ll align on outcomes, data access, and a fast path to a working prototype.

Ready to talk?
Scope a 1–2 week discovery