Predictive models on your data — not LLMs in a costume
Churn prediction, demand forecasting, recommendation engines, fraud detection. Classical ML done right — features, training, monitoring, retraining — by people who've shipped models that earn their keep.
Where classical ML still beats LLMs
LLMs got all the headlines in 2024-2026. But behind the scenes, the predictive models running e-commerce recommendations, fraud detection, demand forecasting, and credit scoring are still mostly gradient-boosted trees, neural networks, and other “boring” ML — and they’re still where the measurable money lives.
The reason: structured data + clear target variable + lots of historical examples is exactly what classical ML eats. LLMs are wonderful for unstructured text. They are not a replacement for a churn model.
What production ML looks like when it works
The unsexy infrastructure that separates a Kaggle notebook from a real business asset.
Feature engineering
The 70% of the work that decides if a model wins. We build feature stores that engineering can extend without rebuilding the model.
Model selection
Right algorithm for the problem — boosted trees for tabular, neural nets when the data demands it. We don't use deep learning to look modern.
Online evaluation
A/B harness so model V2 has to beat V1 on real business KPI, not just offline metric, before it ships.
Drift monitoring
Auto-alerts when input distribution or model performance starts sliding. No surprise "the model died 3 months ago" moments.
Retraining pipelines
Scheduled or trigger-based retraining with proper data validation. The model gets sharper, not staler, over time.
Explainability
SHAP values + plain-English feature reasons on every prediction. Required for credit, hiring, healthcare; useful everywhere.
Models we’ve built variants of repeatedly
Pattern-recognise yours and we'll move fast on the parts that aren't standard.
Churn / retention prediction
- Predicts churn 30-90 days before it happens
- Drives proactive save-play workflows for CS team
- Pays for itself the first quarter post-launch (typical)
Recommendation engines
- Product / content / next-best-action recommendations
- Hybrid collaborative + content-based filtering
- A/B-tested vs. your current baseline
Fraud / risk scoring
- Real-time transaction risk scoring
- Tuned per business — false-positive cost vs. false-negative cost
- Explainable enough to satisfy compliance
From data audit to live model
Most ML engagements take 6-10 weeks. The data prep is the slow part — that's normal.
Weeks 1-2 · Data audit + features
Map the data, fix the obvious issues, build the feature store. This is usually where 50% of the engagement lives.
Weeks 3-5 · Train + evaluate
Multiple model candidates against holdout sets. Pick the winner on the business metric, not just AUC.
Week 6 · Shadow + A/B
Run the model in shadow mode against your current decisions. If wins are real, A/B-test live traffic.
Weeks 7-8 · Monitoring + handover
Drift dashboards live. Retraining pipeline scheduled. Your data team trained to own it.
Questions data leaders ask first
The honest answers — including the disqualifying ones.
01 How much data do we need?
For binary classification (churn, fraud), thousands of labelled positive examples is usually the floor. For forecasting, 2+ years of history. We’ll tell you straight if you don’t have enough — sometimes the answer is “collect for 6 months, then come back.”
02 Should we use LLMs or classical ML for this?
If the input is structured (rows in a table, numeric/categorical features), classical ML usually wins on cost, latency, and accuracy. If the input is text or images, LLMs / neural nets are right. We pick per problem, not per fashion.
03 How do we know the model is still working in 6 months?
Drift monitoring. We track input distribution and model performance continuously, and alert when either slides. Most production model failures are silent without this; with it, you catch them in days.
04 Can we explain the model's decisions to a regulator?
Yes. SHAP values + plain-English feature contributions on every prediction. For regulated workloads (credit, hiring, healthcare) explainability is non-negotiable and we build it in from day one.
05 What if the model's wrong?
It will be sometimes — that’s what tuned thresholds and human review for low-confidence cases are for. We measure cost of false positives vs. false negatives and tune the operating point to match your business reality.
What teams say after going live with machine learning
Tell us about the decision you want to automate
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