ML & AI analytics
Predictive models, segmentation, churn, LTV, embeddings.
ML & AI analytics is predictive models, segmentation, churn, ltv, embeddings.
Why this work matters
ML projects often die in the gap between notebook and decision. The model works; nobody changes their behavior because of it. We start with the decision, work backwards to the model, and ship into the workflow that uses it.
The work, in detail.
- Churn + propensity models
- LTV + customer segmentation
- Anomaly detection in metrics
- Recommendation engines
- Embedding-based search & dedup
- MLOps + model monitoring
- Decision-driven, not demo-driven
- →Production ML model with API
- →Decision-driven thresholds + workflows
- →Model monitoring dashboards
- →Retraining pipeline
- →Documentation + business case
Practical machine learning embedded in your business: churn prediction, LTV models, customer segmentation, anomaly detection, and embeddings-based search.
The approach.
Decision-first
Every model starts with a decision: who will act on the output, in what tool, with what threshold. No deck-only models.
Boring beats clever
Logistic regression and gradient boosting beat fancy architectures 80% of the time. We pick the model that fits the decision, not the model that wins on a benchmark.
Monitored or not deployed
Drift, calibration, and bias monitored from day 1. Models that decay get retrained or retired; nobody acts on stale predictions.
More from Data, BI & Power Platform
The cost of waiting
is your competitor.
Every 90 days you delay is 90 days of authority compounding for someone else. Get the audit. See the math. Then decide.