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.
ML & AI analytics — common questions
What does an ML & AI analytics engagement deliver?
A production ML model with an API, decision-driven thresholds and workflows, model monitoring dashboards, a retraining pipeline, and documentation with a business case. The work covers churn and propensity, LTV and segmentation, anomaly detection, recommendations, and embedding-based search and dedup.
Why do you start with the decision instead of the model?
Most ML dies in the gap between notebook and decision: the model works but nobody changes their behavior because of it. We define who will act on the output, in what tool, and at what threshold first, then work backwards to the model and ship it into the workflow.
Do you use the latest, most advanced model architectures?
Only when they fit the decision. Boring beats clever: logistic regression and gradient boosting outperform fancy architectures most of the time, so we pick the model that fits the decision rather than the one that wins on a benchmark.
How do you keep models reliable after launch?
We monitor drift, calibration, and bias from day one and treat monitoring as a precondition for deployment. Models that decay get retrained or retired so nobody acts on stale predictions, and a retraining pipeline is part of the deliverables.
Who is this for?
Teams that want machine learning to drive real decisions and actions rather than demos. If the output won't change someone's behavior in a specific tool, we'll say so rather than build a deck-only model.
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