Better decisions at scale
Scores and rankings that improve conversion or ops efficiency.
AI · Machine Learning
Machine learning that predicts what matters.
Classical and modern ML for ranking, forecasting, personalization, and risk - when patterns in your data beat hard-coded rules.
Outcomes
What success looks like.
Measured lift
Baselines and experiments so you know the model helps.
Maintainable models
Retraining and monitoring planned - not a one-off notebook.
Capabilities
How we deliver in this area.
Ranking & recommendations
Surfaces the right item, role, or content next.
Forecasting
Demand, traffic, or capacity when history supports it.
Classification models
Fraud, quality, intent, and routing signals.
Personalization
Experiences that adapt without creepy overreach.
Feature engineering
Signals from product events that models can learn from.
Model serving
APIs and batch jobs that fit your product cadence.
How we run it
A clear path from brief to release.
Seniors stay close to the work. Status stays honest. The process bends to your stage.
01
Problem & metric
What prediction changes a decision - and how we score success.
02
Data & baseline
Start with a simple baseline before complex models.
03
Train & validate
Holdouts, leakage checks, and fairness spot-checks.
04
Deploy & retrain
Serving, monitoring, and a schedule to stay current.
Questions
Straight answers before you commit.
Is GenAI the same as ML?
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Is GenAI the same as ML?
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Related, but not the same. GenAI generates; many ML wins are about prediction and ranking. We use both when they fit.
What if our data is messy?
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What if our data is messy?
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That's normal. Part of the engagement is cleaning and deciding whether ML is ready - or whether better logging comes first.