Models built for production.
Ranking, recommendation, classification, regression — engineered with the infrastructure, evaluation and governance enterprise production demands.
Beyond the leaderboard, into the system.
A model that wins an offline benchmark and never ships is worthless. We engineer the full path — feature stores, serving, monitoring, retraining — that turns models into compounding business systems.
Six capabilities. One operating system.
Recommendation Systems
Personalization, ranking, retrieval — across content, products, accounts.
Classification
Intent, routing, scoring, segmentation — with calibrated probabilities.
Regression & Estimation
Pricing, value, time-to-event modeling with uncertainty quantification.
Graph & Network ML
Fraud rings, recommendation graphs, knowledge graph completion.
Reinforcement Learning
Sequential decisioning, bidding, allocation in well-instrumented domains.
Feature Engineering
Feature stores, real-time features, point-in-time correctness.
From kickoff to compounding outcomes.
Frame
Business problem, decision, success metric, baseline.
Build
Feature engineering, model development, holdout validation.
Productionize
Serving infrastructure, monitoring, A/B framework.
Validate
Online experiment against business metric, not offline proxy.
Iterate
Continuous improvement against business KPI.
Where this earns its keep.
Recommendation & ranking
Personalized recommendations that lift AOV, retention and engagement — proven against holdouts.
Lead and account scoring
Calibrated propensity scores that route work and resources.
Dynamic pricing
Real-time pricing models with guardrails and explainability.
Anomaly and fraud detection
Production-grade detection with low false-positive rates and analyst feedback loops.
Frequently asked
Scope a production model.
A 60-minute architecture session — we shape one use case end-to-end with a clear path from notebook to revenue.