Model Development
Baselines, ensembles, and LLM-enabled systems. We favor simple-first—with tests, fallbacks, and monitoring baked in.
Build for reliability
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Frame
Target, constraints, and success criteria. Choose the simplest viable model.
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Train
CV, leakage checks, bias/fairness tests, honest backtests, and ablations.
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Ship
Versioning, feature stores, CI/CD, real-time metrics, and safe fallbacks.
Teams ship baselines quickly, then iterate safely — with tests, fallbacks, and monitoring that keep confidence high.
When models drift, operations stay stable — ensembles, redundancy, and careful rollout make that possible.
Model patterns we use
Forecasting
SARIMAX, Prophet-like hybrids, gradient boosting with holidays & events.
Classification
Logistic baseline → XGBoost/LightGBM → calibrated probabilities.
Ranking/Recommenders
Matrix factorization, two-tower, contextual bandits with safety.
What you get
Model Cards
Assumptions, data slices, fairness, and maintenance plan.
Monitoring
Drift, calibration, latency, and alerting dashboards.
Fallbacks
Rules & heuristics to keep ops safe if signals degrade.
Ship a model you can trust.
We’ll help you go from baseline to monitored production.