INTELLIGENCE
Predictions that survive contact with production.
A model is only as useful as the pipeline behind it. We build forecasting, scoring, and detection models, from a gradient-boosted regression to a fine-tuned vision model, plus the training, serving, and monitoring pipeline that keeps each one running. The goal is a model still working six months later on purpose, not by accident.
THE PROBLEM
The model that only lives in one person's notebook.
Machine learning fails in enterprises for boring reasons, almost always after the interesting part, the modeling, is already done:
- A forecasting model lives in a data scientist's notebook and gets re-run by hand once a quarter, if that person is still around and remembers how.
- A churn, pricing, or demand model looked great in a slide deck and was never validated against what actually happened afterward. Nobody closed the loop.
- No one currently knows when the production model was last retrained, on what data, or whether it still beats the simple baseline it replaced.
- A computer-vision or document-extraction proof of concept never made it past the demo, because nobody built the ingestion, labeling, and serving pipeline a real deployment needs.
WHAT WE BUILD
Classical, forecasting, and deep, matched to the problem, not the trend.
Forecasting & demand models
Statistical and ML forecasting for demand, revenue, staffing, and inventory planning. Often a well-tuned gradient-boosted or statistical model outperforms anything more exotic, and costs far less to run.
Churn, propensity & pricing models
Classification and regression models tied to one specific decision: who to retain, what to charge, who to prioritize. The decision gets named before the model is built, not after.
Anomaly detection
Fraud, quality, and operational anomaly models tuned to your actual tolerance for false positives. A model nobody trusts because it cries wolf is a model nobody uses.
Recommendation systems
Collaborative and content-based recommenders sized to your actual catalog and traffic, not a reference architecture built for a company ten times your size.
Computer vision & document extraction
Defect detection, visual inspection, and structured extraction from invoices, forms, and scanned documents, including the labeling pipeline that keeps the model current as documents drift.
MLOps & the notebook-to-production path
Feature stores, training pipelines, a model registry, and drift monitoring: the infrastructure that turns a one-off model into a system that keeps working.
HOW WE ENGAGE
Five phases. The baseline gets built before the exciting model does.
- 1–2 weeks
Problem & Data Framing
A Decision Framing Document naming the exact decision the model changes and what happens today without it, plus a data audit
- 2–4 weeks
Feature & Baseline
A defined feature set and a simple baseline model, with baseline accuracy/error numbers the real model has to beat
- 4–10 weeks
Model Development
Candidate models, an offline evaluation report, and a bias/fairness check wherever the model affects individual people
- 3–6 weeks
Productionization
A training pipeline, feature-store integration, a model-registry entry, a serving endpoint, and drift monitors, all live before launch, not bolted on after
- ongoing
Monitor & Retrain
A monthly drift report, an agreed retraining cadence, and model-card updates each time the model changes
TECHNOLOGY
Boring where boring wins. Deep where depth earns it.
- Classical & forecasting
- Python: pandas, scikit-learn, statsmodels, Prophet/NeuralProphet, and gradient boosting via XGBoost, LightGBM, or CatBoost. Our default starting point for the large majority of forecasting and scoring problems.
- Deep learning & vision
- PyTorch, and fine-tuned transformer or vision models via Hugging Face and timm, used when the problem genuinely needs them: document layout understanding, image classification, defect detection.
- Feature stores & pipelines
- Feast or Tecton for feature stores; Airflow, Dagster, or Vercel Workflow for orchestration, sized to the pipeline's actual complexity.
- Experiment tracking & registry
- MLflow or Weights & Biases for experiment tracking; MLflow Model Registry, SageMaker Model Registry, or Vertex AI Model Registry depending on the client's cloud.
- Serving & monitoring
- SageMaker, Vertex AI, BentoML, or a self-hosted FastAPI/Docker endpoint for serving; Evidently, Arize, or WhyLabs for drift monitoring.
- Warehouse/lakehouse source
- Snowflake, BigQuery, or Databricks as the feature source. We build on the warehouse Data Engineering already gave you, not a parallel copy of it.
- Document/OCR layer
- Azure AI Document Intelligence and AWS Textract, shared with the RPA practice's document layer where an engagement touches both.
GOVERNANCE & RISK
A model making decisions about people needs a paper trail.
- Model cards for every production model: training data summary, intended use, and known limitations, written in plain language a non-modeler can review.
- Bias and fairness testing wherever a model affects individuals, such as credit, hiring-adjacent screening, or differential pricing, before launch and not after a complaint.
- Data lineage and consent tracking for training data, so we can answer 'where did this number come from' for any prediction, at any time.
- A human-review threshold on model decisions above a defined stakes level. The model informs; a person with authority decides, until the client explicitly raises that threshold.
- A versioned model registry with rollback. Every production model can be reverted to its prior version in minutes, not days.
- Drift alerting tied to a named, accountable owner, not a dashboard nobody is on the hook to check.
- Reproducibility by default: a pinned data snapshot, code version, and config saved per trained model, so any result can be regenerated exactly.
QUESTIONS
What people ask before the first model ships.
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