Data science has always been a bottleneck: one scientist, a notebook, a long wait. Analytics Stack turns the discipline into a stack — a team of agents that can run many experiments in parallel, propose features a human might miss, tune models without hand-holding, and explain what they did in plain language. The agents handle the drudgery. Humans stay in the loop for judgment. Between Knowledge Stack (what is true) and Decision Stack (what to do), Analytics Stack answers the predictive question: what patterns are in the data, and what will happen next?
Analytics Stack mirrors Knowledge Stack's four-layer architecture — because it is a peer stack, not a plugin.
Each capability is delivered as an agent that a human data scientist would otherwise do by hand.
Explores new datasets automatically. Profiles distributions, spots outliers, surfaces correlations, and writes a short report you can review before modeling.
Proposes new features — aggregations, lags, ratios, interactions. Tests each one's contribution and keeps the ones that move the metric.
Tries multiple algorithms, tunes hyperparameters, and picks the winner on your validation set. Reports cross-validation scores with confidence bounds.
Generates SHAP values, feature importances, and partial dependence plots. Writes a plain-language explanation of why the model makes each prediction.
MLflow-backed tracking of every run: parameters, metrics, datasets, model artifacts. Reproduce any result in one command.
Shared feature definitions across projects. Compute once, reuse everywhere. Train/serve parity guaranteed.
Generate privacy-preserving training data that matches your real distributions. Useful when the real data is small, sensitive, or imbalanced.
Versioned model store. Staging, production, archived. Every model has a card describing data, metrics, and intended use.
Every deployed model ships with a model card: what it predicts, on which population, with what known limitations. Governance from day one.
Your data, your models, your infrastructure. Claude enhancement is an opt-in layer that unlocks deeper reasoning for tasks where local models fall short.
Your data never leaves your infrastructure.
Opt-in. Metadata-only by default — raw data stays local.
Start local. Scale with Claude. Switch anytime.
Every engagement starts with consulting — we understand your prediction challenge and design the right solution. Then you choose how to run it.
We host, operate, and optimize it for you.
Your cloud or on-premise. Full control over data and models.
