Orion ML puts production-grade models in your engineers' hands. Atlas keeps every agent you run governed, observable, and safe to scale.
The forecasting and optimization library built from a decade of CPG and manufacturing engagements.
Orion ML packages the models we deploy most into a versioned Python library: demand forecasting, price and promotion optimization, assortment, and anomaly detection. Each module ships with backtesting, monitoring hooks, and sensible defaults learned from production, so your team starts from working baselines instead of blank notebooks.
Hierarchical SKU-store forecasting with seasonality, promotion uplift, and new product logic built in.
Price, promotion, assortment, and inventory optimizers with business constraints expressed in plain configuration.
Drift and outlier detection across pipelines and model outputs, tuned to alert planners before dashboards go wrong.
Every model validates against your history before it touches a live decision. Accuracy reports come standard.
First-class MLflow integration, drift metrics, and retraining triggers so models age gracefully.
Modules compose into pipelines that run anywhere Python runs: Databricks, Snowflake, or a single VM.
Curated history in, features out
Validated against your numbers
Versioned release into production
Drift watched, retraining triggered
Animated demo: a Python script configures an Orion ML hierarchical forecaster at SKU and store level with a 12 month horizon, fits it with backtesting, and writes stock norms. The run reports 92.4 percent SKU accuracy and 30 percent lower stockout risk, with a chart of history and forecast.
A consumer goods distributor replaced spreadsheet forecasts with Orion ML's hierarchical engine. Forecasts now refresh nightly at SKU-store level, stock norms update automatically, and planners only review exceptions.
A governed registry for every AI agent your enterprise runs.
Atlas is the control plane for agentic AI at scale. Every agent registers with a manifest: what tools it may call, what data it may touch, who owns it, and how it is evaluated. Atlas versions agents like software, traces every action they take, and enforces human-in-the-loop policies where judgement matters.
Every agent, prompt, and tool manifest versioned in one registry. Roll back an agent the way you roll back a deploy.
Declarative scopes for tools, data, and actions. An agent can only do what its manifest says it can.
Every tool call, decision, and handoff is logged and searchable. When someone asks why an agent did something, you have the answer.
Route low-confidence or high-stakes actions to a named owner. Approvals happen in the tools your team already uses.
Agents pass scored evaluation suites before each release. Regressions block promotion automatically.
Live dashboards for throughput, automation rate, escalations, and cost across every agent in production.
Animated demo: a live Atlas trace console. Classification, query, and payment agents triage vendor invoice emails: intents classified, replies drafted, purchase orders matched and fast-tracked, and non-PO invoices handed to a human owner for approval. Counters show emails handled today and a 70 percent automation rate.
An enterprise shared-services team runs its invoice triage agents on Atlas. Classification, query, and payment agents handle 200 to 300 vendor emails daily; Atlas enforces the review policy on every non-PO invoice and traces each decision end to end.
Orion ML models register into Atlas like any other agent. Each pipeline ships with a manifest, evaluation gates, and tracing from its first run.
Bring one decision, process, or workload. We will run Orion ML and Atlas against it in a 30-minute working session.
We don't sell slide decks. We build working solutions.