Stocastic · Retail & supply chain
Stocastic: an AI control tower you can play against
Retail inventory decisions like allocation, markdown, and replenishment are high-stakes and hard to trust to a black box. So we made the AI prove itself in a head-to-head game.
41–65%
forecast error reduction vs. baseline on 8 public datasets
1,851
simulated stores in the live playable demo
8
language SDKs on a real, versioned REST + gRPC API
The idea
Nobody trusts an AI with inventory money because vendors show slide decks, not proof. Stocastic proves AI value experientially: you run a multi-store retail network through a week of real-world chaos: demand spikes, supply shocks, markdown decisions. Then the AI runs the exact same week. The side-by-side P&L settles the argument.
What we built
- A playable digital twin: ~1,851 stores across 43 states, multi-echelon supply (store ← DC ← supplier), deterministic simulation with seasonal demand, price elasticity, and spoilage.
- You-vs-the-AI mode: same scenario, same shocks, compared P&L.
- Real ML underneath: LightGBM forecasting with quantile models, live weather signals, and holiday/payday features, benchmarked on public retail datasets (Rossmann, Walmart, M5).
- Production API: versioned REST + gRPC, OpenAPI 3.1, API-key and SSO auth, tamper-evident hash-chained audit log, policy guardrails with an autonomy circuit breaker.
- Commercial infrastructure: pilot onboarding tooling, backtest reports, and a full contract pack.
Why it matters for your project
This is what we mean by “custom functionality”: not a widget on a template, but a real system (simulation engine, ML pipeline, APIs, audit trail) designed, built, and shipped by the same hands you’d be hiring.