The problem it solves

Static discount rules age badly: the 10% promo that worked last year runs forever because nobody can prove it stopped working. Manual A/B tests cover a handful of SKUs while thousands drift. The knowledge of what price actually moves each product, for each segment, exists only as folklore.

What it does for your business

Runs disciplined experiments, continuously

The system tests price points and promotion structures across products and segments — small, controlled variations, always inside floors, contracts, and MAP rules you set.

Learns causally, not anecdotally

It measures what a price change caused, controlling for seasonality and demand shifts — so 'the promo worked' becomes a number, not an opinion.

Shifts budget to what performs

Promotion spend migrates automatically from offers that merely discount to offers that genuinely lift revenue and margin.

Keeps finance in the loop

Every active experiment, its guardrails, and its measured impact are visible in one console — auditable, pausable, and reversible at any time.

How it works

Contextual-bandit / reinforcement-learning optimization over price and promotion actions, constrained by hard business rules. Suited to catalogs with meaningful order volume — the system needs enough transactions to learn quickly; we assess fit during scoping.

What we need from you

Order history with realized prices, cost data, promotion history, and your pricing constraints. Starts in shadow mode — recommending and measuring — before any price changes go live.

Talk through AI-Driven Pricing & Promotion Optimization with our team

A 30-minute conversation on your architecture and goals — with live demos of the underlying models where they exist today.

Request a Demo