The problem it solves

Flat price lists leave money on the table twice: pricing too low on products customers would happily pay more for, and losing deals on products where you're out of market. But nobody can manually tune thousands of SKU prices across segments — so prices get set once a year and drift out of date.

What it does for your business

Learns price elasticity per product and segment

How much demand actually moves when price moves — measured from your own sales history, not guessed.

Recommends, you approve

Start in recommendation mode: the engine proposes price changes with expected impact; your team accepts, edits, or rejects. Automate only when you're ready.

Respects every constraint

Margin floors, contract pricing, MAP compliance, and price-change frequency limits are hard guardrails — the engine optimizes inside them, never around them.

Shows the why

Each recommendation carries its reasoning: elasticity estimate, competitor context if you feed it, and margin impact — auditable by finance.

How it works

ML-estimated elasticity models feeding a rules-based optimizer with your margin and compliance constraints. A deliberate first step: fully adaptive (reinforcement-learning) pricing is available later, once volume and comfort justify it.

What we need from you

Order history with realized prices and quantities, cost data, and your pricing rules (floors, contracts, MAP). Competitor price feeds optional.

See Dynamic Pricing Engine on data like yours

A 30-minute walkthrough on live sample data — including how the model is trained and validated.

Request a Demo