Dynamic Pricing Engine

Recommend the right price for the moment, inside the guardrails your team already trusts.

The problem it solves

Flat price lists cost money in both directions: some products are priced below what customers would readily pay, and others lose winnable deals because the number is out of market. No pricing team can manually retune thousands of SKUs across segments, so lists get set once and drift out of date.

What it does for your business

Learns real elasticity

How demand actually moves with price, measured from your own sales history per product and segment, not assumed.

Starts as a recommendation

The engine proposes a price with its expected impact; your team accepts, edits, or rejects it. Automation is a later step, not a starting requirement.

Respects every constraint you set

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

Shows its reasoning

Each recommendation carries the elasticity estimate and margin impact behind it, so sales and finance can both see why the number was chosen.

Where this moves the needle

Higher margin on optimized SKUs Better win rate on price-sensitive lines Fewer manual price overrides Faster price-change cycle
How it works
Elasticity is estimated per product and segment from your sales history and fed into an optimization layer that respects the margin, contract, and compliance rules you configure. A fully adaptive pricing mode can be introduced later once the team is comfortable with the recommendation stage.
What we need from you
Order history with realized prices and quantities, cost data, and your current pricing rules, including floors and contract terms. Competitor price feeds are useful but optional.

See the Dynamic Pricing Engine on data like yours

A short walkthrough on sample data, including how guardrails are configured before any price ever changes automatically.

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