Price optimization with AI

AI For Good Meetup | Exploring AI & Renewable energy on November 28th. Register now

Price optimization

Uplift your business with AI‑based price optimization

Combine industry knowledge with machine learning.

Own your pricing process

Set a dynamic frequency of adjustments that depend on the business goals you are willing to optimize.Take advantage of AI-generated insights and suggestions based on your historical data, or start controlled experimentation where data lacks.Effortlessly scale to thousands of products.

Dynamically adjust prices

Our AI-based pricing algorithms can look at a vast number of internal and external factors.Consider seasonality, customer price elasticity, competitor pricing strategies, or specific features that might impact your business — even weather conditions.Get the best possible price for every single item at each point in time.

Never run out of stock

Manage your stock by integrating your inventory management decisions with the data-driven pricing system.Our AI solution will granularly anticipate product demand and signal when there is a chance you may run out of stock

Simulate using what-if scenarios

Experiments gone bad can be very costly. With what-if scenarios, analysts and decision makers can understand different strategies and pricing schemes, visualizing the estimated impact on sales while playing with different factors that affect demand.What if you increase the price in 5% next week? Will it affect your sales? Try it on!

+1M USD

annual savings for second-hand luxury store

100k

SKUs optimized per month for online retailer

Case study

28% increase in gross margin

For a brick & mortar luxury retailer with +9,000 employees and 160M customers per year, Tryolabs developed a price optimization solution to replace a traditional pricing process.

The client implemented it through weekly changes in the price tags.

Deployment against a control store using the traditional pricing mechanism showed a 28% increase in gross margin for the selected SKUs and significantly reduced out-of-stock related losses.