Announcing our seed raise to help retailers make better pricing decisions.

April 26, 2023

Pricing strategy is one of the most powerful levers that retailers have to create margin and revenue growth, yet most retail pricing teams make decisions in spreadsheets and are often shooting in the dark. One category manager we spoke to at an apparel brand complained that she was spending over 8 hours a week in a forecasting spreadsheet, “doing 10x the work I need to be doing for 20% of the impact I think I can get”.

The reason price decisioning in retail is poor is because it is hard. Retail pricing teams have to incorporate large volumes of data from multiple channels to build a strategy – sales history, market trends, competitor price changes and inventory availability. And, they have to do this across 10,000s of SKUs, with approvals from multiple stakeholders. This level of complexity makes price changes time-consuming and inefficient, and ultimately leaves money on the table.

When Yonah and I worked on pricing at Uber, we helped create massive uplift in revenue and profits by building pricing algorithms and putting configurable tools in the hands of Uber’s operators. We realized that retail was lacking the same quality of tooling, and saw an opportunity to help retailers make better pricing decisions.

Meet the Pricing Co-Pilot for Retail

Luca is an AI-powered co-pilot for enterprise retailers, which constantly identifies revenue and profit headroom, makes recommendations for price adjustments and saves countless work hours along the way. Our solution is complementary to the human decision maker in a retail organization. We want to give them superpowers by converting a sea of data about their business into clear recommendations they can act upon confidently with the click of a button. 

Luca generates price recommendations by absorbing retailers’ sales, inventory and competitor data to forecast outcomes at different price points, ultimately selecting a winning one. Once recommendations are pushed live, Luca monitors the price changes in production, watches out for abnormal patterns and alerts the decision maker if something is off. Lastly, Luca’s attribution models isolate the impact of the pricing change and accurately measure business impact, helping the decision maker gain confidence in their pricing strategy.

Over the past 5 months, we’ve seen a strong pull from retailers for what we’re building. We’ve already worked with 8 mid-size brands, and are in pilot design with 2 Fortune 500 retailers to help them manage their pricing strategy. One of our current customers is seeing a 20% increase in profits from using our platform within the first 60 days.

$2.5 million, led by Menlo Ventures

Therefore we are excited to announce our $2.5 million seed round to scale out our data science and engineering teams, and to support more retailers on our platform. Our round is led by Aunkur Arya from Menlo Ventures, with participation from Y Combinator, Soma Capital, Uber’s angel syndicate and strategic angels.

We are excited to partner with Aunkur because he shares our vision to use AI to enable revenue optimization in enterprise. In his own words –

"The best investment opportunities we’re pursuing right now link data science and machine-learning to workflow compression inside the enterprise. The really exciting ones have a direct tie to improving enterprise gross margins. Luca is doing all of these."

We're hiring

Pricing is our first step towards a larger vision for revenue optimization in retail and we are excited to start this journey with our early customers, teammates and investors. If you are excited by this vision and want to help us build this company, we are hiring our founding engineers and data scientists. Please apply to our roles here.


Tanvi Surti
Tanvi is the CEO and Co-Founder at Luca. Before Luca, Tanvi spent a decade building product teams at Uber and Microsoft. At Uber, she led the pricing team that created ~$1B in margin improvements on the ridesharing business, and now gets to help retailers solve the same problem, at scale.

Ready to learn more?