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Founders & Management

Brice Konda, Valentin Pignolet

In Portfolio

Since: March 2023
In Vehicle(s): Fund II



Inpulse.ai revolutionises the management of stocks and supplier orders: assisted by artificial intelligence, your points of sale gain up to 5 points of margin on material costs. You can monitor the performance of your network in real-time and act quickly.

With Inpulse, restaurant chains optimise their margins thanks to AI.

Brice Konda and Valentin Pignolet, who heads the start-up, met at Centrale Paris. While having lunch at Blend in 2018, they spoke with the founder of this iconic burger chain and realised the problems faced by restaurant owners: the extent of losses, stock-outs of losses, stock-outs, telephone orders, and a vague view of the margin. They then decided to bring together two worlds: that of data science, which they mastered perfectly, and that of the restaurant owners, which they and that of the restaurant owners, whom they interviewed for months to design a solution They interviewed them for months to design a solution that would automate all the steps involved in supplying restaurants.

The first artificial intelligence platform that helps restaurant chains optimise their material costs has been launched. Inpulse is based on 3 pillars:

  • Supplier order automation: Inpulse has developed a proprietary AI technology that predicts future sales and automatically generates supplier orders to have just the right amount in stock. By taking into account various factors that impact the business such as weather, holidays, sporting events or public holidays, Inpulse enables restaurateurs to order what they need at the right time.
  • Simplification of stock management operations: such as automated orders, automatic detection of stock shortages, digitalized inventories, or automated management of credit notes and invoices.
  • Real-time access to profitability ratios: For the first time, operators and line managers have real-time access to material costs, actual gross margins and consumption variances for in-depth analysis by plant. No longer do they have to wait until the end of the month to spot raw material price increases or unusual losses, they can react quickly and make better decisions thanks to AI.

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