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PhD in AI and Data Science

The BOOPER Team

Ines Amor

PhD in AI and Data Science

Ines Amor designs predictive models applied to pricing, forecasting, and purchasing behavior analysis. She leads initiatives focused on explainable AI and optimization algorithms dedicated to retail pricing decisions.

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For our clients, Inès configures the AI engine at the core of the platform:

  • Designs and calibrates sales forecast and price elasticity models (simple and cross-price) for each catalog.
  • Configure pricing recommendations based on the retailer's business constraints (price image, price tiers).
  • Document the factors underlying each recommendation so that the pricing teams can retain control over the decision.
  • Works with clients' data teams to ensure the reliability of input data (sales, inventory, competition).

His articles

AI Price Recommendation with Explanatory Factors and Audit Log

AI Pricing Recommendations: Explainable and Auditable for the Retail Sector

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Illustration of agent-based AI related to autonomous pricing decisions: price, margin, governance

Agentic Pricing: The End of Static Pricing in Retail Pricing

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General-purpose AI (ChatGPT, Claude, Copilot, Perplexity) integrated with Booper, specialized pricing

General-Purpose AI vs. Dedicated Pricing Solution: Why Combine the Two

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Price tag linked to multiple data points representing a plotted decision

Explaining a Pricing Decision: Why Auditing a Rule Isn't Enough

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Three documents bound together by a shared price tag, symbolizing a shared perspective

Pricing, Purchasing, Category Management: Moving Away from Separate Files

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An electrical outlet plugged into a price tag, symbolizing seamless integration

Integrating pricing data without a major IT project: three levels

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