How Ibotta Uses Databricks Vector Search and AI/BI to Personalize Cashback Offers and Reduce Latency at Scale

Ibotta's core product is matching shoppers with relevant cashback offers from brands — a personalization challenge that requires fast, accurate semantic understanding of shopper intent and offer attributes. With millions of shoppers and thousands of brand offers, the quality of the matching algorithm directly determines engagement, conversion, and the value delivered to both sides of the marketplace.

Before consolidating on Databricks, Ibotta's data and ML teams operated across fragmented tooling — separate systems for data processing, feature engineering, model training, and serving. This fragmentation slowed development cycles, created governance gaps, and made it difficult for analysts and engineers to collaborate on a shared understanding of platform data.

Ibotta standardized on Databricks Data Intelligence Platform, centralizing all data and ML operations under Unity Catalog for governance and lineage tracking. The team deployed Vector Search for semantic similarity matching across offer inventories — enabling the recommendation engine to match shoppers with relevant offers based on intent signals rather than rigid category rules. Lakeflow Jobs provides orchestrated pipeline management for the ML training and serving workflows. AI/BI democratizes analytics, enabling business teams to query platform performance data without requiring engineering support.

The consolidated platform delivered measurable improvements across the offer matching pipeline: increased offer relevance through better semantic matching, higher shopper engagement with personalized recommendations, reduced latency in the recommendation serving path, and faster development cycles for engineers and analysts working from a unified, governed data foundation.