Application Refactoring · Marketing Personalization
Hippo Innovations needed to replace rigid relational databases and bolt-on search services with a simpler platform that could support highly variable product data, heavy e-commerce traffic, and new AI-driven search and personalization features without growing operational overhead.
Reported impact
StoreHippo cut release-cycle time in half, reduced query latency, saved engineering time and storage cost, simplified search operations, and gained a cloud-agnostic foundation for multilingual search, personalization, and AI retrieval.
Hippo Innovations consolidated StoreHippo's data and AI stack on MongoDB Atlas after moving away from self-managed databases and bolt-on services. Atlas holds the platform's operational data, including product catalogs, orders, inventory, customer accounts, and payment information, in tenant-isolated databases that can scale across regions and clouds. MongoDB Search handles full-text retrieval, autocomplete, facets, and relevance tuning against the same live documents, eliminating separate index-remapping and sync logic with external search engines. MongoDB Vector Search and Voyage AI extend that same data layer into semantic search and retrieval-augmented generation workflows, while MongoDB Data API lets front-end code query data over HTTPS and MongoDB Atlas Charts exposes campaign-performance visualizations to merchants. Multi-region replication and Atlas operations let the team move customer data with no downtime and ship features faster.
Merchant catalog, order, inventory, payment, and customer data land in tenant-specific MongoDB Atlas databases. MongoDB Search queries those live documents for autocomplete, facets, and relevance tuning; MongoDB Vector Search plus Voyage AI create embeddings and support semantic and RAG retrieval; MongoDB Data API exposes data over HTTPS to client features, and MongoDB Atlas Charts surfaces campaign insights to merchants.
"I'd estimate running in MongoDB Atlas saves around 10% of our total engineering cycles, which we can now reinvest in innovation."
"Separate queries that were previously run independently against the bolt-on search engine and database are now run in a single application call, leading to 30% lower query latency."
"The StoreHippo platform offered customers a choice of using the Algolia or Azure Cognitive search engines, but this made the technology stack complex and costly."
"The platform serves over 1.5 billion secure API calls per month."
"With a few clicks in the Atlas UI, we can move customer data on-demand, with no downtime."
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The public Organization JSON-LD contains numberOfEmployees value 60, and the page lists Company size 51-200 employees.
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