Rezolve Ai modernizes product discovery platform to improve search performance with MongoDB

THE CHALLENGE

Navigating excessive latency, an outdated delivery platform, and mounting technical debt

Rezolve Ai is transforming how shoppers discover products online. The company uses AI to power e-commerce search and product discovery solutions that enable retailers to bridge the gap between consumers and merchants. The company serves both business-to-business (B2B) and business-to-consumer (B2C) retail brands through its software-as-a-service (SaaS) platform.

Rezolve Ai’s original product discovery system featured a contextual, AI-based search engine that performed well with subjective queries, such as "blue blankets." As Rezolve Ai expanded its product discovery system to serve more diverse use cases, the company identified an opportunity to enhance its capabilities for B2B customers. Specifically, the system could be improved to more efficiently support precise part number searches, which follow specific formats without contextual elements. To address this need, Rezolve Ai sought to incorporate a specialized keyword-based search capability.

Behind the scenes, the Rezolve Ai team grappled with extensive operational overhead. The company’s Elasticsearch-based solution required dedicated teams to maintain the system. The platform also lacked the flexibility needed to adapt to evolving customer requirements.

As Rezolve Ai modernized its platform from 1.0 to 2.0, it began exploring alternative foundations that could deliver the required performance, reduce operational complexity, and handle both contextual and non-contextual search queries. After evaluating multiple vendors and conducting proof-of-concept tests, the product discovery team identified MongoDB Atlas as the ideal solution.

“MongoDB Atlas, with its auto-scaling and search node provisioning, eases a lot of the overhead,” said Henry Tang, Lead Solutions Architect at Rezolve Ai. “It was a no-brainer to adopt MongoDB Atlas, from not only a feature perspective but also an operational one.”

OUR SOLUTION

Building a resilient data hub using MongoDB Atlas

Starting in 2024, Rezolve Ai implemented MongoDB Atlas as the foundation for its next-generation product discovery platform. This modern, centralized data management system can efficiently handle both contextual and non-contextual queries while reducing operational overhead. What’s more, the migration to MongoDB Atlas was completed with zero customer downtime or disruption, allowing for a smooth transition.

The solution centers around what Rezolve Ai calls its Data Hub. This is a metadata store built on MongoDB Atlas that streams real-time product updates to all database engines. The hub uses MongoDB Change Streams to capture customer data at its initiation point. The data is then augmented and transformed before being sent to the underlying data structures.

“MongoDB provides the best of both worlds,” said Tang. “You’re able to store and retrieve things really quickly, and you can still make modifications and query the database at a high volume without having to pay out the nose for infrastructure.”

The Data Hub serves three critical functions for Rezolve Ai’s product discovery platform:

• First, it acts as a metadata service that stores and manages all catalog data from retail clients. This maintains the state of product information and enables version control.
• Second, it functions as a near real-time data transformation system that processes incoming product data into the formats required by different search engines.
• Third, it maintains consistent and up-to-date product information available across all client-facing interfaces, regardless of which backend engine is being used.

MongoDB Atlas Search provides the Lucene-based keyword search capability that powers Rezolve Ai’s part number search functionality. This enables exact matching for part numbers without attempting to interpret them contextually, delivering precise results for B2B customers. Rezolve Ai can route queries to the most appropriate search engine automatically.

With MongoDB Atlas, Rezolve Ai has simplified infrastructure management by using its auto-scaling capabilities, search node provisioning, and intuitive interface coupled with a Terraform integration. As a result, the product discovery team no longer needs to dedicate specialized staff to maintain servers and database operations. Instead, they can focus on enhancing product features and responding to customer needs.

“Our team is a lot happier now that there are fewer things to manage and watch,” said Tang. “In certain use cases, we’ve seen improvements in performance. We saw a reduction in latency by up to 50%, and we’ve achieved an average response time latency of roughly 50 milliseconds—which is better than expected.”

OUTCOME

Enhancing performance and architectural flexibility while reducing management overhead

The MongoDB-powered solution is currently in limited release with select customers, and Rezolve Ai plans to make it available to all customers by the end of Q2 2025. The early results have been promising, with customers experiencing improvements in business performance thanks to enhanced product discovery capabilities. With the new platform’s flexibility, Rezolve Ai can quickly adapt to customer feedback and make adjustments to optimize the search experience.

This is just the beginning of Rezolve Ai’s journey with MongoDB. As the company continues to invest in AI-powered solutions, it plans to explore MongoDB’s AI and large language model (LLM) capabilities to further enhance its product discovery offerings.

“MongoDB is a great company to work with, from both a relationship standpoint and a technology standpoint,” said Tang. “MongoDB Atlas is a fantastic product with lots of capabilities. MongoDB has all the tools you might require in its repertoire.”