Reported impact
Business outcome
Up to 50% reduction in search query latency
- Cost Reduction
- Customer Experience
- Scalability
MongoDBRetailMarketing PersonalizationAI and Machine LearningCloud NativeData PlatformCost ReductionCustomer ExperienceScalability2025 MongoDBMDB#00684aRetailGlobalRezolve Ai’s legacy product discovery system relied on a contextual, AI-based search engine that excelled with subjective queries. However, the system could not support the precise searches that B2B customers required.We saw a reduction in latency by up to 50%, and we’ve achieved an average response time latency of roughly 50…Dec 23, 20255Marketing PersonalizationMongoDB Atlas · MongoDB Atlas Search0 2025-12-23 5
Rezolve AI
Marketing Personalization
Rezolve Ai’s legacy product discovery system relied on a contextual, AI-based search engine that excelled with subjective queries. However, the system could not support the precise searches that B2B customers required.
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.” “MongoDB Atlas, with its auto-scaling and search node provisioning, eases a lot of the overhead.
Solution
Rezolve Ai’s legacy product discovery system relied on a contextual, AI-based search engine that excelled with subjective queries. However, the system could not support the precise searches that B2B customers required. Rezolve Ai implemented MongoDB Atlas as the foundation for its next-generation product discovery platform. This system can handle contextual and non-contextual queries while reducing overhead. Rezolve Ai Talk to an Expert Rezolve Ai modernizes product discovery platform to improve search performance with MongoDB The Challenge Rezolve Ai’s legacy product discovery system relied on a contextual, AI-based search engine that excelled with subjective queries.
Data flow
Operational events and application data flow into MongoDB Atlas, where the platform stores current state and supports real-time queries for downstream applications and integrations such as AWS, Elasticsearch.
Solution architecture
- Storage
- MongoDB Atlas Stores the operational data and provides the managed document database foundation for Rezolve AI's workload.
- Serving
- MongoDB Atlas Search Provides integrated full-text and faceted search across Rezolve AI's application data.
Architecture clues
- Full-text or faceted search runs inside the MongoDB platform.
- Migration or scaling was completed with zero downtime.
- The deployment integrates with AWS.
Evidence from the source
Rezolve Ai implemented MongoDB Atlas as the foundation for its next-generation product discovery platform. This system can handle contextual and non-contextual queries while reducing overhead.
Rezolve Ai’s legacy product discovery system relied on a contextual, AI-based search engine that excelled with subjective queries. However, the system could not support the precise searches that B2B customers required.
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
Up to 50% reduction in search query latency
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.” “MongoDB Atlas, with its auto-scaling and search node provisioning, eases a lot of the overhead.
Transparency Evidence & methodology
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
How scores are calculated
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Customer reference value82/100
- Problem clarity20/20Measures whether the customer's operational problem is specific enough to compare.
- Implementation depth25/25Rewards named components, data flow, integrations, and architecture detail.
- Outcome specificity12/20Measures whether the reported benefit is concrete rather than a generic claim.
- Quantified impact15/251 metric describe measurable impact.
- Deployment scale10/10Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
- Source authority20/25The main evidence is an official vendor-published customer story.
- Claim traceability21/2510 of 12 key fields link to a named source.
- Direct evidence20/205 direct source quotes retained.
- Date provenance10/10The publication date comes from the source page or publisher.
- Source transparency8/101 source reference document publisher, URL, authority, retrieval, and supported fields.
- Implementation specificity8/10Rewards source-backed components, data flow, quantified impact, and a specific outcome.
Traceability
Sources & provenance
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
- https://www.mongodb.com/solutions/customer-case-studies/rezolve-aiMongoDBFirst-party
Rezolve AI customer story
Supports- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes
- Publication date
Date evidence
- Published on source page Dec 23, 2025 · MongoDB · First-partyOpen date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.
Related
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