Reported impact
Business outcome
Delivering outstanding returns with high growth potential
- Productivity Improvement
- Revenue Growth
- Scalability
MongoDBSoftware / SaaSSupply Chain OptimizationAI and Machine LearningData PlatformGenerative AI / LLMProductivity ImprovementRevenue GrowthScalability2025 MongoDBMDB#00684aSoftware / SaaSGlobalRelevance AI Talk to an Expert Relevance AI agents deliver 3,000% ROI for clients with MongoDB Industry Transportation & Logistics Product Atlas Vector Search MongoDB Atlas Use Case Logistics Optimization THE CHALLENGE…“A typical AI agent that’s running well can deliver an ROI of around 3,000% for the customer,” said Koh.Apr 25, 20256Supply Chain OptimizationMongoDB Atlas · MongoDB Atlas Vector Search0 2025-04-25 6
Relevance AI
Supply Chain Optimization
Relevance AI Talk to an Expert Relevance AI agents deliver 3,000% ROI for clients with MongoDB Industry Transportation & Logistics Product Atlas Vector Search MongoDB Atlas Use Case Logistics Optimization THE CHALLENGE Using MongoDB to deliver AI-driven growth AI is set to define our working lives for decades to come.
“A typical AI agent that’s running well can deliver an ROI of around 3,000% for the customer,” said Koh.
“Now that figure is close to a billion, and we’re looking at growth of between 2,000% and 5,000% in the coming year.
“We want companies to only be limited by the size of their ideas, not the size of their teams.” Founded in 2020, Relevance AI’s users span an impressive roster of clients, from dynamic tech startups to Fortune 500 giants.
Solution
Relevance AI Talk to an Expert Relevance AI agents deliver 3,000% ROI for clients with MongoDB Industry Transportation & Logistics Product Atlas Vector Search MongoDB Atlas Use Case Logistics Optimization THE CHALLENGE Using MongoDB to deliver AI-driven growth AI is set to define our working lives for decades to come. Switch to MongoDB enables flexibility and manageability The business launched on a proprietary cloud-based NoSQL platform, however quickly realized that a lack of vector search capabilities was a severe limitation. Relevance AI needed a more comprehensive solution that would meet its requirements for handling large and growing volumes of unstructured data and corresponding vectors quickly, reliably and effectively.
Data flow
Documents, logs, or product records are stored in MongoDB Atlas, embedded or indexed for MongoDB Atlas Vector Search, and queried by Relevance AI's application workflows to return relevant results in real time.
Solution architecture
- Storage
- MongoDB Atlas Stores the operational data and provides the managed document database foundation for Relevance AI's workload.
- Serving
- MongoDB Atlas Vector Search Indexes and retrieves vector embeddings for semantic search and generative-AI workflows.
Architecture clues
- APIs are a key integration layer in the application architecture.
- The deployment integrates with AWS.
- The deployment integrates with Azure.
- Vector search is part of the retrieval layer for AI and semantic search workloads.
Evidence from the source
Delivering outstanding returns with high growth potential
Relevance AI Talk to an Expert Relevance AI agents deliver 3,000% ROI for clients with MongoDB Industry Transportation & Logistics Product Atlas Vector Search MongoDB Atlas Use Case Logistics Optimization THE CHALLENGE Using MongoDB to deliver AI-driven growth
Switch to MongoDB enables flexibility and manageability
“Now that figure is close to a billion, and we’re looking at growth of between 2,000% and 5,000% in the coming year.
“We want companies to only be limited by the size of their ideas, not the size of their teams.” Founded in 2020, Relevance AI’s users span an impressive roster of clients, from dynamic tech startups to Fortune 500 giants.
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 value92/100
- 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 impact25/252 metrics 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/relevance-aiMongoDBFirst-party
Relevance 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 Apr 25, 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
Similar use cases
NVIDIA Sample Sep 22, 2026 Sluicebox Sluicebox needed to turn fragmented bills of materials, supplier records, PDFs, CAD files, and emissions inventories into timely product carbon assessments, but manual life-cycle-analysis work was slow, expensive, and… 51-80% lower inference costs Lucy · NVIDIA Nemotron 3 Ultra · carbon graph 
Google Cloud Sample Modified · Sep 17, 2026 Mentana Founded in Australia, Mentana is an AI SaaS platform designed to modernize global supply chain operations. Operating as an AI orchestration layer, the platform unifies real-time reasoning across legacy enterprise… Achieves 99.99% uptime and sub-5-minute scaling using Compute Engine Artifact Registry · Compute Engine · Gemini Google Cloud Sample Modified · Aug 24, 2026 Sourcy Sourcy needed to turn rough buyer requests into manufacturable product specifications and supplier quotes quickly, but traditional product-development handoffs depended on slow manual coordination that was difficult to… Reduced AI costs to roughly $1 per million tokens App Engine · BigQuery · Cloud Run