Reported impact
Business outcome
Applivery has cut feature delivery from weeks to days and automated support with AI, creating a powerful environment for innovation and growth.
- Productivity Improvement
- Scalability
- Speed to Market
MongoDBSoftware / SaaSIT Operations AutomationCloud NativeCybersecurityGenerative AI / LLMProductivity ImprovementScalabilitySpeed to MarketUndated MongoDBMDB#00684aSoftware / SaaSSpainApplivery needed a flexible solution to handle mutating data schemas, with unpredictable device updates and 400% growth ruling out legacy options.Applivery Mastering mutating data with MongoDB Atlas Applivery has scaled by 400%, managing billions of…Archived · Mar 16, 20265IT Operations AutomationMongoDB Atlas · MongoDB Atlas Vector Search0 2026-03-16 5
Applivery
IT Operations Automation
Applivery needed a flexible solution to handle mutating data schemas, with unpredictable device updates and 400% growth ruling out legacy options.
Applivery Mastering mutating data with MongoDB Atlas Applivery has scaled by 400%, managing billions of unstructured device datapoints and accelerating feature delivery from weeks to just days.
Solution
Applivery needed a flexible solution to handle mutating data schemas, with unpredictable device updates and 400% growth ruling out legacy options. MongoDB Atlas’s flexible model handles evolving schemas without migration, supporting billions of data points with full sovereignty. Applivery Mastering mutating data with MongoDB Atlas Applivery has scaled by 400%, managing billions of unstructured device datapoints and accelerating feature delivery from weeks to just days. Our Solution MongoDB Atlas’s flexible model handles evolving schemas without migration, supporting billions of data points with full sovereignty. Industry Computer Software & Technology Startups Product MongoDB Atlas Use Case Gen AI Managing a constant stream of unstructured data is a Spanish business that specializes in endpoint management and security.
Data flow
Documents, logs, or product records are stored in MongoDB Atlas Vector Search, embedded or indexed for MongoDB Atlas Vector Search, and queried by Applivery'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 Applivery's workload.
- Serving
- MongoDB Atlas Vector Search Indexes and retrieves vector embeddings for semantic search and generative-AI workflows.
Architecture clues
- High-availability cluster deployment is part of the design.
- Managed backups and recovery capabilities are part of the production design.
- Migration or scaling was completed with zero downtime.
- Sharding is used to scale data horizontally.
- The deployment integrates with AWS.
- Vector search is part of the retrieval layer for AI and semantic search workloads.
Evidence from the source
Applivery Mastering mutating data with MongoDB Atlas Applivery has scaled by 400%, managing billions of unstructured device datapoints and accelerating feature delivery from weeks to just days.
Applivery has cut feature delivery from weeks to days and automated support with AI, creating a powerful environment for innovation and growth.
Applivery needed a flexible solution to handle mutating data schemas, with unpredictable device updates and 400% growth ruling out legacy options.
MongoDB Atlas’s flexible model handles evolving schemas without migration, supporting billions of data points with full sovereignty.
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 value90/100
- 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 specificity20/20Measures 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 confidence79%
- 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 evidence20/204 direct source quotes retained.
- Date provenance0/10The publication date has no documented provenance.
- Source transparency8/101 source reference document publisher, URL, authority, retrieval, and supported fields.
- Implementation specificity10/10Rewards 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/appliveryMongoDBFirst-party
Applivery 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
Date evidence
- First archived Mar 16, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.Open date source
- Sitemap last modified Feb 19, 2026 · MongoDB · Publisher metadata Publisher-reported modification timestamp; not a publication date.Open 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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