Use Case Intelligence RSS Search use cases Search
MongoDB Financial Services Europe

Navisio AI

Document Processing · Enterprise Knowledge Search

Rapid adoption of Navisio AI's platform triggered severe latency spikes, elevated CPU and RAM consumption, and costly vertical scaling.

Read the original story Explore all use cases
As it prepares to scale to 1,000 by the end of the year, and eventually toward 5,000 users, the team is confident that MongoDB has given them a database built for massive, cost-effective growth.
Today, the company stands at 500 users.

Reported impact

Business outcome

Navisio AI slashed monthly database costs by over 60%, achieved an eight-month ROI, and freed its small team to focus entirely on the product.

  • Cost Reduction
  • Productivity Improvement
  • Speed to Market

Solution

Rapid adoption of Navisio AI's platform triggered severe latency spikes, elevated CPU and RAM consumption, and costly vertical scaling. MongoDB helped Navisio AI migrate to a secure multi-tenant database on GCP and deploy hybrid search to deliver cited, accurate answers. In an insurance sector where many intermediaries still process documents much as they did three decades ago, Italian InsurTech startup is spearheading a modern digital shift. Navisio AI’s solution is an AI-powered platform with a conversational interface that allows brokers to query private documents and compare them against a public library of 1,600 Italian insurance products. As Navisio AI transitioned from research to commercial launch, the team strategically graduated from local MongoDB Community Edition to to power their cloud operations. The original, multi-database design triggered latency spikes, elevated CPU and RAM consumption, and forced costly vertical scaling.

Data flow

Business documents, tickets, records, and other operational content stay in MongoDB Atlas Vector Search and MongoDB Atlas, where search indexes or vector embeddings are built and then queried in real time to supply grounded results to applications, assistants, or discovery features. Upstream and downstream integrations include AWS, Google Cloud, APIs.

Solution architecture

2 components · 2 layers
  1. Storage
    • MongoDB Atlas Stores the operational, document, and context data behind Navisio AI's AI-powered search and assistant experiences.
  2. Serving
    • MongoDB Atlas Vector Search Retrieves semantically relevant context from Navisio AI's data for AI, search, and recommendation workflows.

Architecture clues

  • AI agents or assistants are layered on top of the MongoDB data platform.
  • The deployment integrates with APIs.
  • The deployment integrates with AWS.
  • The deployment integrates with Google Cloud.
  • Vector search is part of the retrieval layer for semantic or generative-AI use cases.

Evidence from the source

"We were reaching a point of no return,”
“If we wanted to grow our customer base, the only possible solution was to scale the platform vertically, doubling the cost each time.”
“It was a really useful way to put the database and the machines within the same virtual private network,”
“We’re focused on building the harness around our solution—creating an architecture where, even if we change a model tomorrow, we can still deliver the best output for our customers. And we are certain that MongoDB is a key pillar of this harness.”
Transparency Evidence & methodology 100/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

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 value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. MongoDB

    Navisio AI customer story

    First-party
    https://www.mongodb.com/solutions/customer-case-studies/navisio-ai
    Source type
    Vendor-published customer story
    Published
    Oct 8, 2026
    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 Oct 8, 2026 · MongoDB · First-party
    Open 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

See all like this
Google Cloud Archived · Sep 27, 2026 SIGNAL IDUNA Financial Services Germany SIGNAL IDUNA needed to spread AI safely across a large insurance workforce, make a century of internal policy knowledge searchable, and let teams build their own agents without creating a fragmented, insecure shadow-AI… 80% weekly active adoption rate with a +45 NPS in first five months Document ProcessingEnterprise Knowledge Search Document AI · Gemini · Gemini Enterprise Agent Platform MongoDB Sep 9, 2026 Squary AI Software / SaaS United States Squary AI needed to eliminate slow, outage-prone legal search infrastructure that combined MongoDB with OpenSearch and hurt document review speed, cost, and trust. At Quatrini Law Group, Squary AI analyzed more than 460,000 pages and saved over 400 attorney and paralegal hours. Document ProcessingEnterprise Knowledge Search Amazon Bedrock · Claude · MongoDB Atlas OpenAI Jul 31, 2026 Univé Financial Services Europe & UK Univé needed to build organization-wide AI capability across insurance operations so employees could safely redesign their own work instead of waiting for centralized software projects or isolated AI pilots. 85% of licensed users active every week Document ProcessingEnterprise Knowledge Search ChatGPT Enterprise · Custom GPTs · Workspace Agents