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MongoDB Software / SaaS Italy

TeamSystem

Customer Service Automation · Enterprise Knowledge Search

TeamSystem aimed to embed AI for smooth content access on a secure, scalable platform; choosing MongoDB Atlas unified data and boosted innovation.

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By leveraging MongoDB Atlas’s automatic horizontal scaling, TeamSystem has taken the platform from an initial pilot of 50,000 users to half a million, without compromising search performance or latency.
TeamSystem is a leading provider of digital management solutions, trusted by over 2.5 million customers across Europe, from freelancers to major corporations and across diverse industries.

Reported impact

Business outcome

TeamSystem built a high-performance, scalable AI platform for 500K users that cuts task times by 40%, boosts productivity, and simplifies data management.

  • Customer Experience
  • Productivity Improvement
  • Scalability

Solution

TeamSystem aimed to embed AI for smooth content access on a secure, scalable platform; choosing MongoDB Atlas unified data and boosted innovation. TeamSystem combined full-text, metadata and vector search using MongoDB Atlas for precise, secure AI-powered queries. Based in Italy, with a portfolio of more than 300 products, a workforce of 4,800+, and expansion into markets such as Spain, France, Turkey, and Denmark, TeamSystem is driving digital transformation across European business. In 2024, TeamSystem launched a five-year R&D plan to embed AI deeply into its product ecosystem, focusing on real user needs. “There’s a lot of hype around AI, we wanted to be more pragmatic,” says Gianluca Genga, AI & Platform Engineering Manager, TeamSystem. However, with such a vast and expanding product catalog, silos of data and fragmented workflows made maximizing AI’s benefits challenging.

Data flow

Application, customer, and workflow data is consolidated in MongoDB Atlas, indexed or enriched where needed, and then delivered to downstream applications, analytics, or AI features through low-latency queries and APIs.

Solution architecture

1 components · 1 layer
  1. Storage
    • MongoDB Atlas Stores the operational, document, and context data behind TeamSystem's AI-powered search and assistant experiences.

Architecture clues

  • AI agents or assistants are layered on top of the MongoDB data platform.
  • Vector search is part of the retrieval layer for semantic or generative-AI use cases.

Evidence from the source

“And we didn’t just need a database. We needed a whole plethora of adjacent services.”
“The difficulties users were having in moving through regulated fiscal environments is where we first saw the opportunity for AI to add value.”
“There’s a lot of hype around AI, we wanted to be more pragmatic,”
“We chose MongoDB Atlas because it’s not just a database,”
Transparency Evidence & methodology 80/100 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 value80/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    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 impact15/25
    1 metric describe measurable impact.
  • Deployment scale5/10
    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

    TeamSystem customer story

    First-party
    https://www.mongodb.com/solutions/customer-case-studies/teamsystem
    Source type
    Vendor-published customer story
    Published
    Sep 30, 2025
    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 Sep 30, 2025 · 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.

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