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MongoDB Software / SaaS Latin America

iConstruye

Application Refactoring · Enterprise Knowledge Search

Digitizing the construction industry Business-to-business (B2B) digital services company uses a full suite of MongoDB offerings to develop solutions that serve the construction industry.

Read the original story Explore all use cases

Reported impact

Business outcome

20% reduction in time to market 25% increase in deployment frequency rate Enhanced efficiency

  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Digitizing the construction industry Business-to-business (B2B) digital services company uses a full suite of MongoDB offerings to develop solutions that serve the construction industry. iConstruye simplified its database architecture by migrating to MongoDB Atlas. The company’s products streamline supply chain operations, from requesting contract services all the way to making payments. In 2017, the company started using MongoDB Community Edition, a self-managed document database, to meet its database needs. Since then, MongoDB’s document model has helped the company simplify development and boost developer productivity. “MongoDB helps customers like us gain new capabilities as technology evolves,” said Matías Vázquez, vice president of engineering at iConstruye. Adopting Atlas Search and Vector Search helped the company greatly simplify its search architecture.

Data flow

Business documents, tickets, records, and other operational content stay in MongoDB Atlas Vector Search and MongoDB Atlas Search, 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.

Solution architecture

3 components · 2 layers
  1. Storage
    • MongoDB Atlas Stores the operational, document, and context data behind iConstruye's AI-powered search and assistant experiences.
  2. Serving
    • MongoDB Atlas Vector Search Retrieves semantically relevant context from iConstruye's data for AI, search, and recommendation workflows.
    • MongoDB Atlas Search Provides relevance-based text retrieval across iConstruye's documents and application data.

Architecture clues

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

Evidence from the source

“By removing that complexity, our architecture became more efficient and resilient, which enhanced the value we deliver to our clients,”
“MongoDB helps customers like us gain new capabilities as technology evolves,”
“We trust MongoDB architecture, so we don’t have to find and vet external providers to access the latest features and advances.”
“With MongoDB, we can ride the wave of new technologies like generative AI,”
Transparency Evidence & methodology 71/100 reference value · 77% 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 value71/100
  • 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 specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence77%
  • 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 provenance0/10
    The publication date has no documented provenance.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity8/10
    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

    iConstruye customer story

    First-party
    https://www.mongodb.com/solutions/customer-case-studies/iconstruye
    Source type
    Vendor-published 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 Sep 11, 2025 · 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 Apr 10, 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.

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