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MongoDB Software / SaaS global

Xlrt

Document Processing · Risk Management

Xlrt needed to help a multinational bank overcome data silos and manual contract workflows that slowed forecasting, procurement analysis, and vendor management across many regions.

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ContractUS currently ingests feeds from five different systems across seven countries.
Processes that once took 15 days now take just three, which is 75% faster and more cost efficient.
The bank has more than 40 million customers and operations in around 60 countries.
The payable-aging analysis revealed more than $17.75 million in delayed obligations.

Reported impact

Business outcome

Xlrt helped the bank cut a process from 15 days to 3, surfaced $17.75 million in delayed obligations, and improved forecasting and procurement analysis efficiency across a global operation.

  • Cost Reduction
  • Productivity Improvement
  • Risk Reduction

Solution

Xlrt built its ContractUS intelligent document-processing platform on MongoDB so it could analyze unstructured procurement and finance documents for banks without forcing customers into one deployment model. The company uses MongoDB Enterprise Advanced for the bank's on-premises instance and MongoDB Atlas for customers that prefer managed cloud services. ContractUS ingests data feeds from five systems across seven countries, handles multiple currencies and languages, and stores purchase orders, invoices, credit notes, financial plans, and contract text in a document model that can support both transactional workflows and AI-driven analysis. API integrations connect the platform to existing systems without invasive rewrites, while the unified data layer supports cash-flow forecasting, tariff-clause detection, payable-aging analysis, and auditability. By grounding contract intelligence in one operational store, Xlrt helps financial teams move from fragmented manual review to faster decision support with lower risk of error.

Data flow

Feeds from procurement, finance, and contract systems flow through non-invasive APIs into ContractUS, where MongoDB stores documents and supporting records, AI analysis extracts clauses and obligations, and the resulting insights are used for forecasting, payable-aging reviews, and procurement monitoring.

Solution architecture

2 components · 1 layer
  1. Storage
    • MongoDB Enterprise Advanced Supports the bank's on-premises ContractUS deployment where data sovereignty and local control are required.
    • MongoDB Atlas Provides the managed cloud deployment option Xlrt offers to customers that prefer MongoDB as a service.

Architecture clues

  • ContractUS maintains a full audit trail of user actions.
  • The application supports online transaction processing for procurement, compliance, and corporate workflows.
  • The platform can handle English, Spanish, and Simplified Chinese.
  • Xlrt offers the platform both on premises and as a managed cloud service.

Evidence from the source

Processes that once took 15 days now take just three.
The payable-aging analysis revealed more than $17.75 million in delayed obligations.
“MongoDB was the natural first choice. Our product handles a large volume of confidential customer information, making reliability and robust performance non-negotiable.”
“The bank turned to Xlrt to develop a contract and procurement analysis platform and create a single source of truth.”
Transparency Evidence & methodology 90/100 reference value · 87% 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 value90/100
  • 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 impact15/25
    1 metric describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • 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 provenance8/10
    The publication date comes from publisher-controlled metadata.
  • 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

    Xlrt customer story

    First-party
    https://www.mongodb.com/solutions/customer-case-studies/xlrt
    Source type
    Vendor-published customer story
    Published
    Dec 22, 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

  • Publisher catalog date Dec 22, 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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