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MongoDB Commercial and Professional Services India

IGT Solutions

Document Processing · Enterprise Knowledge Search

IGT Solutions needed to replace manual, error-prone workflows and a split relational-plus-vector architecture with a flexible platform that could store large volumes of operational and vector data, support multiple AI services across clouds, and shorten time to market for new client solutions.

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IGT Solutions operates 30 delivery centers and provides end-to-end platforms for over 25,000 users.
MongoDB-led improvements produced a 20% rise in productivity and a 25% improvement in time to market.
Procurement.AI led to a 90% reduction in response times for finance queries at one client.
Recruitment.AI has handled around 45,000 resumés.

Reported impact

Business outcome

MongoDB helped IGT Solutions raise productivity by 20%, improve time to market by 25%, cut finance-query response times by 90% for one client, and scale AI-assisted recruitment workflows that have already handled around 45,000 resumés.

  • Cost Reduction
  • Productivity Improvement
  • Speed to Market

Solution

IGT Solutions standardized many of its AI offerings on MongoDB Atlas so it could combine operational and vector data for travel, hospitality, procurement, recruitment, and other outsourced-service workloads. In the initial project that convinced the team, Atlas stored about 50 gigabytes of data and processed an average of 7 million tickets a day, while MongoDB Atlas Vector Search let the company build intelligent applications over structured and unstructured content without a separate vector store. Running on Azure with Python back-end services, MongoDB also collects operational logs and supports reporting, alerting, compliance, and multi-LLM integrations inside one multi-cloud platform. That common data layer powers solutions such as Procurement.AI and Recruitment.AI, where LLM workflows analyze thousands of resumés, answer finance queries, and schedule interviews, while developers can change schemas and add new AI services with minimal code changes.

Data flow

Operational records, tickets, documents, logs, and resumés are stored in MongoDB Atlas, retrieved through MongoDB Atlas Vector Search, and fed into LLM-driven applications on Azure and Python back ends for search, query answering, reporting, and automated workflow execution.

Solution architecture

3 components · 3 layers
  1. Storage
    • MongoDB Atlas Stores operational and application data for IGT's growing portfolio of AI solutions.
  2. Serving
    • MongoDB Atlas Vector Search Adds vector retrieval so IGT can build intelligent applications over documents and other unstructured content.
  3. Orchestration
    • Azure Provides the cloud environment IGT uses with Python back-end services for many GenAI workloads.

Architecture clues

  • IGT uses MongoDB Atlas Vector Search to combine vector and operational data on one platform.
  • Multi-cloud support lets clients move between providers without changing the core MongoDB pattern.
  • Operational logs, reporting, alerting, compliance, and AI retrieval run from the same MongoDB-backed stack.
  • The company highlights Azure and Python as the back-end environment for GenAI workloads.

Evidence from the source

"Having an operational database and a vector database on a single platform was another important consideration. MongoDB Atlas Vector Search makes service integration much easier."
"Procurement.AI led to a 90% reduction in response times for finance queries at one client."
"Reduced development and system administration times enabled by MongoDB have led to a 20% rise in productivity and a 25% improvement in time to market."
"The pilot project went on to handle 50 gigabytes of data and analyze an average of 7 million tickets a day."
Transparency Evidence & methodology 100/100 reference value · 79% 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
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence79%
  • 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 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

    IGT Solutions customer story

    First-party
    https://www.mongodb.com/solutions/customer-case-studies/igt-solutions
    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 30, 2024 · 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 30, 2025 · 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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