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MongoDB Software / SaaS India

ESG Data & Solutions

Enterprise Knowledge Search · Real-time Analytics

ESG Data & Solutions needed a flexible, secure data platform that could ingest huge volumes of ESG documents and feeds, replace relational and separate vector databases, reduce manual review cycles that took days per company, and satisfy multi-region regulatory requirements for financial-sector clients.

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ESGDS delivers nearly twice the volume of ESG datasets as traditional manual workflows.
ESGSure collects data points from over 20,000 companies and investors.
ESGSure has reduced manual data review time for ESG data sets from multiple days to minutes.
MongoDB's vector capabilities can return sub-second results for every search prompt.

Reported impact

Business outcome

ESGDS nearly doubled data-ingestion capacity, cut manual ESG data review from two to three days per company to minutes, vectorized nearly 400,000 papers within a year, and created a platform that can expand across regions while meeting strict security and compliance demands.

  • Compliance
  • Productivity Improvement
  • Scalability

Solution

ESG Data & Solutions built its ESGSure research platform on MongoDB to manage the constantly changing mix of ESG reports, filings, news, questionnaires, videos, PDFs, and API feeds used by investors and banks. The company first broke away from rigid relational systems with MongoDB Community Edition, then moved the SaaS platform to MongoDB Atlas on AWS so it could scale elastically across regions and client deployment models. Atlas stores diverse operational records and time-stamped ESG data, while MongoDB Atlas Vector Search retrieves relevant passages and datasets for hybrid text and vector queries that feed LLM workflows such as Anthropic's Claude. Dedicated search nodes and direct vectorization inside MongoDB remove the ETL burden of separate vector databases, and Atlas's multi-cloud, client-specific cluster options help ESGDS satisfy GDPR and other regulated-environment requirements while keeping data near real time.

Data flow

Annual reports, filings, news, questionnaires, sanctions lists, PDFs, videos, and API data flow into MongoDB Atlas, where ESGSure stores and vectorizes the records; MongoDB Atlas Vector Search retrieves relevant datasets and passages, which are then passed to LLM workflows such as Anthropic Claude to generate investor-ready insights.

Solution architecture

3 components · 3 layers
  1. Compute
    • Anthropic Claude Consumes the retrieved ESG context to generate AI-driven insights for users of the platform.
  2. Storage
    • MongoDB Atlas Hosts ESGSure's operational data, time-stamped ESG records, and elastic multi-region SaaS deployments.
  3. Serving
    • MongoDB Atlas Vector Search Runs hybrid retrieval over vectorized ESG content to support investor-facing AI workflows.

Architecture clues

  • Clients can run the platform on preferred cloud providers or on-premises to satisfy regulated-environment requirements.
  • Dedicated search nodes are used to preserve retrieval performance under heavy bulk-processing loads.
  • MongoDB time-series capabilities are used to eliminate stale data in ESG workflows.
  • The platform moved from PostgreSQL and Pinecone to MongoDB-backed operational and vector workloads.

Evidence from the source

"ESGSure has reduced manual data review time for ESG data sets from multiple days to minutes."
"Our platform needs to process massive, diverse, and unstructured data sets, so we can then use a combination of large language models (LLMs), real-time data, and vector search capabilities to deliver AI-driven granular, personalized, and actionable insights for investors."
"When it comes to our growth and geographic expansion, MongoDB’s multi-cloud and multi-region capabilities have been key."
"With MongoDB’s built-in vector search, ESGDS doesn’t need any bolt-on vector databases, and can perform hybrid queries that combine vector and text search."
Transparency Evidence & methodology 75/100 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 value75/100
  • 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 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

    ESG Data & Solutions customer story

    First-party
    https://www.mongodb.com/solutions/customer-case-studies/esgds
    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 Mar 16, 2026 · 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 Feb 18, 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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