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Google Cloud Financial Services New York City

Gain

Enterprise Knowledge Search

However, as Brian Leenen points out,"The challenge goes beyond automated data collection.

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By combining automation with human validation, Gain has successfully scaled its news production. Its pipeline now verifies and publishes over 17,000 articles per month. This allows the company to systematically apply a rigorous…
Founded in Amsterdam in 2018, Gain saw what others were starting to feel: private markets held more potential than ever, but were getting harder to win in. What began as a better way to do private market research has grown into something…
Nearly 17,000 articles published monthly
The business impact is clearly reflected in the quality of the generated content: the share of sentences flagged as unsubstantiated had been reduced to 0.46% by the time the beta was complete. The small fraction of articles that still…

Reported impact

Business outcome

Nearly 17,000 articles published monthly; Zero unsubstantiated claims across 400 published articles

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement
  • Revenue Growth

Solution

However, as Brian Leenen points out,"The challenge goes beyond automated data collection. To build this detailed mapping of private markets, Gain designed data pipelines that crawl the web to identify new companies, detect financial filings, and track funding rounds and M&A activity. From the beginning, the company chose Google Cloud to power its… To scale its private market coverage globally, Gain integrated Google Cloud's Gemini models using the Gemini platform. The goal: automate intermediate tasks like consolidation, drafting, and fact-checking, without ever compromising on data quality. Key components include Gemini platform coordinates multi-agent workflows, tool calling, and governed automation across the application; AlloyDB for PostgreSQL stores transactional state and application data for the operational workflow at low latency; Cloud Run runs serverless or virtualized application services with elastic scale for production AI workloads; Gemini models provides the.

Solution architecture

5 components · 3 layers
  1. Compute
    • Cloud Run Runs serverless or virtualized application services with elastic scale for production AI workloads.
    • Gemini models Provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis.
  2. Storage
    • AlloyDB for PostgreSQL Stores transactional state and application data for the operational workflow at low latency.
    • AlloyDB Stores transactional state and application data for the operational workflow at low latency.
  3. Orchestration
    • Gemini platform Coordinates multi-agent workflows, tool calling, and governed automation across the application.

Architecture clues

  • "In such a highly competitive market, having the right information at the right time can be the difference between winning a deal and missing an opportunity. At Gain, we collect, verify, and connect fragmented data to…
  • Gain leverages Google Cloud and Gemini to collect, structure, and verify information on private companies, their investors and lenders, and the transactions shaping private markets.
  • However, as Brian Leenen points out,"The challenge goes beyond automated data collection. Our goal is to provide an integrated, reliable view of a complex ecosystem. Therefore, our core value lies in our ability to…
  • In such a highly competitive market, having the right information at the right time can be the difference between winning a deal and missing an opportunity. At Gain, we collect, verify, and connect fragmented data to…
  • To build this detailed mapping of private markets, Gain designed data pipelines that crawl the web to identify new companies, detect financial filings, and track funding rounds and M&A activity. From the beginning, the…

Evidence from the source

"With Google Cloud, we found technical depth, high performance, and ease of use all in one place," Leenen continues. "The intuitive design of the services makes it easy to understand the role of each component, allowing…
Founded in Amsterdam in 2018, Gain saw what others were starting to feel: private markets held more potential than ever, but were getting harder to win in. What began as a better way to do private market research has…
Human analysts then step in to verify, enrich, and contextualize the gathered data. They also interact directly with private company executives, investors, and advisors to obtain insights that aren't publicly available.…
Relative to private markets, public markets offer a standardized information environment: financials are externally audited, issuer communications are regulated as to content and timing, and prices are observable…
To build this detailed mapping of private markets, Gain designed data pipelines that crawl the web to identify new companies, detect financial filings, and track funding rounds and M&A activity. From the beginning, the…
To scale its private market coverage globally, Gain integrated Google Cloud's Gemini models using the Gemini platform. The goal: automate intermediate tasks like consolidation, drafting, and fact-checking, without ever…
Transparency Evidence & methodology 79/100 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 value79/100
  • Problem clarity12/20
    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 specificity12/20
    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 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
    6 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. Google Cloud

    Gain customer story

    First-party
    https://cloud.google.com/customers/gain
    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 25, 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 Oct 9, 2026 · Google Cloud · 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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