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OpenAI Financial Services Europe

Singular Bank

Document Processing · Enterprise Knowledge Search

Before Singularity, analyzing a client portfolio meant working across systems: pulling positions, reconciling data manually, and assembling a coherent view ahead of a meeting. That process could take significant preparation time and had to be repeated for every client.

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Singular Bank built an internal assistant that analyzes portfolios, recommends next actions in real time, and saves bankers 60–90 minutes per day.
Singular Bank, a private bank based in Madrid, built Singularity—an internal assistant powered by ChatGPT and Codex—that helps bankers analyze portfolios in real time, prepare for meetings, and...
The system is also used heavily in practice. In a 30-day period, bankers executed more than 3,500 operations, averaging around 120 per day across 19 different workflows.

Reported impact

Business outcome

Singular Bank built an internal assistant that analyzes portfolios, recommends next actions in real time, and saves bankers 60–90 minutes per day.

  • Customer Experience
  • Productivity Improvement
  • Risk Reduction

Solution

Before Singularity, analyzing a client portfolio meant working across systems: pulling positions, reconciling data manually, and assembling a coherent view ahead of a meeting. That process could take significant preparation time and had to be repeated for every client. Singular Bank built an internal assistant that analyzes portfolios, recommends next actions in real time, and saves bankers 60–90 minutes per day. Singular Bank, a private bank based in Madrid, built Singularity—an internal assistant powered by ChatGPT and Codex—that helps bankers analyze portfolios in real time, prepare for meetings, and generate compliant follow-up communications.

Data flow

Singular Bank, a private bank based in Madrid, built Singularity—an internal assistant powered by ChatGPT and Codex—that helps bankers analyze portfolios in real time, prepare for meetings, and generate compliant follow-up communications. Across the team, bankers save 60–90 minutes per day and spend more time advising clients instead of preparing materials.

Solution architecture

2 components · 2 layers
  1. Compute
    • Codex Implements, tests, reviews, or proposes code changes so engineers can move faster with human oversight.
  2. Serving
    • Singularity Gives bankers a real-time portfolio assistant for preparation and compliant follow-up.

Architecture clues

  • Gives bankers a real-time portfolio assistant for preparation and compliant follow-up.
  • Implements, tests, reviews, or proposes code changes so engineers can move faster with human oversight.
  • Singular Bank built an internal assistant that analyzes portfolios, recommends next actions in real time, and saves bankers 60–90 minutes per day.
  • Singular Bank, a private bank based in Madrid, built Singularity—an internal assistant powered by ChatGPT and Codex—that helps bankers analyze portfolios in real time, prepare for...
  • With less time spent searching for information and preparing materials, bankers can focus on what matters most: understanding the client, building relationships, and delivering...

Evidence from the source

Before Singularity, analyzing a client portfolio meant working across systems: pulling positions, reconciling data manually, and assembling a coherent view ahead of a meeting.
Singular Bank built an internal assistant that analyzes portfolios, recommends next actions in real time, and saves bankers 60–90 minutes per day.
Singular Bank, a private bank based in Madrid, built Singularity—an internal assistant powered by ChatGPT and Codex—that helps bankers analyze portfolios in real time, prepare for...
With less time spent searching for information and preparing materials, bankers can focus on what matters most: understanding the client, building relationships, and delivering...
Transparency Evidence & methodology 85/100 reference value · 89% 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 value85/100
  • Problem clarity20/20
    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 specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale0/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • 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 provenance10/10
    The publication date comes from the source page or publisher.
  • 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. OpenAI

    Singular Bank customer story

    First-party
    https://openai.com/index/singular-bank/
    Source type
    Vendor-published customer story
    Published
    May 6, 2026
    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

  • Published on source page May 6, 2026 · OpenAI · 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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