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AWS Financial Services global

Jefferies

Sales Automation

Jefferies wanted to modernize its infrastructure and then use cloud data and AI services to help bankers, traders, and sales teams surface better insights and advice for clients while controlling cost and operational complexity.

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Reported impact

Business outcome

Jefferies uses AWS to reduce infrastructure cost, improve productivity, and deliver better insights and advice to clients while creating new AI-driven products that extend its competitive advantage.

  • Customer Experience
  • Productivity Improvement
  • Speed to Market

Solution

Jefferies used AWS first to modernize infrastructure and later as a platform for data and AI innovation across the investment bank. Foundational workloads moved onto Amazon EC2, Amazon S3, and AWS Backup so Jefferies could scale data centers and lower costs. On top of that base, the firm now uses Amazon RDS Custom for data services and applies Amazon SageMaker, Amazon Bedrock, and Amazon Q to build products that help salespeople, traders, and bankers surface better insights and advice for clients. This creates a progression from infrastructure modernization to intelligent workflows: core data and applications run in the cloud, governed backups protect them, customized databases support demanding financial applications, and AI services help employees turn firm data into differentiated client outcomes. The result is higher productivity inside the bank and a faster path to new products built on the same AWS foundation.

Data flow

Data and applications are modernized onto Amazon EC2, Amazon S3, and Amazon RDS Custom, then AI and generative-AI services such as Amazon SageMaker, Amazon Bedrock, and Amazon Q turn that cloud foundation into productivity and insight tools for traders, bankers, and sales teams.

Solution architecture

7 components · 4 layers
  1. Compute
    • Amazon EC2 Provides scalable infrastructure for Jefferies' modernized core environments.
    • Amazon SageMaker Supports AI and machine-learning product development for banker and trader insights.
    • Amazon Bedrock Provides generative-AI capabilities for new Jefferies products.
  2. Storage
    • Amazon S3 Stores data supporting Jefferies' cloud-based innovation stack.
    • Amazon RDS Custom Supports customized database environments for financial applications and data services.
  3. Serving
    • Amazon Q Supports knowledge and productivity workflows for teams building and using Jefferies' AI tools.
  4. Governance
    • AWS Backup Protects Jefferies workloads as they move into AWS.

Architecture clues

  • Progressed from infrastructure modernization to AI-driven product innovation on the same AWS base.
  • Supports customized database environments with Amazon RDS Custom.
  • Uses multiple AI services rather than a single model endpoint.

Evidence from the source

It started its journey on Amazon Web Services (AWS) to modernize its infrastructure, scale its data centers, and reduce costs using Amazon EC2, Amazon S3, and AWS Backup.
Jefferies builds on AWS to help drive innovation across its firm to improve productivity and deliver better insights and advice to its clients.
Today, Jefferies builds on AWS for innovation.
continues to innovate using AI and generative AI services such as Amazon SageMaker Amazon Bedrock, and Amazon Q
it enables its salesforce, traders, and bankers to deliver better insights and advice to their clients.
Transparency Evidence & methodology 75/100 reference value · 85% 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 confidence85%
  • 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
    5 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 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. AWS

    Jefferies customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/jefferies-video-case-study/
    Source type
    Vendor-published customer story
    Published
    Jun 2, 2024
    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

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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