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AWS Financial Services Global

Deriv

Enterprise Knowledge Search

Deriv , one of the world’s largest online brokers, faced challenges accessing vast amounts of data spread across various platforms. It turned to AWS for help and adopted Amazon Q Business , a generative artificial intelligence (AI) tool, to retrieve and process data from multiple sources.

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1.5 seconds
2.5 million
20 minutes
25 years

Reported impact

Business outcome

In just a few weeks’ time, Amazon Q Business made a significant impact across Deriv’s departments. The company saw productivity gains in all areas where the solution was implemented, saving employees time by reducing manual document searches and eliminating many meetings. Users also became more comfortable using AI technology, and increased their search efficiency by learning how to create good prompts.

  • Cost Reduction
  • Productivity Improvement
  • Speed to Market

Solution

Deriv , one of the world’s largest online brokers, faced challenges accessing vast amounts of data spread across various platforms. It turned to AWS for help and adopted Amazon Q Business , a generative artificial intelligence (AI) tool, to retrieve and process data from multiple sources. Deriv had no previous experience working with AI, and had delayed exploring it, assuming it would require hiring a team of senior engineers and researchers to implement and manage. However, as a fully managed service that doesn’t require ML experience, working with Amazon Q Business proved to be extremely intuitive. With a team consisting of a single developer, Deriv set up the data sources it wanted Amazon Q Business to index, then tested a variety of use cases. After just half a day of testing, the solution worked smoothly and launched for employees the very next day.

Data flow

Application logic runs on Amazon Bedrock and is surfaced through Amazon Q Developer, Amazon Q Business.

Solution architecture

5 components · 2 layers
  1. Compute
    • Amazon Bedrock Hosts the foundation models and generative AI application logic.
  2. Serving
    • Amazon Q Developer Assists engineers with code generation, troubleshooting, and modernization tasks.
    • Amazon Q Business Indexes enterprise content and answers employee questions across internal systems.
    • Amazon CloudFront Accelerates content delivery and improves response time for users.
    • AWS Global Accelerator Improves network routing and responsiveness for global users.

Architecture clues

  • These tools helped Deriv improve availability, performance, and security for its applications, and reduced workload latency by 45 percent—from 1.

Evidence from the source

Deriv , one of the world’s largest online brokers, faced challenges accessing vast amounts of data spread across various platforms.
Deriv had no previous experience working with AI, and had delayed exploring it, assuming it would require hiring a team of senior engineers and researchers to implement and manage.
However, as a fully managed service that doesn’t require ML experience, working with Amazon Q Business proved to be extremely intuitive.
It turned to AWS for help and adopted Amazon Q Business , a generative artificial intelligence (AI) tool, to retrieve and process data from multiple sources.
Millions of customers use Deriv’s financial platform, and over the past 25 years, Deriv employees created a massive amount of content to support its services and operations.
This data was hosted in a variety of locations, such as Google Drive, GitHub, internal wiki pages, and discussions and threads in Slack.
Transparency Evidence & methodology 100/100 reference value · 87% 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
    2 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • 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 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 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. AWS

    Deriv customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/deriv-case-study/
    Source type
    Vendor-published customer story
    Published
    Apr 15, 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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