What challenges did Deriv face before AI implementation?

Employee knowledge was spread across multiple systems, making it hard to locate current process and policy information quickly. New hires needed about a month to get up to speed, and recruiters spent significant time reviewing long candidate questionnaires. Marketing teams had to coordinate campaign status across multiple stakeholders and time zones.

What was the impact of the AI solution?

New-hire onboarding time was reduced by 45%, from about one month to about one week. Recruiting task time and questionnaire review effort were reduced by 50%. Workload latency was reduced by 45%, from 1.5 seconds to under 200 milliseconds. The implementation was completed quickly with a single developer and launched the next day after half a day of testing.

Rank#2,027of 3,449

Source

Evidence: Medium50/100

Deriv reduces onboarding time 45% using Amazon Q Business across customer support, marketing, recruiting

Use case typeStaff assistantUpdated Jun 13, 2026

Deriv is an online trading and brokerage company that struggled to make internal knowledge easy to find across Slack, Google Drive, GitHub, internal wiki pages, and other repositories. The scattered information made onboarding training labor-intensive, slowed recruiting questionnaire review, and delayed marketing coordination across distributed teams.

Organization: Deriv
Industry: Finance
Location: CY
Published: May 2026

Reported outcomes

−50%

timeTime & speed

−45%time−45%quantified impact1.5 secondsquantified impact

Strategic outcomes

Speed & agilityReduced onboarding time for new hiresSpeed & agilityImproved application responsiveness

Catalog median for time & speed deployments: −50% across 312 reported metrics. Compare benchmarks →

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Time: 45% decrease

AWS Solutions case studyMay 13, 2026Case studyInferred claimMedium evidence strength

New-hire onboarding time was reduced by 45%, from about one month to about one week.

Normalized claim

Time: 50% decrease

AWS Solutions case studyMay 13, 2026Case studyInferred claimMedium evidence strength

Recruiting task time and questionnaire review effort were reduced by 50%.

Normalized claim

Quantified impact: 45% decrease

AWS Solutions case studyMay 13, 2026Case studyInferred claimMedium evidence strength

Workload latency was reduced by 45%, from 1.5 seconds to under 200 milliseconds.

Normalized claim

Quantified impact: 1.5 seconds decrease

AWS Solutions case studyMay 13, 2026Case studyInferred claimMedium evidence strength

Workload latency was reduced by 45%, from 1.5 seconds to under 200 milliseconds.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Deriv
Provider: AWS
Maturity: Unknown
Linked source: AWS Solutions case study

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 4

1Internal knowledge assistant
2Employee onboarding
3Recruiting productivity

Employee knowledge was spread across multiple systems, making it hard to locate current process and policy information quickly.
New hires needed about a month to get up to speed, and recruiters spent significant time reviewing long candidate questionnaires.
Marketing teams had to coordinate campaign status across multiple stakeholders and time zones.

Deriv implemented Amazon Q Business as a generative AI assistant across customer support, marketing, content creation, and recruiting.
The company connected Amazon Q Business to Slack, Google Docs, Google Drive, GitHub, and web content so employees could ask questions and receive answers grounded in internal sources.
Support teams used it to answer questions from indexed manuals, marketing teams used it to summarize campaign status, and recruiters used it to summarize candidate questionnaires.
AWS Global Accelerator and Amazon CloudFront were used to improve application availability, performance, and security; the article also mentions Amazon Bedrock in relation to future plans for Amazon Q Developer and code transformation.

Technologies

Amazon Q Business AWS Global Accelerator Amazon CloudFront Amazon Bedrock

New-hire onboarding time was reduced by 45%, from about one month to about one week.
Recruiting task time and questionnaire review effort were reduced by 50%.
Workload latency was reduced by 45%, from 1.5 seconds to under 200 milliseconds.
The implementation was completed quickly with a single developer and launched the next day after half a day of testing.

Architecture

Amazon Q Business was connected to internal and external knowledge sources including Slack, Google Docs, Google Drive, GitHub, and web content. Users across support, marketing, and recruiting queried the assistant for summaries and answers. AWS Global Accelerator and Amazon CloudFront supported the underlying application environment, and Amazon Bedrock was referenced for future code-transformation plans.

Sources & evidence1

Evidence: Medium50/100Evidence strength