AWS Media and Entertainment Malaysia

123RF

Document Processing · Security and Compliance

Subsequently, 123RF developed machine learning (ML) models using open-source technology to automate image screening, which are hosted on Amazon Elastic Compute Cloud (Amazon EC2). Additionally, it sought to improve the accuracy of ML models in flagging unsuitable content.

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10-100x

2-3x

The system generates flags for inappropriate content within 1‒2 seconds, comfortably meeting the company’s target timeframe.

With Anthropic Claude 3 models on Amazon Bedrock, 123RF has successfully achieved its objective of fully automating content screening with high accuracy.

Reported impact

Business outcome

With Anthropic Claude 3 models on Amazon Bedrock, 123RF has successfully achieved its objective of fully automating content screening with high accuracy. The system generates flags for inappropriate content within 1‒2 seconds, comfortably meeting the company’s target timeframe. Key reported metrics include cost reduction in batch translation of assets across nine languages 95%; seconds to flag inappropriate content 1-2; faster image screening process 10-100x.

Solution

123RF Achieves 95% Cost Reduction on Content Translation and Up to 100x Faster Content Screening with Generative AI on AWS 123RF, a stock photos agency, is an online resource for creative assets including AI-generated images from text. To automate and improve the accuracy of its content moderation, 123RF turned to AWS. By accelerating AI development on AWS, 123RF can automatically screen images for suitability and copyright issues, flagging inappropriate content within 1–2 seconds. This automation has eliminated complaints about inappropriate images, allowing the company to reallocate resources from manual reviews to business development. Additionally, 123RF has implemented content translation to enhance its global reach and efficiency. 123RF subscribes to AWS Enterprise Support and holds weekly meetings with its technical account manager to collaborate on technology solutions that advance the business.

Data flow

Source data enters the 123RF workflow through its application services, processed with Amazon EC2, Anthropic Claude 3, Amazon Bedrock.

Solution architecture

3 components · 1 layer

Compute
- Amazon EC2 Provides configurable compute capacity for application, training, or inference workloads.
- Anthropic Claude 3 Serves as the primary large language model for generation, analysis, or reasoning tasks.
- Amazon Bedrock Hosts the foundation models and inference APIs that power the generative AI features.

Architecture clues

123RF successfully implemented the solution into production in July 2024 and has since reduced batch translation costs from $120,000 to $6,000 for nine languages.
Although the company achieved significant success in English-speaking markets with its generative AI–based semantic search tool, it faced content discovery challenges in 15 other languages due to English-only titles and
Furthermore, 123RF can seamlessly scale up to process an infinite amount of image data without worrying about compute power availability or labor hours.
Initially skeptical due to previous cost optimization challenges, the 123RF team, together with AWS solutions architects, conducted rapid iterations and small POCs on Anthropic Claude 3 to refine translation prompts.
“Because AI technology—including generative AI—evolves so rapidly, investing in skill development is essential,” Kho says.

Evidence from the source

10-100x

123RF subscribes to AWS Enterprise Support and holds weekly meetings with its technical account manager to collaborate on technology solutions that advance the business.

2-3x

Subsequently, 123RF developed machine learning (ML) models using open-source technology to automate image screening, which are hosted on Amazon Elastic Compute Cloud (Amazon EC2). Additionally, it sought to improve the accuracy of ML models

The system generates flags for inappropriate content within 1‒2 seconds, comfortably meeting the company’s target timeframe.

With Anthropic Claude 3 models on Amazon Bedrock, 123RF has successfully achieved its objective of fully automating content screening with high accuracy.

Transparency Evidence & methodology 95/100 reference value · 87% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

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 value95/100

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
8 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.

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

AWS
123RF customer story

First-party
https://aws.amazon.com/solutions/case-studies/123rf-2024/

Source type
Vendor-published customer story

Published
Oct 20, 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

Publisher catalog date Oct 20, 2024 · AWS · 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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