What challenges did New Math Data face before AI implementation?

The customer faced long buyer proposal creation times due to manually designing custom furniture concepts and sifting through 500-1,000 products to create personalized recommendations, causing buyer decisional overload and mismatching.

Source

ProductionEvidence: Medium65/100

New Math Data Cuts Custom Furniture Design Time From Hours to Seconds Using AI on AWS Bedrock

Use case typeCustomer personalizationUpdated Jun 13, 2026

New Math Data, a visualization software SaaS company in manufacturing furniture, addressed long buyer proposal creation times caused by manual custom furniture design and product recommendation processes. They deployed an AI-powered generative design engine using AWS Bedrock for AI image generation and embeddings, Amazon S3 for data storage, and AWS Lambda for orchestrating design generation and matching logic. Their solution includes a vector database similarity matching system, RAG architecture processing multimodal catalog data and buyer purchase history on S3, and automated workflow from design generation to quote preparation. The implementation resulted in a 90%+ reduction in time to create buyer proposals and moved proof-of-concept to production; a second proof-of-concept enables automated production quoting for AI-designed custom furniture.

Organization: New Math Data
Industry: Manufacturing
Location: United States
Published: November 2025

Reported outcomes

Time: More than 90% lower

Time & speed

Planned next steps

Successfully moved from proof-of-concept to production with expanded capabilities for automated production quoting.

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

Normalized claim

Time: 90% decrease

New Math Data Case StudiesNov 4, 2025Case studyInferred claimMedium evidence strength

Reduced buyer proposal creation time by over 90%.

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

Customer: New Math Data
Provider: AWS
Maturity: Production
Linked source: New Math Data Case Studies

They deployed an AI-powered generative design engine using AWS Bedrock for AI image generation and embeddings, Amazon S3 for data storage, and AWS Lambda for orchestrating design generation and matching logic

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

1Personalization & Recommendation
2Generative Design
3Workflow Automation

They implemented an AI-powered generative design engine using AWS Bedrock for image generation and embeddings.
Amazon S3 was used for data storage, and AWS Lambda orchestrated the design generation and matching logic.
The system uses AI embeddings, similarity matching algorithms, knowledge graphs, and large language models to understand preferences and style.
Multimodal AI processes both structured catalog data and visual product information to generate new furniture designs.
Automated marketing content generation creates detailed product descriptions and personalized sales messages.

Technologies

Amazon Bedrock AWS Lambda Amazon S3

Reduced buyer proposal creation time by over 90%.

Architecture

The architecture includes an AWS Bedrock-powered context engine with vector database to generate product recommendations and new furniture designs, multimodal data processing on Amazon S3, and AWS Lambda orchestration of workflows.

Sources & evidence1

Evidence: Medium65/100Evidence strength