What challenges did Haast face before AI implementation?

Legal and marketing teams faced slow, time-consuming manual compliance reviews across documents, long-form videos, websites, and podcasts. AI-generated campaigns increased review iterations and made approval cycles unsustainable.

What was the impact of the AI solution?

Improved productivity by over 80%. Saved more than 500 hours of manual review time.

Source

ProductionEvidence: Medium65/100

Haast automates compliance approvals with Amazon Bedrock

Use case typeCompliance automationUpdated Jun 13, 2026

Haast is a marketing compliance software for enterprises in regulated sectors like insurance and banking that automates compliance reviews across documents, websites, and multimedia. The platform uses Amazon Bedrock to power custom-built AI agents that analyze content, identify and classify compliance risks, recommend changes, and provide contextual explanations and suggested remediations. Haast also runs backend services on Amazon EKS and uses Amazon EKS Auto Mode for data pipelines, with private access to Amazon Bedrock APIs through VPC endpoints.

Organization: Haast
Industry: Insurance
Location: United States
Published: May 2026

Reported outcomes

Content-processing capacity: 4×

Productivity & throughput

Manual review time saved: More than 500 hoursCampaign launch speed: Up to 3×

Catalog median for productivity & throughput deployments: +40% across 100 reported metrics. Compare benchmarks →

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

Normalized claim

Productivity improvement: 80% increase

AWS Case StudyMay 27, 2026Case studyExplicit claimMedium evidence strength

improved productivity for legal and marketing teams by over 80 percent

Normalized claim

Manual review time saved: 500 hours decrease

AWS Case StudyMay 27, 2026Case studyExplicit claimMedium evidence strength

saving more than 500 hours in compliance review time

Normalized claim

Content-processing capacity: 4 x increase

AWS Case StudyMay 27, 2026Case studyExplicit claimMedium evidence strength

expanding content-processing capacity fourfold

Normalized claim

Campaign launch speed: 3 x increase

AWS Case StudyMay 27, 2026Case studyExplicit claimMedium evidence strength

customers have also reported up to three times faster campaign launches

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

Customer: Haast
Provider: AWS
Maturity: Production
Linked source: AWS Case Study

Deployed supporting infrastructure on Amazon EKS and Amazon EKS Auto Mode with secure VPC endpoint access to Amazon Bedrock

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

1Compliance automation
2Content moderation
3Document processing

Built agentic AI workflows on Amazon Bedrock using foundation models such as Claude, Llama, and Mixtral.
Created custom AI agents that simulate legal and compliance reviewers, flag risks by timestamp and location, and guide marketers to remediated sections.
Deployed supporting infrastructure on Amazon EKS and Amazon EKS Auto Mode with secure VPC endpoint access to Amazon Bedrock.

Technologies

Amazon Bedrock Amazon Elastic Kubernetes Service Amazon EKS Auto Mode Amazon VPC

Improved productivity by over 80%.
Saved more than 500 hours of manual review time.

Architecture

Haast built an agentic compliance automation platform on AWS. Its backend APIs and services run on Amazon Elastic Kubernetes Service (Amazon EKS), large-scale regulatory data pipelines use Amazon EKS Auto Mode, and generative AI workloads use Amazon Bedrock foundation models via private VPC endpoint access. Custom AI agents analyze documents, websites, and multimedia to detect and classify compliance risks and suggest remediations.

Sources & evidence1

Evidence: Medium65/100Evidence strength