Normalized claim
Time: 2 hours decrease
Average briefing time dropped from 2 hours to 25 minutes.
MAX Security launches SCOUT AI agent on Amazon Bedrock/OpenSearch to cut briefing time from 2 hours to 25 minutes
Use case typeAutomotive operations automationUpdated Jun 13, 2026
MAX Security and AWS partner BigData Boutique built SCOUT AI to help analysts query MAX Security’s proprietary intelligence archive in natural language and generate decision-ready briefings faster. The solution uses Amazon Bedrock, Amazon OpenSearch Service, AWS Lambda, Amazon ECS, and Amazon CloudWatch, plus custom chunking and embeddings for semantic retrieval across archived intelligence. SCOUT AI also generates real-time summaries, maps, and visualizations to support threat analysis and broader access to vetted intelligence.
- Organization
- MAX Security
- Industry
- Other
- Location
- Israel
- Published
- June 2026
Reported outcomes
+25%
productivityProductivity & throughput
2 hourstime25 minutestime7 hourstime+42%quantified impact
Strategic outcomes
Speed & agilityReduced briefing preparation timeNew product / capabilityEnabled natural-language intelligence queryingNew product / capabilityGenerated summaries, maps, and visualizationsRisk & complianceReduced hallucination risk
Catalog median for productivity & throughput deployments: +40% across 108 reported metrics. Compare benchmarks →
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Time: 25 minutes decrease
Average briefing time dropped from 2 hours to 25 minutes.
Normalized claim
Time: 7 hours decrease
The solution saved about 7 hours of research per week per analyst.
Normalized claim
Quantified impact: 42% increase
More than 42% of outputs were rated highly relevant by analysts.
Normalized claim
Productivity: 25% increase
Analyst efficiency improved by about 25% versus manual research.
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- MAX Security
- Provider
- AWS
- Maturity
- Production
- Linked source
- AWS Solutions Case Study
Producing decision-ready briefings from a pre-vetted intelligence archive was time-intensive and created high operational overhead
Customer identity supportedSource describes one deploymentMaturity supported
Primary read
Use case focus
Showing 3 of 4
- 1Agentic AI
- 2Search and Retrieval
- 3Business Intelligence
- Producing decision-ready briefings from a pre-vetted intelligence archive was time-intensive and created high operational overhead.
- Analysts had limited capacity to cover more high-priority topics.
- BigData Boutique implemented MAX SCOUT AI, an AI agent that lets users query proprietary intelligence using natural language.
- The system plans multi-step queries, retrieves grounded answers from the closed MAX archive, and generates summaries, maps, and visualizations for threat analysis.
- The implementation uses custom chunking, Cohere embeddings, a GenAI router/planner, Amazon Bedrock for generation, Amazon OpenSearch Service for retrieval, AWS Lambda for ETL and indexing, Amazon ECS for hosting, and Amazon CloudWatch for monitoring.
- Average briefing time dropped from 2 hours to 25 minutes.
- The solution saved about 7 hours of research per week per analyst.
- More than 42% of outputs were rated highly relevant by analysts.
- Analyst efficiency improved by about 25% versus manual research.
- The system reduced hallucination risk by restricting outputs to trusted intelligence.
Architecture
SCOUT AI is an AWS-hosted agent application built by BigData Boutique for MAX Security. It uses custom chunking and embeddings to index proprietary intelligence in Amazon OpenSearch Service. A GenAI router/planner orchestrates analytical tools and query steps. Amazon Bedrock provides hosted model access for generation and reasoning, AWS Lambda runs serverless ETL and embedding/indexing workflows, Amazon ECS hosts the agent service, and Amazon CloudWatch monitors health and usage.
Implementation partners1
Sources & evidence1
Evidence: Medium65/100Evidence strength
- Customer explicitly identified
- Deployment status explicitly supported
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Customer StoryPublished: Jun 8, 2026Publisher: AWSEvidence: VendorConfidence: Medium
AI-generated summary. Verify important details with the linked sources before relying on this case.
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