What challenges did New Relic face before AI implementation?

Engineers spent significant time searching fragmented documentation and systems for knowledge and operational questions, sometimes taking more than a day. New Relic needed improved data security, consistent response quality, and faster response times at enterprise scale.

What was the impact of the AI solution?

Reduced information search time by 95%. Processes over 1,000 daily queries. Maintained 80% accuracy in responses. Kept responses under 20 seconds. Automated complex operational workflows such as permission requests and rate-limit management.

Source

New Relic transforms productivity with generative AI on AWS (New Relic NOVA)

Use case typeKnowledge managementUpdated Feb 9, 2026

New Relic built New Relic NOVA, an enterprise AI assistant, to help engineers and employees find answers across fragmented internal documentation and systems. The solution uses Amazon Bedrock, Amazon Bedrock Knowledge Bases, Amazon Kendra, Amazon Q Index, Amazon S3, Amazon Nova Lite, Amazon Nova Pro, Strands Agents SDK, and MCP to support knowledge retrieval and transactional workflows. It integrates with Confluence, GitHub, Salesforce, Jira, Slack, and internal systems, and includes PII detection, masking, and an LLM-as-a-judge evaluation framework.

Organization: New Relic
Industry: Tech & Comms
Location: United States
Published: February 2026

Reported outcomes

−95%

information search timeTime & speed

1,000 queries/daydaily queries processed+80%response accuracy

Strategic outcomes

Speed & agilityKept responses under 20 secondsOther strategic outcomeAutomated permission requests and rate-limit management

Catalog median for time & speed deployments: −60% across 727 reported metrics. Compare benchmarks →

Primary read

Use case focus

Showing 3 of 3

1Knowledge management
2Workflow automation
3Agent orchestration

Engineers spent significant time searching fragmented documentation and systems for knowledge and operational questions, sometimes taking more than a day.
New Relic needed improved data security, consistent response quality, and faster response times at enterprise scale.

Built New Relic NOVA as a multi-layer AI assistant with a central orchestrator that routes requests to specialized agents.
Used Amazon Bedrock Knowledge Bases for Confluence, Amazon Kendra for GitHub Enterprise, Amazon Q Index for Slack history, and Strands Agents for Salesforce and Jira workflows.
Added custom document enrichment, PII detection and masking, and an LLM-as-a-judge evaluation framework to improve retrieval quality and latency.
Used Amazon Nova Lite and Amazon Nova Pro through Amazon Bedrock to balance response quality, latency, and cost efficiency.

Technologies

Amazon Bedrock Amazon Bedrock Knowledge Bases Amazon Kendra Amazon Q Index Amazon S3 Amazon Nova Lite Amazon Nova Pro Strands Agents SDK MCP

Reduced information search time by 95%.
Processes over 1,000 daily queries.
Maintained 80% accuracy in responses.
Kept responses under 20 seconds.
Automated complex operational workflows such as permission requests and rate-limit management.

Architecture

A central orchestration layer routes user intents to retrieval and action agents. The retrieval layer uses Amazon Bedrock Knowledge Bases, Amazon Kendra, and Amazon Q Index over multiple internal data sources, while the action layer uses Strands Agents and MCP for transactional workflows. Security controls include PII detection and masking, and performance is tuned with an LLM-as-a-judge evaluation framework.

Implementation partners1

AWS Generative AI Innovation Center

Sources & evidence1

Groundedness: 5/5Type: Blog PostPublished: Feb 9, 2026Publisher: AWSEvidence: VendorConfidence: Medium

Primary source

AI-generated summary. Verify important details with the linked sources before relying on this case.

Explore related AI use cases

Customer evidence