What challenges did Observe.AI face before AI implementation?

Growing customer deployments increased inference request volumes across the contact center intelligence platform. The company needed predictable performance, cost efficiency, and reduced operational complexity for large-scale AI inference. Observe.AI also wanted to accelerate experimentation and delivery of new AI-powered features without rearchitecting core systems.

What was the impact of the AI solution?

Observe.AI achieved a 10–15x increase in AI model consumption over roughly one year. The platform supports about 90,000 requests per minute and processes around 100 million tokens per minute across regions. Teams deliver new AI capabilities 40–50% faster while maintaining reliable production performance.

Source

ProductionEvidence: Medium65/100

Observe.AI scales agentic contact center inference using Amazon Bedrock and Amazon Nova Lite

Use case typeCustomer service automationUpdated Jun 13, 2026

Observe. AI is an AI agent platform for customer experience that helps enterprises automate customer interactions with natural conversations and predictable outcomes. As customer deployments and agent populations grew, Observe. AI needed to process large volumes of conversational data with predictable inference performance, consistency, and cost efficiency while reducing operational complexity and accelerating prototype-to-production for new AI features.

Organization: Observe.AI
Industry: Tech & Comms
Location: United States
Published: May 2026

Reported outcomes

10-15x

quantified impactOther quantified impact

40-50%time

Strategic outcomes

Speed & agilityAccelerated delivery of new AI capabilitiesScale & capacityScaled high-volume inference workloadsCost efficiencyImproved inference cost efficiencyNew product / capabilityAdded high-throughput inference capability

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

Normalized claim

Quantified impact: 10-15 x increase

AWS Solutions case studyMay 13, 2026Case studyInferred claimMedium evidence strength

Observe.AI achieved a 10–15x increase in AI model consumption over roughly one year.

Normalized claim

Time: 40-50% increase

AWS Solutions case studyMay 13, 2026Case studyInferred claimMedium evidence strength

Teams deliver new AI capabilities 40–50% faster while maintaining reliable production performance.

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

Customer: Observe.AI
Provider: AWS
Maturity: Production
Linked source: AWS Solutions case study

AI needed to process large volumes of conversational data with predictable inference performance, consistency, and cost efficiency while reducing operational complexity and accelerating prototype-to-production for new AI features

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

1Conversational AI
2Contact center automation
3Inference optimization

Growing customer deployments increased inference request volumes across the contact center intelligence platform.
The company needed predictable performance, cost efficiency, and reduced operational complexity for large-scale AI inference.
Observe.AI also wanted to accelerate experimentation and delivery of new AI-powered features without rearchitecting core systems.

Observe.AI adopted Amazon Bedrock to access models through a single API and expand external model options for production AI workloads.
The company selected Amazon Nova Lite for high-throughput inference to support batch and latency-sensitive use cases.
It integrated inference through Amazon Bedrock directly into existing services, reducing model management and deployment effort across environments.

Technologies

Amazon Bedrock Amazon Nova Lite

Observe.AI achieved a 10–15x increase in AI model consumption over roughly one year.
The platform supports about 90,000 requests per minute and processes around 100 million tokens per minute across regions.
Teams deliver new AI capabilities 40–50% faster while maintaining reliable production performance.

Sources & evidence1

Evidence: Medium65/100Evidence strength