Perimattic AI Observability and LLM Monitoring on AWS

Production AI Observability Services on AWS

Perimattic delivers AI observability services that give engineering and ML platform teams real-time visibility into production LLMs, generative AI applications, RAG pipelines, AI agents, and machine learning models deployed on AWS. We help you detect issues before they impact users, reduce inference costs, and maintain enterprise-grade governance over your AI systems.

How We Work: Phased Engagement Model

Our engagements follow a structured three-phase approach:

Phase 1 - Discovery and Assessment (1-2 weeks) We audit your current AI infrastructure, identify observability gaps, and deliver an AI Observability Architecture Document with recommended metrics, tooling, and integration points.

Phase 2 - Implementation (2-6 weeks) We build and deploy your monitoring stack using Amazon CloudWatch, Amazon Bedrock, Amazon SageMaker, AWS Lambda, and leading AI observability platforms. Deliverables include configured dashboards, alert rules, evaluation pipelines, and operational runbooks.

Phase 3 - Optimization and Handoff (Ongoing or 2-4 weeks) We tune thresholds, refine evaluations based on production data, train your team, and optionally provide ongoing managed AI operations.

What We Monitor LLM Behavior: Response quality, hallucination rates, prompt effectiveness, and token usage patterns Performance: Latency, throughput, error rates, and model drift across endpoints Cost: Token consumption, inference spend by model and application, cost anomaly detection RAG Pipelines: Retrieval relevance, context window utilization, and grounding accuracy AI Agents: Tool call success rates, reasoning chain quality, and multi-step workflow completion Security: Prompt injection attempts, PII leakage detection, and access pattern anomalies

AWS Integration

We build natively on AWS services including Amazon Bedrock for model invocation monitoring, Amazon SageMaker for ML model tracking, Amazon CloudWatch for centralized metrics and alarms, and AWS Lambda for event-driven evaluation pipelines. Our solutions integrate with your existing AWS infrastructure without requiring data to leave your account.

Use Case Example

For organizations running customer-facing AI applications - such as a financial services chatbot generating compliance-sensitive responses or a healthcare RAG pipeline retrieving clinical guidelines - our observability layer continuously evaluates response accuracy, flags hallucinated content, monitors regulatory adherence, and alerts teams to degradation before end users are affected.

Services Included AI Observability Strategy and Architecture LLM Monitoring and Evaluation Prompt and Response Analytics Hallucination Detection and Scoring Model Performance and Drift Detection RAG Pipeline Evaluation AI Agent Monitoring Token Usage and Cost Optimization Automated Alerting and Dashboards AI Security Monitoring MLOps Integration Continuous AI Optimization

Prerequisites

To engage with Perimattic, you need an active AWS account with deployed or in-development AI/ML workloads. Your team should provide a technical point of contact with access to relevant AWS services. We support organizations running models on Amazon Bedrock, SageMaker, or self-hosted infrastructure on AWS.

Get Started

Contact us at sales@perimattic.com  to schedule a discovery call and receive a preliminary observability assessment for your AI systems.