Perimattic AI Testing & Validation for LLMs and GenAI on AWS

Reliable, Secure AI Applications - Validated Before Production

Perimattic delivers comprehensive AI Testing and Validation Services that help enterprises and growth-stage teams confidently deploy LLMs, generative AI applications, AI agents, and RAG pipelines on AWS. Our engagement model is designed to surface risks early, quantify model quality, and deliver actionable remediation guidance so your AI systems perform as expected in production.

How We Work - A Phased Engagement Model

Phase 1 - Discovery and Test Strategy (Week 1-2) We conduct a scoping workshop to understand your AI architecture, business objectives, and risk tolerance. Deliverables include a Testing Strategy Document, evaluation criteria matrix, and prioritized test plan.

Phase 2 - Test Design and Execution (Week 3-5) Our engineers build and execute automated evaluation pipelines using Amazon Bedrock, Amazon SageMaker, Amazon CloudWatch, and AWS Lambda. We test across multiple dimensions - accuracy, hallucination rate, prompt robustness, latency, security posture, and bias. Deliverables include a detailed Evaluation Report with scored results and identified failure modes.

Phase 3 - Remediation and Production Readiness (Week 6-8) We deliver a Remediation Roadmap, optimized prompts, hardened pipeline configurations, and a Production Readiness Scorecard. For ongoing needs, we transfer automated testing pipelines to your team with full documentation and runbooks.

What We Test LLM Evaluation - Response accuracy, consistency, factual grounding, and edge-case behavior Hallucination Detection - Systematic identification of fabricated claims, unsupported inferences, and citation failures Prompt Testing and Optimization - Stress-testing prompt templates across diverse inputs to maximize reliability RAG Pipeline Validation - Retrieval relevance, chunk quality, context window utilization, and answer faithfulness AI Agent Testing - Tool-use correctness, multi-step reasoning, error recovery, and guardrail adherence AI Security Testing - Prompt injection resistance, data leakage detection, and adversarial robustness Bias and Fairness Evaluation - Demographic parity, equalized odds, and representation analysis Performance and Load Testing - Latency profiling, throughput limits, and cost-per-inference optimization Regression Testing - Continuous validation across model updates to prevent quality degradation

Example Use Case

An enterprise deploying an internal knowledge-base chatbot built on Amazon Bedrock engaged Perimattic to validate their RAG pipeline before company-wide rollout. Our team executed over 2,000 test scenarios covering retrieval accuracy, hallucination rates, and prompt injection resistance. The evaluation identified critical failure modes in document chunking and context handling, which were remediated before launch - resulting in a production-ready system with validated quality benchmarks.

Scope and Prerequisites Engagements require an active AWS account with the relevant AI/ML services provisioned (e.g., Bedrock, SageMaker) A designated technical point of contact from your team is required for access and context Minimum engagement duration is typically 4 weeks Services focus on testing, evaluation, and validation - model training, data labeling, and ongoing SLA-bound monitoring are not included

Why Perimattic

Our team specializes exclusively in AI quality assurance and validation on AWS. We combine deep expertise in modern evaluation frameworks with production-grade automation to deliver repeatable, scalable testing that integrates into your CI/CD workflows. Every engagement produces transferable artifacts - automated pipelines, scoring dashboards, and documentation - so your team maintains validation capabilities long after our engagement concludes.

Ready to validate your AI systems? Schedule a discovery call to scope your testing needs and receive a tailored engagement plan.