Can we trust AI in production?
Practical frameworks, testing methodologies, evaluation systems, and reliability engineering patterns for modern AI applications.
The AI quality stack, covered
Eight pillar hubs across AI testing, evaluation, agents, RAG, governance, and Oracle AI.
AI Testing
Frameworks, metrics, and CI gates for LLM apps.
Agent Testing
Trajectory evals, tool-call assertions, SLOs.
LLM Evaluation
Judges, datasets, regression, monitoring.
RAG Validation
Retrieval, reranking, faithfulness, hallucination.
Hallucination Detection
Claim-level NLI, citation gating, judges.
AI Observability
Traces, prompts, costs, and drift in production.
AI Governance
Risk tiers, audit trails, HITL, compliance.
Oracle AI Testing
Fusion AI Agents, ERP, quarterly updates.
Built by AI quality practitioners
Independent. Methodical. Battle-tested in regulated environments. The platform AI startup founders, CTOs, engineering managers, and QA leaders trust.
Independent Assessment
We don't sell models. We test them.
Production-Grade Methodology
Frameworks battle-tested in regulated industries.
Enterprise + Oracle Expertise
Decade of Oracle Fusion QA meets modern AI.
Career-Ready Curriculum
Six tracks for AI quality engineers.
Reliability Engineering Mindset
SLOs, observability, postmortems for AI.
Governance That Ships
Audit-friendly without killing throughput.
From The AI Reliability Blog
Frameworks, anti-patterns, and field notes from validating AI in production.
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Ready to validate your AI?
Independent assessments from senior AI quality engineers.