Architecture profile
Describe the deployed system: channel, model, retrieval, tools, memory, data boundaries, and observability.
AI reliability engineering platform
Test the system you actually deployed.
Audit & Fix models the architecture behind an AI product, selects the risks that architecture creates, and proves whether targeted fixes improve it.
Audit surfaces
One method, different AI systems.
Each runner captures different evidence, but every result uses the same severity model and before/after discipline.
Runnable nowRunnable
Chat & RAG
Audit grounded customer-support responses, retrieval quality, safety, precision, and brand behavior.
RAGHallucinationInjectionPrecisionTone
60 executable cases →Runner blueprintBlueprint
Voice agents
Measure the full spoken turn: transcription, reasoning, tools, first audio, interruptions, and handoffs.
STTLatencyBarge-inTTSHandoff
10 designed cases →Runner blueprintBlueprint
Agents & workflows
Test tool selection, permissions, state, retries, approval gates, memory isolation, and task completion.
ToolsAuthorizationStateRetriesApprovals
10 designed cases →Architecture-aware evaluation
Tests follow the system map.
Risk model
Map each component and data flow to the failures it can create and the business impact of those failures.
Suite composer
Select deterministic, adversarial, audio, workflow, and model-graded tests that fit the actual architecture.
Runner adapters
Exercise chat endpoints, voice streams, model providers, tool APIs, and workflow orchestrators through stable contracts.
Evidence normalization
Capture responses, retrieval, tool calls, timing events, audio artifacts, state changes, and trace identifiers.
Grading and report
Apply hard rules and semantic rubrics, diagnose root causes, then compare the same suite before and after a fix.