What is TraceLogicAI?
TraceLogicAI is an AI assurance and architecture evaluation platform that helps teams compare AI systems, measure quality, security, cost, and reliability, inspect execution evidence, detect regressions, and govern agent permissions. It helps organizations answer a critical question: Can you prove your AI was allowed to do what it just did?
What it solves
- Choose the Right Architecture — Compare AI systems and identify the best fit.
- Stop AI Regressions — Catch quality or security issues before production.
- Control Agent Access — Verify agents use only approved data, tools, and permissions.
- Prove AI Actions — Show what the AI did, why it acted, and if it was authorized.
How it works
- Run the Same Prompt — Test AI architectures under identical conditions.
- Score the Results — Measure quality, cost, latency, security, and reliability.
- Inspect the Trace — Review retrievals, tools, permissions, and policy decisions.
- Recommend & Gate — Rank the best approach and stop regressions before production.
Key Features
AI Architecture Comparison — Compare RAG
MCP
agents
and other workflows
AI Quality Evaluation — Score reliability
cost
latency
safety
and security
Agent Access Governance — Validate permissions
tools
data access
and policies
Execution Evidence — Trace AI actions
decisions
citations
and authorization
Integrations
Anthropic Claude — Primary model and tool-use executionModel Context Protocol (MCP) — Connects AI workflows to external tools through MCP serversSupabase — Authenticationuser accountsrun historyand persistenceGitHub Actions — CI/CD evaluation gatesbuildsand automated security checksResend — Account confirmation and password-recovery email deliveryClaudeChatGPT & Gemini — Supported providers for the optional LLM-as-judge evaluation layerGitleaksSemgrepTrivy & npm audit — DevSecOps security scanning and repository assuranceFlyio / Docker — Reference production deploymentwith OCI portability to Cloud RunECS/FargateRenderRailwayand similar platformsXenova / Hugging Face MiniLM — Local embeddings powering the RAG evaluation pipeline
Lumina
Simulate marketing ideas on AI audiences before you launch
Free
★★★★★5.05.0 (1)
Questions & Answers
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Meet the Team
MD