Teckel AI provides production observability for AI systems. We believe debugging should not mean scrolling through thousands of individual traces. Our platform automatically surfaces what matters so you can fix root causes instead of chasing individual errors.
We built Teckel for three audiences: enterprise teams who need compliance and audit trails, developers who prefer CLI tools over dashboards, and product managers who want to track topics relevant to their domain without writing code.
Debugging manually across thousands of traces is not practical. We use small, custom-trained models to cheaply classify topics so you understand which areas are problematic. At this targeted scale, our agents read traces with full evaluator context to surface actual problems and suggest fixes.
Track any potential problem with customizable patterns. Create evals or use natural language if you prefer to define patterns you want to monitor. Group similar failures, see frequency and impact, and fix issues systematically.
Your code agents (Claude Code, Codex, Cursor) can query Teckel directly. Ask about topics, inspect patterns, pull trace logs for debugging with semantic search. Never leave your development environment. We have useful and beautiful dashboards, but if you prefer terminals we also built Teckel for you.
Unlike other tools that treat documents as metadata, Teckel connects AI responses back to source content. Often AI failures come from legacy documentation giving it bad context, in Teckel see which documents drive answers and where knowledge gaps exist.
Full audit history, compliance-ready trace management, and understanding of how your unique business knowledge interacts with AI systems.
Let your code agent collect and query observability data via CLI and MCP. Semantic search for problems directly in your terminal.
Track and manage topics relevant to your product area. Classify topics, create patterns, and monitor quality without writing code.