About LucidityDB
Infrastructure for AI systems that need to explain their outputs.
What LucidityDB Does
LucidityDB wraps AI model calls with instrumentation that captures why outputs were generated, not just what was generated. Every query returns structured metadata: confidence scores, reasoning chains, model attribution, and latency breakdowns.
This metadata enables debugging, auditing, and quality control for AI-powered applications.
Core Capabilities
- 1. Confidence Scoring — Each response includes calibrated confidence estimates based on model uncertainty, retrieval quality, and prompt matching.
- 2. Reasoning Traces — Step-by-step logs showing how the model processed the query, what context was retrieved, and which reasoning path was taken.
- 3. Multi-Model Routing — Automatically route queries to appropriate models based on complexity, cost, and latency requirements.
- 4. Audit Trails — Complete logging of all queries, responses, and metadata for compliance and debugging.
Use Cases
Debugging AI Failures
When an AI system produces wrong output, reasoning traces show exactly where the logic failed.
Quality Monitoring
Track confidence distributions over time to detect model drift or retrieval degradation.
Compliance Auditing
Maintain complete records of AI decisions for regulatory requirements.
Cost Optimization
Route simple queries to cheaper models while reserving expensive models for complex tasks.
Technical Details
LucidityDB runs as a proxy layer between your application and AI providers. It intercepts API calls, adds instrumentation, and returns enriched responses with metadata attached.
Supports OpenAI, Anthropic, and other major providers. Available as managed service or self-hosted deployment.