Kadropic Labs
Kadropic Labs offers LEO Soul, a metacognitive layer that sits between users and any LLM, deciding whether to answer, ask, confirm, refuse, or hold based on calibrated confidence assessments. The system provides certified abstention with guaranteed error bounds, pressure resistance, and adaptive speed routing for high-stakes queries. It is the first product from the company's Project LEO research initiative toward decentralized AGI.
- Artificial Intelligence
- AI Agents
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Standard LLM agents respond with uniform confidence regardless of their actual certainty, often guessing under pressure or producing hallucinated outputs when faced with ambiguous or high-stakes queries. This lack of calibrated self-assessment forces users to either distrust all outputs or manually verify every response, undermining reliability in critical applications.
Solution
LEO Soul is a metacognitive layer that intercepts user messages before they reach any LLM, deciding whether to answer, ask, confirm, refuse, or hold based on internal confidence assessments. The system implements calibrated confidence, ensuring that when it states 90% certainty, it is correct approximately 90% of the time. It supports certified abstention with a guaranteed error bound, prompting for clarification or holding responses rather than guessing. Pressure resistance prevents the agent from caving to user pushback unless genuinely new evidence is presented. Adaptive speed routing uses a fast path for straightforward queries and deep verification for high-stakes ones, balancing latency with accuracy. All memory remains user-owned, and the layer operates with verifiable, privacy-preserving properties via Zero-Knowledge Fragmentation. The product is live and available today as the first step of the broader Project LEO research initiative.
Target Audience
Primary users are developers and enterprises deploying LLM-powered agents in high-stakes domains such as healthcare, finance, and legal services where calibrated confidence, abstention, and auditability are essential for operational safety and regulatory compliance.
Features
- Metacognitive decision engine that classifies each query into answer, ask, confirm, refuse, or hold before any model inference
- Calibrated confidence scoring system with empirically validated accuracy alignment on a 0-100% scale
- Certified abstention mechanism with formal error bounds that guarantees refusal over guessing when uncertainty is high
- Pressure resistance protocol that maintains stance under user pushback, only revising when new evidence is introduced
- Adaptive speed routing with fast-path processing for low-risk queries and deep verification checks for high-stakes scenarios
- Zero-Knowledge Fragmentation (ZKF) architecture enabling verifiable, privacy-preserving distributed inference without central data aggregation
- User-owned persistent memory layer that maintains state across sessions without exposing raw data to third parties