Expert Knowledge Capture

Expert knowledge is distilled into reusable decision trees and AI agents to automate complex tasks and scale expertise.

Business impact

• Knowledge deployment scale — Expands reach of expert knowledge to thousands, improving organizational learning and consistency
• Operational efficiency — Automates expert responses, reducing manual effort and speeding up workflows
• Calibration speed for 108 of 112 qubits — Speeds up complex calibration tasks by 4.5×, enabling faster hardware readiness

Data requirements

• Expert knowledge databases (Text) — Provide foundational domain expertise for AI training and query responses
• Operational telemetry and logs (Numeric) — Supply real-time data to contextualize expert knowledge and improve decision accuracy
• Manual training inputs (Text) — Human-in-the-loop annotations and corrections refine AI understanding and performance

AI methods and techniques

• Agentic AI — Autonomously executes expert knowledge workflows and adapts to new scenarios
• Symbolic AI — Encodes expert rules and decision trees for transparent, auditable knowledge representation
• Predictive AI — Forecasts outcomes based on expert knowledge and operational data to guide decisions

AI models and model families

GPT-4o, Claude, Custom LLM agents

Industries

Real-world evidence

3 documented case studies on record.

Companies using this: Hangzhou Institute for Advanced Study, National Grid, SaaStr.