Proactive hierarchical question generation
The AI infers goals and context first, then proposes the right questions and guidance.
Core Technology
Technology
A practical architecture linking LLMs, RAG, speech understanding, and expert feedback to real products.
RootAI technology stack
A practical technology stack designed to make complex work easier, more accurate, and more explainable.
Technology philosophy
RootAI prioritizes question design, grounded response generation, and expert feedback integration over raw model performance alone.
RAG-based domain inference
It connects manuals, regulations, guidelines, and interview data through retrieval-augmented generation.
Speech and dialogue understanding
Real-time STT, dialogue analysis, keyword extraction, and structured interview workflows turn field conversations into data.
Expert feedback learning loop
Human corrections and refinements are continuously folded back into the learning loop.
Multi-agent reasoning
Multiple agents reason across effectiveness, safety, and suitability to produce more explainable outcomes.
Field-connected implementation
We implement production-grade systems spanning LLMs, APIs, monitoring, vision AI, and IoT integration.
Implementation range
- LLM orchestration
- Retrieval & vector search
- Speech recognition and dialogue parsing
- Admin monitoring and feedback loops
- API, web, app, and device integration