Reduce hallucinations: controllable · traceable · verifiable
1
Ingest & structure
Turn sources into maintainable knowledge
2
Index & retrieve
Ground answers with evidence
3
Guardrails
Constrain behavior
4
Citations
Cite sources per answer
5
Review × regression
Human-in-the-loop + test sets
We reduce risks via an end-to-end method set to make AI “more accurate, more stable, and more controllable”:
- Multimodal knowledge ingestion: PDF/Word/SOP, Excel/financials/KPI, BI/Dashboard, legal clauses/contracts/cases, recordings/transcripts, etc.
- Import and use: Automatic structure and indexing (knowledge graph / vector index) to build a maintainable knowledge base; professional prompt templates to make it usable for non-technical teams.
- Traceable answers: Cited outputs (auditable and verifiable) with guardrails and access controls.
- Acceptance-ready improvement: Human-in-the-loop review + regression testing (Error Taxonomy × AIEC).
Note: the presentation mentions an AIEC capability check (example pass rate 96%) as a reference framework; actual delivery is governed by the shipped version and acceptance criteria.