What Happens When an OAG System Goes Through a Real Enterprise Knowledge Workflow?

Описание и применение

Gnosis применяет OAG для преобразования разрозненных корпоративных документов в управляемые знания, обеспечивая ответам доказательства и соблюдение онтологических ограничений.

Задача

Enterprise knowledge is scattered across PDFs, Word files, Excel sheets, and screenshots, making it hard to answer questions with evidence-backed, governed AI outputs.

Результат

Gnosis demonstrates that embedding ontology constraints, provenance tracking, and deterministic validation throughout the knowledge lifecycle yields AI outputs that are traceable, evidence-backed, and governance-compliant, enabling reliable enterprise decision support.

Рабочий процесс

• 1. Constrained extraction of entities and relationships guided by ontology.
• 2. Validation of extracted structures; invalid items sent to review or rejected.
• 3. Construction of a knowledge graph with provenance links to source documents.
• 4. Hybrid retrieval (keyword, semantic, graph traversal) and relevance gating.
• 5. Generation of answer candidates by LLM.
• 6. Citation validation to ensure each sentence cites a valid source.
• 7. Final answer output with citations or a “no information” refusal.

Доказательства

Enterprise knowledge was scattered across PDF contracts, Word specifications, Excel trackers, meeting records, and screenshots.

Источник

The LLM proposes; policy and deterministic code decide which proposals go forward.

Источник

One of the most important behaviors implemented in Gnosis is the ability to refuse an answer.

Источник