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

Description and use

Gnosis applies OAG to transform scattered enterprise documents into governed knowledge, ensuring answers are evidence-backed and comply with ontology constraints.

Problem

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

Outcome

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.

Workflow

• 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.

Evidence

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

Source

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

Source

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

Source