Investigation scopes, regulatory requirements, and engineering practices live in documents and in people’s heads. Vedana converts this operational knowledge into a structured knowledge graph, so AI systems can reason over it and produce verifiable answers.
Vedana provides a base model for the Development domain: projects, technical assignments, investigations, work items, regulatory standards, and engineering requirements.
This model is extended with your internal documents, engineering practices, and company-specific rules.
The result is a structured knowledge base where project data, investigation scope, and regulatory constraints are represented as connected entities.
Vedana models a domain as a graph of anchors (entities), attributes (properties), and links (relationships).
In this Development demo, projects, technical assignments, investigations, work items, and regulatory requirements are represented as typed nodes connected by explicit relationships.
This structure allows AI systems to reason over engineering data instead of relying only on text prompts.
Vedana parses engineering documents such as technical assignments, investigation reports, and regulatory standards.
Relevant entities are extracted and normalized into structured records: projects, work items, investigations, requirements, and regulations.
Extracted entities are connected into a knowledge graph.
For example:
Technical Assignment → Work Item → Investigation → Regulation → Requirement
These relationships encode engineering dependencies and constraints.
When a user asks a question, Vedana traverses the graph and applies domain rules.
Answers are generated from structured data and traceable relationships, not from raw text generation.
Each step of the reasoning process can be inspected and verified.