PDF & Data Extraction

Turn engineering knowledge into an intelligent, connected system.

Transform requirements, engineering data, and documents into actionable knowledge. AI agents combine Retrieval-Augmented Generation (RAG), vector search, knowledge graphs, and MCP to find information, understand relationships, analyze requirements, and automate complex engineering tasks.

Engineering knowledge is complex, distributed, and difficult to use at scale.

Critical information is often spread across requirements, standards, specifications, test artifacts, project documentation, and other systems.

Finding the right information—and understanding how it relates to existing requirements—can require significant manual effort. As projects grow, identifying duplicates, dependencies, conflicts, and compliance issues becomes increasingly difficult.

Connect your engineering knowledge with Agentic AI.

AI agents combine multiple sources and technologies to retrieve relevant information, understand engineering context, and perform specialized tasks.

RAG and vector search help retrieve relevant information from large volumes of engineering content, while knowledge graphs capture relationships and structured knowledge across requirements, standards, and other project information.

Through MCP, AI agents can connect these intelligence capabilities with engineering applications and workflows, enabling them to retrieve context and perform supported actions across connected systems.

Together, these technologies create an intelligent foundation for analyzing, generating, validating, and managing requirements at scale.

1. Connect
Create custom knowledge bases from ELM modules, structured engineering content, and documents including PDF, Word, Excel, PowerPoint, HTML, TXT, and Markdown.

2. Retrieve
Use RAG and vector search to identify relevant information based on meaning and engineering context—not just exact keyword matches.

3. Understand Relationships
Use knowledge graphs to represent connections between requirements, standards, dependencies, and other engineering information.

4. Analyze
AI agents use retrieved and connected knowledge to detect duplicates, conflicts, inconsistencies, dependencies, and potential compliance issues.

5. Act
Generate requirements and test cases, perform engineering analyses, and execute supported workflows through connected systems and MCP integrations.

Detect Duplicates and Conflicts
Identify semantically similar requirements and uncover potential contradictions or inconsistencies.

Analyze Impact and Dependencies
Discover relationships between engineering artifacts and support automated impact and dependency analysis.

Validate Against Engineering Knowledge
Compare requirements with standards, norms, and other information represented in the knowledge base or knowledge graph to identify potential inconsistencies.

Generate Requirements and Test Cases
Create engineering artifacts from specifications, existing project information, or natural-language instructions.

Support Compliance and Traceability
Use connected engineering knowledge to identify gaps and support the traceability of requirements, including safety-critical requirements.

Automate with AI Agents
Apply specialized agents to engineering tasks and combine them into multi-step workflows rather than treating AI as a standalone question-and-answer tool.

Bring together Agentic AI, MCP, RAG, vector stores, and knowledge graphs to create an intelligent layer across your engineering environment.

Give AI agents the context they need to retrieve the right information, understand relationships, analyze requirements, and turn engineering knowledge into action.

Discover how connected AI agents can help your teams analyze requirements, uncover relationships, automate engineering tasks, and make better use of existing project knowledge.