Engineering Copilot

AI-powered assistant streamlines engineering data management and decision-making for faster development.

Business impact

• Development cycle time — Reduces time needed to complete engineering projects through automation and insights
• Traceability accuracy — Improves tracking of requirements and changes across engineering artifacts
• Decision-making quality — Enables more informed choices by integrating comprehensive engineering data

Data requirements

• Engineering design documents (Text) — Provide detailed specifications and requirements for AI analysis
• Software code repositories (Code) — Supply source code and version history for traceability and impact analysis
• System models and simulations (Numeric) — Offer system behavior data to support decision-making and validation
• Project management tools (Structured) — Contain task and progress data to monitor development status

AI methods and techniques

• Predictive AI — Forecasts development risks and timelines based on historical data patterns
• Generative AI — Generates contextual recommendations and documentation from engineering knowledge graphs
• Agentic AI — Supports autonomous assistance in navigating complex engineering workflows

AI models and model families

GPT-4, Claude, Neo4j Retrieval-Augmented Generation (RAG)

Industries

Real-world evidence

7 documented case studies on record.

Companies using this: Ace Cloud, BMW, EY GDS, Honeywell, R Systems, Superbot, Tata Elxsi.