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Schematic Intelligence & Agentic Engineering

Transforming complex engineering schematics, P&IDs, and technical diagrams into high-fidelity Knowledge Graphs queried by autonomous MCP AI Agents for automated QA/QC checking, safety relief verification, and cross-document reconciliation.

Schematic Diagram to Knowledge Graph & Agentic Verification
End-to-End Flow: 2D Engineering Schematics → Spatial Computer Vision & NMS → Topological Knowledge Graph → Model Context Protocol (MCP) Agent Swarm → Senior Reviewer Reflection & Automated Redline Scorecards.

1. From Static Diagrams to Living Knowledge Repositories

In technical industries, mission-critical engineering intent remains trapped in dense, multi-page 2D drawing sets—including Piping & Instrumentation Diagrams (P&IDs), Electrical Schematics, Single-Line Diagrams, and Process Flow Diagrams. Traditional engineering reviews require human subject-matter experts to manually cross-reference hundreds of sheets against engineering line lists, equipment indices, and regulatory standards.

We bridge this gap by transforming static CAD outputs and vectorized PDF drawings into connected, queryable Knowledge Graphs. Every line segment, instrument bubble, valve tag, inline component, and continuation boundary is extracted into an active entity layer, enabling instantaneous spatial, structural, and semantic reasoning.

Spatial Computer Vision & Continuation Logic

Our proprietary extraction engine applies targeted OCR combined with Square-Object and Non-Maximum Suppression (NMS) algorithms to identify fine-grained schematic components at high coordinates fidelity:

  • Symbol & Instrument Bubble Classification: High-precision extraction of functional instrument tags (e.g., Pressure Transmitters, Flow Controllers, Level Alarms) and inline equipment.
  • Continuation Arrow Tracing: Automatically discovers off-sheet continuation arrows, matches bidirectional "To/From" labels across disparate drawing pages, and constructs unified topological process lines across entire plant sets.
  • Drawing Index & Title Block Surfacing: Extracts structural drawing numbers, revision flags, and project metadata with automated signature verification.

2. GraphRAG: Topological Grounding for Enterprise AI

Standard Vector RAG fails on technical diagrams because engineering questions demand topological path traversal (e.g., "What isolation valves exist between Pump P-101 and Vessel V-302 along line 4"-HC-1002?").

By storing schematic connectivity in Neo4j Property Graphs, we ground foundational multimodal models (such as Gemini 3.x / Vertex AI) directly in verified physical topology. The LLM does not hallucinate connectivity; it traverses graph paths with deterministic precision, enabling deep semantic interrogation of multi-discipline drawing packages.

3. Distributed Agent Swarms via Model Context Protocol (MCP)

We orchestrate audit and analysis tools using distributed FastMCP (Model Context Protocol) microservices. Rather than relying on monolithic prompts, specialized AI agents are invoked dynamically to perform dedicated tasks:

run_consistency_audit

Executes automated audits for referential integrity of the schematic; provides customized logic all re-inforced by recursive results review

continuity_matching

These large document sets have to retain consistency in specification across multiple page references, these types of checks ensure multiple references across pages are consistent.

symbology_matching

Resolving generic as well as site specific symbology is critical for the dynamic design environments. The symbols are mapped to a data dictionary providing additional functional purpose and specification.

schematic_annotation

Annotation plays a critical role in establishing 'engineering trust' such that original work can be annotated at the exact coordinates of observation.

4. Two-Pass Senior Reviewer Reflection Architecture

Engineering safety demands zero-tolerance for AI hallucinations. Our architecture enforces a dual-stage execution model:

  1. Pass 1 (Draft Generation): The intake agent rapidly parses technical drawings utilizing multimodal context caching (20-minute cached tokens on Vertex AI) and extracts candidate findings against discipline rules.
  2. Pass 2 (Senior Reviewer Reflection): A secondary review agent rigorously audits every finding from Pass 1, verifying exact drawing coordinates, validating document citations, and eliminating false positives before scoring.

5. Safety Calculations & Automated Scorecard Generation

Our pipelines combine quantitative deterministic evaluation with qualitative LLM reasoning:

  • Pressure Safety Relief (PSV) Sizing Checks: Automatically compares design operating pressures against relief valve setpoints and discharge piping capacities.
  • Mechanical Line List Validation: Checks pipe specs, insulation ratings, design temperatures, and pressure classes across multi-sheet drawings.
  • Automated Deliverables: Generates structured multi-tab Excel Audit Scorecards alongside production-ready Annotated Redline PDFs complete with color-coded callouts.

Multi-Channel Enterprise Intake

Workflows can be triggered seamlessly through multiple enterprise channels: direct SSE/REST API calls, Teams / Slack Bots, or Email (where engineers submit and communicate in lightweight, frictionless environments in batch or real-time).