Introduction
Acuvate’s DiagramIQ was built to turn complex P&ID (Piping & Instrumentation Diagrams) drawings into structured, usable digital intelligence. In the previous blog of this series, we explored how Intelligent Industrial Documentation lays the foundation by extracting, organizing, and governing tag data from industrial diagrams.
This blog builds directly on that foundation and focuses on the next layer of value a Copilot Bot for DiagramIQ that allows users to converse with their tag and related data instead of hunting through registers, tables, and diagrams.
The goal is simple: make tag intelligence accessible, contextual, and fast using natural language.
From Digitized Diagrams to Conversational Insights
Who Is This For?
The DiagramIQ Copilot is designed for teams that work with complex engineering documentation and large-scale tag registers, particularly in asset-intensive industries such as:
- Oil & Gas (upstream, midstream, downstream)
- Energy & Utilities
- Manufacturing & Process Industries
- Chemicals and Petrochemicals
Typical users include:
- Engineering, Operations & Maintenance teams looking to validate, explore, and understand tag data without manual lookups
For all of them, the Copilot acts as a simple, conversational entry point into otherwise complex industrial data.
Once tags are extracted from engineering drawings and organized into a structured tag register, the next challenge is usability. Engineers and operations teams don’t want to navigate complex UIs or write queries — they want answers.
The DiagramIQ Copilot Bot, built using Microsoft Copilot Studio, acts as a conversational layer on top of the tag ecosystem in Acuvate’s Diagram IQ. It understands user intent, talks to backend data systems, and returns precise, relevant insights in real time.
Deep Tag-Level Insights — Just Ask
The Copilot can deliver detailed insights for any individual tag, including:
- Complete tag metadata (name, type, description, drawing reference)
- Engineering attributes extracted from diagrams
- Relationships to other tags captured in the system
- Data completeness
Instead of manually navigating the tag register, users can simply ask:
“Show me details for tag FV-1023”
“Does this tag have a missing description?”
The response is contextual, precise, and grounded in the actual engineering data — not a generic AI guess.
Flexible Tag Retrieval with Real-World Queries
Beyond single-tag lookups, the Copilot supports filtered and contextual tag discovery, such as:
- All tags associated with a specific drawing file
- Tags with missing or incomplete descriptions
- Recently added or recently modified tags
- Tags filtered by type, discipline, or attribute
For example:
“List all instrument tags from P&ID_Unit03.dwg”
“Show tags added in the last two weeks”
This allows teams to quickly identify gaps, validate documentation quality, and focus on what needs attention — without exporting spreadsheets or writing SQL.
Know More: Modernizing P&IDs with AI
How the Copilot Talks to Your Data
At the core of the solution are two complementary data layers, each serving a distinct purpose.
Tag Registry Database (System of Record)
The tag registry database stores the authoritative, structured representation of all tags extracted from engineering documents. This typically resides in enterprise-grade platforms such as Microsoft Fabric or sometimes Snowflake.
This register contains:
- Tag identifiers and classifications
- All extracted attributes and metadata
- Source drawing references
- Approx 30 attributes per tag-id entry
Purpose of the tag registry:
- Acts as the single source of truth for tags
- Enables fast filtering, reporting, and governance
- Supports compliance, audit, and data quality checks
The Copilot queries this database whenever users ask for lists, filters, counts, or specific tag attributes.
Interface with Tag Registry (Snowflake / The tag registry is accessed using structured, deterministic query interfaces, not free-form AI reasoning.
Typical interfaces include:
- SQL endpoints (Snowflake SQL, Fabric Warehouse / Lakehouse SQL)
- REST APIs exposed through data services
- Secured service principals with read-only or governed access
What the Copilot does technically:
- Converts user intent into parameterized queries
- Executes queries for lists, filters, counts, and attributes
- Receives tabular results
Example internal translation:
User: “Show tags with missing descriptions from this drawing”
→ SQL-style filter on drawing_id and description IS NULL
This ensures:
- Predictable performance
- Auditable results
- No hallucination at the data retrieval layer
Knowledge Graph Database (Context & Relationships)
While the tag registry captures facts, the knowledge graph captures relationships. Implemented using graph technologies such as Neo4j, this layer models how tags relate to each other within drawings.
The knowledge graph represents:
- Logical and semantic relationships between tags
- Navigable paths from one tag to another
Purpose of the knowledge graph:
- Enables contextual exploration beyond flat tables
- Allows the Copilot to traverse relationships dynamically
- Supports questions like “what is connected to this tag?”
Using the graph, the Copilot can help users navigate from one tag to related tags without losing context.
Interface with Knowledge Graph (Neo4j)
The knowledge graph is accessed using graph-native query patterns, typically via:
- Cypher queries (Neo4j)
What the Copilot does technically:
- Identifies the starting node (tag)
- Retrieves only relevant connected nodes and relationships
Example internal intent:
“Show related tags”
→ Cypher query limited to one hop depth related nodes.
This prevents uncontrolled graph exploration while still enabling meaningful navigation.
Know More: Transforming P&IDs from Paper to Smart
Conversational Navigation Across Connected Tags
One of the most powerful capabilities of the Copilot is guided navigation using the knowledge graph.
Starting from a single tag, users can progressively explore:
- Directly connected tags
- Associated tags discovered through graph traversal
This creates a natural, conversational exploration experience:
“Show related tags of FV-1103”
The complexity of the graph remains hidden — users simply follow the conversation.
Context-Aware Conversations with Follow-Ups
The Copilot maintains conversation context so users don’t have to repeat themselves. It can be configured to support up to three levels of follow-up questions, allowing deeper exploration without breaking the flow.
For example:
- “Show tags with missing descriptions”
- “Only from this drawing”
- “Which ones were added recently?”
Each step builds on the previous one, creating a guided analytical experience — not a series of disconnected queries.
Multilingual, Multi-Channel — Built for Real Users
The Copilot is designed for enterprise adoption:
- Multilingual: responds in the user’s preferred language
- Multi-channel: can be deployed on Microsoft Teams, Web applications, WhatsApp, or other supported channels
This ensures consistent access to tag intelligence whether users are in an office environment, a collaboration tool, or a custom application.
Getting Reliable Answers: Prompting, Grounding, and Hallucination Control
A key design goal of the DiagramIQ Copilot is answer correctness, especially in engineering contexts where assumptions are risky.
Prompt Strategy
The Copilot uses structured prompts, not open-ended instructions. Prompts are designed to:
- Instruct the model to answer only from retrieved data
- Separate reasoning from response formatting
- Explicitly state when no data is available
Instead of:
“Answer the user’s question”
The prompt pattern is closer to:
“Using only the retrieved tag registry and graph results, respond to the user. If data is missing, say so explicitly.”
High-Level Architecture Overview
Below is a simplified view of how the Copilot fits into the Acuvate’s DiagramIQ ecosyste
This architecture cleanly separates:
- Conversational intelligence (Copilot)
- Structured tag storage (Tag Registry)
- Relationship modelling (Knowledge Graph)
Key architectural principle:
The Copilot reasons over data returned by systems of record — it does not invent answers. This helps prevent hallucinations.
Built on Microsoft Copilot Studio
By leveraging Microsoft Copilot Studio, the solution benefits from:
- Enterprise security and governance
- Seamless integration with Microsoft data platforms
- Scalable conversation orchestration
- Configurable business logic and prompts
This ensures the Copilot is production-ready, extensible, and aligned with enterprise IT standards.
What’s Next in the Series
This blog is part of Acuvate’s ongoing Intelligent Industrial Documentation series, where we progressively explore how engineering documents evolve into enterprise-grade data and AI assets.
If you’re interested in going deeper, we recommend reading the other blogs in this series to see how the full picture comes together from diagram digitization, to structured tag registries, to knowledge graphs, and now conversational access through Copilot.
Together, these capabilities show how Acuvate’s DiagramIQ helps organizations move from static diagrams to living, conversational industrial intelligence.
DiagramIQ Copilot - FAQs
The ACPID Copilot (Acuvate’s DiagramIQ Copilot) is a conversational tool that allows users to talk to their industrial tag data. It replaces manual lookups in tables with a natural language interface built on Microsoft Copilot Studio.
P&ID Copilot Bots make complex documentation accessible. They allow teams to quickly find detailed tag metadata and engineering attributes across thousands of diagrams without needing to write complex SQL queries or navigate complicated UIs.
Yes, the Copilot for P&ID acts as an intelligent layer over two data systems: a Tag Registry (for structured facts) and a Knowledge Graph (for relationships), ensuring users get precise, contextual answers about their industrial assets.
The Acuvate P&ID Co-Pilot is an AI assistant that delivers detailed tag insights. It uses “grounded” data from your System of Record to answer questions about engineering attributes and metadata without the risk of AI hallucinations.
Conversational AI for P&ID allows engineers to explore documentation using natural language. It can retrieve lists of tags from specific drawings or identify those with missing data, making industrial intelligence fast and contextual for maintenance teams.
