Semantic Digital Twins
Empowering organizations and partners to build semantic digital twins that create context-aware ecosystems, predict impact, and automate reasoning
Fragmented infrastructure and the expert bottleneck
Modern organizations manage thousands of interdependent assets, yet their digital twin data remains trapped in vendor-specific silos (CMDBs, repositories, tools, wikis). This creates a massive visibility gap: you can see that a component exists, but you can’t see the hidden threads connecting it to your customers and services.
This leads to:
- Complex integration: Merging proprietary vendor formats is time-consuming and expert-dependent
- Brittle, hard-coded logic: Traditional digital twin models lack inherent intelligence, they rely on built-in code chains that cannot automatically infer failure modes
- The expert bottleneck: Technical staff and business leaders must rely on a handful of overworked experts to write complex queries and get basic answers
- Blind impact analysis: Manually tracing the dynamic dependencies of parts of a system and how they will be affected by a change or detected failure, is manual, slow and prone to errors
From observation to understanding
While a traditional digital twin creates a virtual replica that mirrors the data and behavior of a physical system, a Semantic Digital Twin “understands” it within a broader context. By replacing rigid tables with a dynamic Knowledge Graph, you move from a static replica to an intelligent, context-aware ecosystem:
- Contextual intelligence & reasoning: Ontologies enable the system to reason close to the source without brittle, hard-coded logic
- 360° real-time visibility: The single, unified view of your infrastructure enables to trace the effects of any change or failure in milliseconds
- Agility & adaptability: The flexible model evolves over time by incorporating new technologies (e.g. DTDL, CIM, AAS) without costly re-engineering
- Democratized insights: Anyone can query the Digital Twin by using natural language and get immediate answers
Our Strategic Moat
The Graphwise advantage
We provide the semantic engine that turns fragmented infrastructure data into a resilient, machine-interpretable source of truth.
Graph power
Enrich existing CMDBs and tables with a flexible graph, modeling complex, many-to-many relationships that tables can’t capture
Explore one use case in practice
Justify your Investment
Stop reacting to failures and start predicting them with measurable performance gains:
Faster impact analysis:
Turn a multi-hour manual “look-up” task into a graph query that returns results in milliseconds
Less false positives:
A semantic digital twin understands context: it knows a “Low Pressure” alert is caused by an HVAC failure, not a machine break, keeping your assembly line running
Confident decommissioning:
Retire legacy systems with 100% visibility into what will be affected, eliminating the “fear of the unknown”
Success Stories
See what Our Customers and Partners do with Graphwise
customer success story
Statnett – Talk to Your Power System
Problem
As the green transition accelerates, Statnett needed to perform rapid power system analyses. However, their data was locked behind complex CIM standards (IEC 61970/61968), requiring highly specialized knowledge and hours of manual extraction just to answer basic questions.
Solution
Graphwise developed a “Talk to your Power System”, a semantic interface allowing engineers to query the grid’s digital twin using natural language instead of complex code.
Results
They achieved a total transformation of their analytical workflow. Complex grid queries that once took hours of expert labor are now resolved in seconds, allowing engineers to focus on grid resilience rather than data hunting.
Problem
Tietoevry’s enterprise clients struggled with Gen AI models that produced fluent but unreliable text because they lacked the deep domain context needed to interpret complex IoT sensor data and specialized culinary constraints.
Solution
Tietoevry integrated Graphwise’s GraphRAG and Knowledge Graph technology to build a “trusted semantic backbone” for their clients, ensuring AI outputs are grounded in industry-specific facts and logic.
Results
By grounding AI in structured knowledge, Tietoevry enabled its clients to automate high-stakes decisions in maintenance and nutrition with total confidence and domain-aware accuracy.
Problem
Two leading Building Automation Systems (BAS) manufacturers struggled with fragmented, siloed data models for HVAC, lighting, and security that required manual, redundant updates.
Solution
The manufacturers integrated Graphwise’s GraphDB with the Brick metadata schema, creating a unified semantic digital twin that centralizes all building assets into a single, standardized graph model.
Results
By replacing isolated silos with a “semantic backbone,” the manufacturers slashed implementation time for integrators and drastically reduced maintenance costs through centralized, enterprise-wide asset visibility.
on-demand webinar
Talk2PowerSystem: Democratizing Power System Analytics via Generative AI
Complex grid data is usually locked behind specialized analysts. Statnett and Graphwise present a natural language chatbot that lets anyone query intricate energy data with plain-language questions.
Explore Our Success Stories
Expanding Editorial Output for the FIFA World Cup, Delivering 800+ Pages in Weeks
The BBC used semantic technology to To power its 2010 FIFA World Cup website to cut editorial costs and improve user experience.
Read more
Scaling Search and Content Governance with Semantic Technology
Microsoft Docs transformed millions of technical documents into a semantically enriched, scalable knowledge system.
Read more
Transforming AI Reliability by Building Knowledge Graph-Powered Customer Support
Avalara used GraphRAG to establish a foundation for reliable, mission-critical AI applications in tax and financial services.
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Building a Collaborative Knowledge Hub for AI-Powered Predictive Intelligence
Sensing Clues implemented Graphwise’s Knowledge Management Suite to build vast organizational knowledge models.
Read moreReady to build a Semantic Digital Twin of your infrastructure?
Create a dynamic, intelligent view of your assets. Analyze dependencies, reduce risk, and make confident decisions with a system that understands your business as well as you do.
