Empowering organizations and partners to build an intelligent, context-aware ecosystem that predicts impact and automates reasoning
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:
| Focus | Traditional Twin | Semantic Digital Twin |
|---|---|---|
| Data model |
Rigid schemas & data models |
Flexible Knowledge Graph |
| Visibility |
Fragmented |
360° real-time connectivity |
| Dependencies |
Manual tracing |
Faster impact analysis |
| Accessibility |
Expert-dependent |
Democratized access to insights |
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:
Our Strategic Moat
We provide the semantic engine that turns fragmented infrastructure data into a resilient, machine-interpretable source of truth.
Enrich existing CMDBs and tables with a flexible graph, modeling complex, many-to-many relationships that tables can’t capture
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 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
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.
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.
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.
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.
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.
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.
Two leading Building Automation Systems (BAS) manufacturers struggled with fragmented, siloed data models for HVAC, lighting, and security that required manual, redundant updates.
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.
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.
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.
