Research and Development
The visual command center for Agentic AI and sovereign innovation.
Innovation at the turning point to operational excellence
By 2026, the R&D sector has left the phase of mere experimentation behind. The focus has shifted from simple digitalization to a profound digital identity, in which intelligence and automation are integral components of the organizational DNA.
DEON provides the decisive platform that combines scientific creativity with unprecedented technological prowess.
From Workspace to universal AI Hub
Even without AI integration, DEON solves the massive problems of information fragmentation. Through the infinite Workspace and the Metabrowser Engine, data silos are broken up and complex research workflows are visualized.
With the new AI extensions, DEON becomes a universal AI hub: Agentic AI now autonomously handles multi-stage tasks, while the Graph Chat ensures that AI-driven hypothesis formation remains transparent and controllable for human experts.
DEON enables R&D leaders to implement sovereign AI strategies that protect valuable intellectual property and drastically shorten time-to-market.
Application areas of DEON in research and development
From laboratory to market maturity: DEON across the entire value chain
The application possibilities of DEON in the R&D sector are as diverse as the disciplines themselves.
In the biopharma industry, DEON acts as a digital twin of the research pipeline, where molecular simulations, clinical trial data, and regulatory requirements converge on a single canvas. Here, AI-supported analysis enables a shortening of the early R&D phases from years to just a few weeks.
In the high-tech and semiconductor industryDEON supports the development of complex chip architectures through visual AI inspections and protects national and corporate data sovereignty through sovereign cloud connections.
Materials research, plant construction and beyond
Another key area is materials research and battery technology. Here, DEON orchestrates the interaction between electrochemical modeling and physical laboratory data in real-time. The Metabrowser Engine allows researchers to place specialized simulation tools directly next to live dashboards from production, eliminating cognitively burdensome context switching.
In machinery and plant construction DEON becomes the bridge between the shop floor and the development department. AI agents prepare automated briefings that translate operational performance data directly into strategic roadmaps.
Whether in the automation of approval processes or collaborative work on digital twins - DEON transforms rigid data graveyards into living knowledge graphs. Cognitive ergonomics always remain in focus: an AI without visual structure is like a brilliant professor without a blackboard - the brilliance is there, but no one can follow it.
Use Cases
Accelerated Active Ingredient Discovery in Biopharma
Challenge
The Cognitive Wall of Data Silos
In modern active ingredient research, teams face a huge challenge: synthesizing information from thousands of heterogeneous sources. Clinical study reports, molecular databases, and regulatory guidelines are often found in isolated systems.
Researchers spend up to 40 percent of their time on manual searching and copying data, instead of focusing on scientific analysis. This so-called Context Wall prevents important signals from being recognized in the flood of data.
In addition, there is enormous time pressure due to expiring patents, which forces a drastic acceleration of the discovery phase without loss of quality.
Anyone trying to work with linear chat systems or Excel lists here loses track faster than an AI can hallucinate.
Solution
The Unified Research Workspace with Agentic AI
DEON solves this problem through a holistic, visual approach. The Metabrowser Engine aggregates all relevant web applications and databases on an infinite canvas. The AI assistant DAIA uses this visual grounding to precisely answer complex research questions.
A single click on a reference button immediately zooms the researcher to the exact location in the project – be it a specific paragraph in a PDF specification or a cell in an embedded table.
- Automated Drafting: AI agents take over the creation of Clinical Study Reports (CSR) by generating texts that are directly linked to the source data on the board.
- Graph Chat for Hypotheses: Research ideas are visualized not in a sidebar, but as interactive knowledge graphs. Teams can open up alternative research branches without losing the main context.
- Multimodal Synthesis: Different LLMs work simultaneously on the same data to provide different scientific perspectives.
This integrated process reduces documentation cycles by up to 90 percent and massively shortens the time to Proof-of-Concept, while maintaining the transparency of a glass box.
Sovereign AI and IP protection in the high-tech sector
Challenge
Geopolitical risks and the danger of knowledge drain
For companies in the semiconductor or defense industry, protecting intellectual property (IP) is essential. The use of public cloud AI models carries the risk that sensitive development data will be incorporated into the training of global models or compromised by foreign laws such as the US Cloud Act.
At the same time, the fear of this knowledge drain often leads to technological paralysis - the so-called pilot-itis. Innovations remain in isolated test environments, while the global competition pulls ahead with AI support.
The challenge is to use the most modern Agentic AI without losing control over data sovereignty. Security must not act as a brake here, but must serve as a foundation for scaling.
Solution
Sovereign AI Hub for protected innovation
DEON enables the establishment of a fully sovereign AI infrastructure. Through partnerships with European cloud providers like IONOS or on-premise operation, all research data remains within the company's legal jurisdiction.
Engineers use state-of-the-art LLMs via a secured AI Model Hub, ensuring that no byte is used for training external models.
- Institutional Memory: The semantic project query allows knowledge from years of projects to be used as searchable precedents without data ever leaving the company.
- Viewport Analysis: Engineers zoom in on complex technical drawings while AI identifies anomalies or suggests optimizations based on internal standards in real-time.
- Secure Collaboration: Even with remote work, all data streams remain encrypted and within the sovereign infrastructure.
Through this approach, AI becomes a scalable corporate capability rather than a risky experiment. You can't predict the future, but with DEON you can at least visualize it in 8K and keep the keys safe.
Networked Materials Research and the Composable Enterprise
Challenge
Fragmentation in Interdisciplinary Development
The development of sustainable battery technologies requires seamless collaboration between electrochemistry, software simulation, and manufacturing technology. Often this fails due to monolithic IT structures: simulation results are stored in one tool, while real test data is visualized in another system.
Engineers lack a single source of truth to compare physical models with real sensor data. The constant switching between programs results in massive cognitive load and increases the error rate when interpreting complex relationships.
Without a visual framework that holds these LEGO blocks of research together, innovation remains fragmented and slow.
Those who believe that data alone creates innovations also believe that a sack of flour is a finished soufflé.
Solution
Visual Orchestration of Highly Complex Simulations
DEON acts as the operating system for the Composable Enterprise in materials research. It enables the visual merging of different research modules on a single, interactive surface.
Via the Metabrowser Engine, PowerBI live dashboards are embedded directly next to CAD models and simulation windows.
- Real-time Monitoring: An AI agent continuously monitors the embedded data streams and immediately places visual warnings on the board when deviations occur in the loading cycles.
- Active Content Control (MCP): Via the Model Context Protocol, AI agents autonomously organize the workspace: they group test results, color priorities, and structure data sets based on research goals.
- Synchronous Simulation: Teams work simultaneously on digital twins worldwide, with each user able to navigate independently without disrupting the focus of others.
This approach reduces prototype cycles by almost 50 percent. Why spend three hours in a meeting when you can see the answer in 30 seconds on a DEON board?
Knowledge Transfer from Shopfloor to R&D in Plant Construction
Challenge
Information Loss in the Product Lifecycle
In modern plant construction, a dangerous gap often exists between theoretical development and practical experience in production.
Valuable insights into material fatigue or assembly issues are lost because they remain in analog shift logs or isolated ticketing systems. When the R&D department works on the next generation of a machine, it often lacks direct, unfiltered data from real-world operations.
This slow flow of information leads to unnecessary redesigns and missed optimization potentials. The challenge is to integrate the shop floor as a live laboratory into the development process without overloading employees on site with complex software.
An unresolved problem here is maintaining context across shift changes.
Solution
The hybrid knowledge graph for closed innovation cycles
DEON bridges the gap between production and development through seamless visual integration. Production data is mirrored directly into the R&D workspace, creating a continuous feedback loop.
AI agents act as intelligent intermediaries here.
- Automated Briefings:
- Condition-Based Alarming: If an error occurs in production, a photo or video is loaded directly into DEON. The AI recognizes the context, links the image to the corresponding CAD drawing and notifies the responsible developer.
- Persistent Strategy: Strategic annual planning and daily operational tasks exist side by side. Projects are not lost in folder structures after the planning phase, but remain as active workspaces.
This hyper-automation minimizes information loss and measurably increases the speed of innovation.