Turning organisational knowledge into actionable AI
By allowing users to show a problem rather than describe it, visual artificial intelligence (AI) can capture device details, fault conditions and environmental context that are often difficult to communicate in words. For example, a user can scan a device, nameplate or machine display panel with a smartphone camera. The system can then extract relevant information, interpret the context and provide guidance where needed. The founders of Blinkin(opens in new window) in Germany recognised that combining this visual understanding with organisational knowledge and AI assistance could significantly improve how support is delivered and scaled beyond office use cases. “Expertise is too often constrained by organisational silos, documentation complexity and dependence on a limited number of specialists,” says Josef Suess from Blinkin. “Our vision was to help organisations transform that knowledge into a continuously available operational asset that can support workers, technicians and customers wherever decisions need to be made.” To achieve this, the company set out to create a deep-tech multimodal intelligence platform that transforms fragmented knowledge into actionable guidance accessible through smartphones and other frontline interfaces. “We wanted to make organisational knowledge accessible and applicable in real-world workflows, not just available as static documentation or a generic chatbot,” adds Suess.
Capturing visual and contextual signals
With the support of the European Innovation Council(opens in new window), the BlinkIn Visual Assistant(opens in new window) project analysed support scenarios across industrial, retail, customer-service and public-sector environments where unclear descriptions, missing context and fragmented information create bottlenecks. The resulting platform captures visual and contextual signals, connects them with organisational knowledge and turns them into guided workflows validated under real deployment conditions. This validation exposed the technology to practical constraints such as usability, data quality, integration, security and scalability.
Lowering the AI deployment barrier
The EU-funded project enabled Blinkin to evolve from visual-assistance technology into an enterprise-ready system capable of turning organisational knowledge into guided operational workflows. A key challenge was that, despite rapid advances in AI, most organisations still cannot easily build, deploy and continuously improve customised and governed multimodal AI applications for their own operations. Doing so typically requires scarce engineering capacity, AI expertise, integration work and ongoing maintenance. The technology progressed beyond technical feasibility into production-oriented validation. Key developments include a no-code environment for creating and deploying AI workflows, capable of handling unstructured text, image, audio and video input as needed for each use case. “Support, service and troubleshooting processes can be executed, measured and improved in a controlled way,” notes Suess.
Learning loops for organisational knowledge
A particularly important outcome was the creation of enterprise knowledge learning loops. “Every interaction can contribute to improving workflows, refining knowledge assets and strengthening future decision support,” explains Suess. “This allows organisations to move towards continuously evolving operational intelligence systems.” The Blinkin team believes these advances can contribute to a future in which organisational knowledge becomes more scalable, accessible and useful. The hope is that the platform will help organisations improve productivity, preserve critical expertise and solve problems more effectively. “More broadly, we hope this project demonstrates how European deep-tech innovation can deliver governed, trustworthy and human-centred AI systems that create measurable value in real-world environments,” says Suess. Next steps include scaling adoption and extending the platform across additional industries and operational domains. “Commercially, the priority is to move from successful deployments towards broader market adoption,” adds Suess.
