We've harnessed the capabilities of artificial intelligence algorithms to transform how technology assets are assessed. But that's not all – we've taken it a step further with a peer review process that ensures the validation of the initial AI-based evaluation.
The system works for the assessment of knowledge-intensive assets. It provides a framework for the evaluation of assets such as scientific research or deep tech innovation and can be used to identify the most promising and impactful assets for further decision making.
Using cutting-edge AI algorithms to meticulously analyze technology assets and provide insightful assessments. By leveraging the power of artificial intelligence, we deliver accurate and comprehensive evaluations that go beyond traditional methods.
To increase the quality of the generative AI-evaluation we introduce a second layer of assessment, which is basically an open assessment of experts reviews.
The result of the review process a continuous log of structured data points is formalized by an algorithm in a metric reflecting the quality of an asset. This simplifies investment decisions and provides an assessment of the asset intrinsic value.
Assessment of Technology Assets broadens perspectives on quality innovation, while also focusing on the levers that lead to quality outcomes. This matters because research, development, and innovation are increasingly diverse in terms of contribution. The Technology Assessment system can handle this diversity and quantity for better quality assurance.
The model is designed to enable the traceability of quality and create an incentive for assessors to contribute fairly. Expertise is a quantitative grade of reputation and track record of the participant in a particular domain in the system. The more Expertise a reviewer possesses, the more influence on assessment the review has.
Since the assessment protocol is blockchain-based, it is transparent and eliminates middlemen who otherwise would be needed to govern and validate the proper execution of the rules. Moreover, it provides high reliability and interoperability of the system. Therefore all the results of the assessment can be verified with the cryptographical proof of each contribution.