KanzoTech focuses on applying ethical and sustainability principles to data flow through Semantic Technologies.
Like other companies, we help you generate insights based on your data to improve your business decisions. Unlike them, we do it by giving you access to technologies that were only available to Big Tech, always seeking to treat the data in an ethical and sustainable manner.
What are semantic technologies?
Semanantic technologies are a class of data management technologies that focus on making the underlying meaning of data more transparent to both human and machine users
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Over the past five years, the increasing demand for AI-based solutions has greatly boosted the importance of semantic technologies, especially Knowledge Graphs.
Their ability to organize knowledge from various sources has made them very important for both the scientific community and businesses.
Specifically, Knowledge Graphs can be used for various information processing and management tasks such as:
Enhance data integration
Improve data Quality
Beter interoperability
Elevated data Governance
Advanced Analytics
Effective Scalability
Augmented Cost-Efficiency
Improve and control Generative AI
KANZO empowers organizations to harness the power of Knowledge Graphs to unlock deeper insights and foster innovation in Semantic Technologies.
We focus on these three areas every day to make a positive impact.
Semantic Technologies engineering
Data Spaces
Research & Innovation activities
PÁGINA WHAT WE DO
We're here to help you make the most of semantic technologies!
Our motivation is to facilitate the use of semantic technologies to improve domain knowledge and achieve more ethical and sustainable data lifecycle.
Organizations must find, access, and understand data from various sources, which is complex and costly. Machines need extensive knowledge to better process information.
Semantic technologies enable machines to understand data meaning and context, leading to improved searches, data organization, automation, and sentiment analysis.
KANZO aligns various data sources, simplifying management and access to information across the organization, facilitating the transformation of complex data into clear domain concepts.
We are specializing in:
Utilizing semantic web standards to enable interoperability and scalability in data architectures.
Developing new ontologies and vocabularies according to specific domains.
Knowledge Graphs creation, implementing population and query mechanisms.
Enhancing General AI using semantic RAG.
According with the European Commission, a Data Space ‘brings together relevant data infrastructures and governance frameworks in order to facilitate data pooling and sharing’.
This innovative concept has the power to revolutionize data portal models, turning them into decentralized spaces where decision-making evolves into data sharing. At Kanzo, we are
dedicated to overcoming the challenges associated with this transformation at both the technical and governance levels. In pursuit of this goal, we are active members of crucial organizations in the sector, such as IDSA and Gaia-X.
Our technical contribution to Data Spaces is integral to this visión:
Self Sovereign & standards-based Identity Building Block (VC/DID/Solid)
Multi-tenant & interoperability Data Space Connectors (FIWARE, ECLIPSE, IDSA)
Vocabularies & Policies creation and enrichment through semantic technologies
AI needs data, and AI can make data more useful.
To truly benefit from AI, it’s important to go beyond just using a single model or chat agent. Improving performance and streamlining the execution of AI processes is a key concern.
At Kanzo, we specialize in various areas of solware engineering to enhance the performance, efficiency, and implementation of AI models. This helps your organization become more agile and reduces its environmental impact.
Kanzo can help you to:
Management of high concurrency peaks in data architectures
Optimize AI Model performance, explainability and transparency.
Low latency processing for heterogeneous Edge-IoT environments
NLP and multi-agent approaches for domain-specific conversational assistants.