Meet the technology for the intelligent enterprise
Powered by human intuition. Augmented by AI.
What is Hybrid Intelligence®
Hybrid Intelligence® combines the complementary strengths of human intuition and AI in solving complex tasks to continuously improve by learning from each other(Dellermann et al. 2019)
Accelerate performance in solving complex tasks by combining human intuition and AI
Take superhuman decisions beyond your individual knowledge and bias
Continuously learn from each other in a connected cycle of improvement
How our Hybrid Intelligence® engine works
Actively collect knowledge from business experts or customers and ask analysts for insights to drive continuous experimentation and learning.
Predict the impact of every decision and model the mechanics of your KPIs to clearly understand critical factors driving risks, opportunities and value.
Simulate the impact of any decision without a very time-consuming, expensive, and even dangerous trial and error in the real world.
Optimize every decision based on simulated outcomes and real-time feedback to make sure that decisions have the intended impact every time.
Under the hood
Use all available data and understand it to sense weak signals of change and optimize every decision.
Make better, more precise, and continuously improving strategic decisions with advanced analytics and explainable AI.
Understand the mechanics of your business and continuously sense weak signals with embedded AI.
Identify the most valuable insights for businesses by integrating data from many sources inside and outside the organization.
Enterprise-grade data security and AI safety
To maximize the business impact of AI in your organization, our team is working on the latest technologies for ensuring privacy and security, such as:
Federated learning is a type of remote execution wherein our models are sent to your enterprise IT structure for local training. This eliminates the need to store your sensitive training data on our servers and allows us to provide you with high-performing models. Moreover, shared learning can be leveraged across customers to accelerate the performance of our models while keeping personalization.
Encrypted computation provides us with the capability to keep our computations secret even if they run on client infrastructure. All models stay encrypted to allow serving multiple clients without the need to open up details on the model or the ability to steal it.
Differential privacy is a technique that highly improves the anonymization of data and makes sure that even if data was accessible, the private information that I may leak remains minimal
At vencortex, we are committed to the highest level of transparency, trust, and regulatory compliance. We dedicate special attention to information security and corporate governance at every level of the organization to keep your data safe at any time. Therefore, we are working on latest technologies such as:
- Homomorphic encryption
- Multi-party computation
- Federated learning
- Client side prediction
- Automatic differential privacy