An AI Cloud platform to rapidly scale the delivery of superhuman performing enterprise grade AI and ML solutions: Kortical

An AI Cloud platform to rapidly scale the delivery of superhuman performing enterprise grade AI and ML solutions: Kortical

In the realm of machine learning (ML) and artificial intelligence (AI), envision the potential of experimenting with 50,000 different ML iterations in just one week. This ambitious capability is not a mere concept but a reality brought to life by Kortical—an AI Cloud platform designed to accelerate the delivery of enterprise-grade AI and ML solutions with both user interface (UI) and code interfaces. Kortical stands as a revolutionary platform, empowering data scientists and coders to swiftly deliver superhuman performing AI and ML solutions. Its uniqueness lies in its ability to cater to those who value delivering significant business value, despise repetitive tasks, and recognize the transformative nature of AI in the business landscape.

Kortical's philosophy revolves around making everything user-friendly while providing the flexibility to tweak every detail. The UI is complemented by an easy-to-use SDK, allowing users to perform advanced tasks such as building and deploying high-performing models with just seven lines of code. Kortical's mission is to democratize the world of AI experimentation and deployment. By offering a platform that combines the power of Superhuman AI and Instant Apps, Kortical not only accelerates the ML development lifecycle but also makes the process accessible and efficient for developers seeking to push the boundaries of what AI can achieve.

Key Features:

1. Superhuman AI:

• Empowerment: Puts users in the driver's seat with detailed solution information, code, and control.
• Cloud Scale: Leverages cloud-scale and assistive technology to exponentially increase iteration speed, allowing for over 50,000 model iterations in a single week.
• Flexibility: Supports users from no-code model building to full code-driven SDK, offering versatility in model development.

2. Instant Apps:

• Dynamic Templates: Offers code-based dynamic templates for various ML applications, easily adaptable to different use cases.
• Rapid Deployment: Enables users to get a production-ready app live in as little as 30 minutes, significantly reducing the time from raw data to a live Machine Learning app.
• Open Source Basis: Built on open source principles, ensuring no vendor lock-in and complete solution transparency.
• Lifecycle Management: Includes model lifetime management, retraining, and more.

3. Success Stories:

• Achieving Excellence: The fusion of Superhuman AI and Instant Apps has enabled customers to deliver ML enterprise apps that outperform human capabilities in less than two weeks.

Optional Services:

Kortical offers optional services for companies lacking the expertise to strategize or implement AI/ML solutions independently:

• Exploratory Data Analysis: Dive into data insights and patterns.
• Assisted Rapid Custom Data Cleaning & Feature Engineering: Streamline data preparation.
• Advanced Model Explainability: Understand and interpret complex models.
• One-Click Deployment & Infrastructure: Simplify the deployment process via UI or API.
• ML App / Service Creation: Build, deploy, and manage ML apps swiftly.
• Self-Learning AI: Adapt to consumer and market behavior effortlessly.
• Key Strategies for Change Management:

Superhuman AI Automation

Put simply Superhuman AI Automation is when an AI performs a task at human level or above. This is critical because most businesses don’t want to adopt technology that is tangibly worse, they’ve optimized what they need to pay to get the job done well and doing it worse usually just isn’t an option. Automation projects that don’t meet near human level or above, don’t go anywhere. This is a large part of why 85% of AI projects are considered failures but once this superhuman boundary is achieved typically 70% - 95% of a business function can be automated. The Superhuman AI Automation approach allows us to take machine learning models that are not superhuman and use “Superhuman Calibration” to find the subset of tasks on which the model can perform at superhuman levels. Then wrap this with the tooling and processes for experienced domain experts to keep improving the AI over time and help it adapt to new data. As you can imagine the ROI for automating 70% - 95% of tasks while keeping or improving task performance tends to be pretty spectacular. How can this be done?

• Up-skilling workers to become AI trainers:With Kortical’s approach to Superhuman AI Automation there are still humans in the loop to continue to train and continuously improve the AI. These people essentially become AI trainers. By teaching them how to provide better examples for AI we can improve the data quality, which improves the automation performance and perhaps incentivise them based on the savings. This will give them valuable new skills, a new job title, prepare them for an AI enabled world and get them excited about the opportunity.
• Up-skilling to more high value work:The jobs that lend themselves to rapid automation tend to have aspects that are highly repetitive. Often the tasks automated are not the only responsibility of the workers that do them and there is more high value work they can pick up and having workers that are already familiar with the company can reduce training time over entirely new recruits.
• Advance Warning:It’s human nature to be wary of change, giving people as much advance notice as possible of what’s coming, what the new way of working will look like, plans for retraining, etc. will give people a chance to prepare mentally for the change and make them more open to it. As well as giving them time to prepare if they would prefer to leave.
• Attrition:Jobs with highly repetitive work which are perfectly suited to automation tend to have high employee attrition, slowing down the replacement rates as you get ready for roll out, ahead of it actually going live, can mean that there are fewer jobs to cut and that given the workload, the workers are glad of the support of the automation. Rather than fearing the sudden drop in workload once it goes live and what that might mean for them. Another benefit of AI automation is that once the majority of the routine and mundane work is done automatically, the work left is more engaging and leads to increased employee happiness and better retention.

 Andy Gray, CEO and CTO

“Kortical supports you, from no code model building, to individually tweaking the layer sizes of your deep neural nets or adjusting the moment parameters on your favourite solver, to full code driven SDK”