Enterprise Ai Solution
Accelerate Integration of Ai Into Mission-Critical Processes
CTO and Head of Product
We used Zeblok in both a consulting and execution capacity for a financial services project that we took from conception through to the Proof of Concept stage and prototyping. Zeblok were able to provide knowledgeable resources in all the key areas required, facilitating the provision of both a solution and an environment within which development, testing and finally production could be executed. We found Zeblok to be responsive, agile and able to engage with and understand our business and technology objectives. It is our intention to continue building out our relationship with Zeblok and using their consultation services and execution environments. I would happily recommend Zeblok as a partner in the provision of Ai products and services.
Enterprise Ai Opportunities and Challenges
The next generation of digital assets for enterprises are core Ai capabilities that will make them an Ai-fueled organization. Going beyond a handful of Ai applications will need a digital transformation for enterprises. To deliver Ai capabilities into their products, services, and workflows, including to their partners, enterprises must consider Ai applications very much like content.
Enterprises seek a comprehensive Ai platform to integrate pragmatic Ai into mission-critical enterprise processes more rapidly and cost-effectively, without cloud vendor lock-in, to lower the cost per insight. Zeblok's Ai-MicroCloud™ provides a turnkey, cloud-native environment, deployed to their existing infrastructure, supporting a hybrid cloud strategy, enabling them to easily create their own Ai ecosystem, Ai assets, and Ai-AppStore.
But to develop this digital transformation Ai ecosystem there are challenges to be solved in three broad categories:
Data comprehension – According to Gartner, 85% of Ai projects fail. We believe there is a data comprehension gap. Additionally, Ai modeling environment needs to handle High-Performance Computing natively to deal with very large datasets needed for model training and optimization.
Lowering the cost per Insight – Enterprises must confront the complexity of integrating multiple independent Ai software vendors (ISVs) and internally developed Ai capabilities. There is a price-to-performance gap and a digital asset curation gap (think AppStore). Additionally, there is latent demand for profiling existing Ai inference engines and optimizing existing Ai models to lower the cost of ownership.
User experience – Enterprises have prevalent systems and machinery for continuous integration and delivery for applications. Perhaps they have made internal investments in MLOps or have procured a MLOps platform. Software developers prefer to use existing development studio environments and prefer to integrate via SDK. API-driven approach to integrating with any existing analytics platform and delivering Ai capabilities as a service is a key requirement.
Delivering Enterprise Ai Digital Foundation
As businesses in every industry embrace innovation in AI and adapt to a changing technological landscape at the Edge, Zeblok's Ai-MicroCloud™ provides the digital foundation that helps meet new demands and prepare for the future.
Zeblok's enterprise Ai solution brings together all the core components & technologies to provide enterprises End-to-End life cycle management to create and manage next-generation Ai digital assets.
From designing flexible architectures that support different topologies and enabling hybrid-clouds inclusive of Edge data centers, to providing an Ai-Platform-as-a-Service that enables companies to securely operate, monitor, manage and support enterprise Ai applications, Zeblok's Ai-MicroCloud™ enables enterprises to adopt Digital Transformation 3.0 with ease.
Additional Benefits for Edge Ai Integration
In the future, 70% of the world’s data is going to be created and acted upon outside of traditional data centers, i.e., Edge data centers. This is a paradigm shift requiring substantial investment in autonomous operations at Edge data centers to match the expectations from decades of investments in traditional data center automation. Furthermore, it requires efficient packaging and software distribution on disparate servers from numerous hardware manufacturers at Edge data centers.
Application developers need integrated workflows that allow them to automate Ai inference deployments at scale and tools to move data from the Edge to further refine insights.