INTRODUCING Predigle Intelligence (Pi)
The advent of cloud computing has accelerated the reduction in total cost of ownership of computing and storage systems exponentially. This is true for both the cost of systems per computer operation and also, related labor. Contributing factors include hardware advances, cloud computing and the economies of scale. Emerging companies with little access to capital can now invest in complex computing solutions.
AI has become faster and more effective. The cost of training a machine learning model with a given computational power has reduced by a factor of 300,000 since 2012. The effectiveness of algorithms, such as the ILSVRC in 2012 showed a significant leap in image classification performance, achieving a top-5 error rate of 15.3%, outperforming traditional CV methods.
Encouraged by its effectiveness and accuracy, AI is being progressively used in transaction processing, not just for decision support, analytics. Examples include autonomous driving, fraud detection and prevention, e-commerce related recommendation systems, trading and financial markets including high frequency trading, virtual assistants and chatbots, and supply chain and inventory management.
Emerging companies that are smaller than Fortune 100 can now exploit advanced technology to innovate and disrupt the marketplace. Lower infrastructure costs, pay-as-you-go pricing, AI platform-as-a-service (PaaS) and Open-Source AI frameworks have all contributed to a lower barrier to entry for smaller entities to access these. Affordable infrastructure, flexible pricing models, and Open-Source AI frameworks have democratized AI access, empowering smaller entities to compete, innovate, and disrupt industries.
Limited access to experienced AI talent hinders emerging companies’ problem-solving capabilities. Resource and skill constraints impede the adoption of AI technology. Overcoming talent challenges through collaboration with AI startups, utilizing cloud services, and active engagement in the AI community.
Infrastructure design complexity: Safety, security, and reliability demand specialized expertise despite reduced costs. Key considerations: Security, privacy(including HIPAA or GDPR compliance), reliability, performance, scalability, data management, model development and maintenance, and ethical AI practices. AI products and platforms: Dedicated AI solutions and skilled talent pool for effective addressing of requirements.
Agility driving adoption: Flexibility, flat management, and higher risk appetite enable faster AI technology adoption. Efficiency through automation: Emerging companies emulate Fortune 50 efficiency by embracing automation. Disruptive leadership: Emerging companies revolutionize customer experience, streamline operations, and optimize various aspects such as marketing, sales, fraud detection, supply chain, customer relationship management, and decision-making.
Instant access to AI-driven healthcare, life sciences, and supply chain solutions through a flexible pay-as-you-go subscription model.
Proprietary AI and data science platforms drive efficient studio-based design and implementation of transactional operations.
Projects are completed within weeks, ensuring quick deployment and time-to-value for clients.
Predigle provides a comprehensive solution, eliminating the need for clients to solve complex challenges related to architecture, technology, reliability, adaptability, efficacy, and efficiency.
Predigle includes implementation services, providing ease of adoption and access to experienced subject matter experts.
Predigle takes on business operations as required, leveraging automation to reduce costs. Additional manual resources are available if needed, ensuring optimal effectiveness and cost efficiency.
