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Rapid AI Use-Case Verification
Our process draws on experience and lessons learned from countless AI PoC projects. It reduces project risk and maximizes the chances of success in a candidate's AI use case. The four quality gates—positive business value, a usable data basis, an AI operating model, and functional verification via a prototypic implementation—ensure that only substantiated candidates proceed to full-scale development.
AI Solutions
To use AI in a scalable manner, you need a proper data platform to make the appropriate data accessible in a secure manner. We can help your organisation to democratise data with data mesh, establish a self-service data platform with a data fabric, and use your data with cognitive services and OpenAI to generate business value with comparatively little effort.
Join our AI Envisioning Workshop and map clear AI strategies that directly convert to your business value.
Unlock the potential of AI for your business with PRODYNA's AI Envisioning Workshop. Our expertly designed session guides key decision-makers through the integration of Microsoft's AI technologies into your products and internal workflows. Experience a unique combination of AI Cards and Design Thinking to not only envision but also map clear AI strategies that convert directly to business value. By participating, you will gain an in-depth understanding of Azure AI's capabilities and how they can revolutionize your processes, making this knowledge an indispensable asset in today's competitive landscape. Join us to foster innovation and position your enterprise at the forefront of the AI revolution.
Data Mesh
Break up the monolithic data platforms and add agility to your data. A data mesh is a decentralized data architecture that organizes data by business domains and distributes data ownership and responsibility to domain-specific teams. It treats data as a product and makes it more accessible and available to data consumers. It helps scale analytics adoption in large, complex organizations. Data mesh helps solve advanced data security challenges through distributed, decentralized ownership. It brings efficiency by improving data access, security, and scalability.
Data Fabric
A data fabric is an architecture that facilitates the end-to-end integration of various data pipelines and cloud environments through intelligent and automated systems. It aims to create more fluidity across data environments, attempting to counteract the problem of data gravity—i.e., the idea that data becomes more difficult to move as it grows. A data fabric abstracts away the technological complexities engaged for data movement, transformation, and integration, making all data available across the enterprise, and is an ideal basis for building a self-serve data platform.
Data Science
Data science is becoming increasingly important due to the value of data. Data science uses statistics, learning processes, and algorithms to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data. It enables enterprises to measure, track, and record performance metrics for facilitating enterprise-wide enhanced decision- making. Data science can improve existing processes and assumptions through new methods and algorithms. It is a critical component of modern-day businesses and organizations, enabling them to gain insights into customer behaviour, identify trends, and improve operational efficiency.
LLMs & OpenAI
Cognitive services are a large and varied set of cloud-based AI-enriched services for vision, speech, and language analysis in addition to automated decision-making capabilities. Many of these can be used as is or trained and tailored to your specific use case. In addition to the standard services, large language models (LLM) are now available as a cloud service. Examples of these are GPT-3.5T and GPT-4 from OpenAI. An LLM is a neural network with many parameters (typically billions of weights), trained on large quantities of unlabeled text using supervised or semi-supervised learning. LLMs utilize deep learning in natural language processing (NLP) and natural language generation (NLG) tasks and are optimised to master the complexity and linkages of language.
AI Infused Applications
The primary reason for building applications is to create business value. The easy integration of insights from your data platform and the extraordinary capabilities of both cognitive services and LLMs enable you to rapidly build applications with comparatively little code that possess an amazing ability to innovate, create unique selling propositions and accelerate business strategies. Contact us to discuss how to solve your business problems with LLMs and integrate these solutions into your application landscape.
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David Wainwright
