AI & Machine Learning Services
Enabling organisations to improve decision making, forecast future outcomes, better engage with customers and adapt to changing markets
Leverage AI & machine learning to improve your services and make decisions better, faster and at scale
Our AI and machine learning projects begin with initial discovery and analysis sessions. We’ll work closely with your stakeholders to review existing processes and datasets, as well as evaluate new opportunities to identify where AI and machine learning solutions are both viable and add immediate value.
We’ll work together to assess different technologies and architectures to develop a strategy for implementing AI and machine learning, exploring various types of solutions — from predictive analysis models, to reactive machines. Following this, we’ll develop proof of concepts and prototypes for initial feasibility studies and to generate early feedback.
We’ll collaborate to help you plan and deliver strategies for data cleansing, transformation, consolidation and warehousing, as well as ensuring that your systems and infrastructure can support sophisticated models. Overall enabling your organisation to find and consume the information you need to become data driven.
Leverage different types of AI and machine learning solutions
Natural Language Processing
Chatbots, virtual assistants, IoT experiences and other AI services for improved communication.
Predictive Analysis
Train machine learning models for predictive analysis across a range of use cases, products and environments.
Planning Optimisation
Leverage combinatorial optimisation models to solve problems from task scheduling to capacity planning.
Azure OpenAI Service
Deliver applications using GPT-3/4 powered search, conversation, text completion and other advanced features through the API.
"Embracing AI in business isn't just about technology; it's about forging a brighter, more efficient, and customer-centric future."
Azure OpenAI Service leverages the concept of prompt engineering, where a system prompt provides context to an AI model, and enables it to respond more accurately to user prompts. By providing a system prompt that specifies the context and purpose of the AI, developers can create powerful AI systems without the need for extensive training data.
This is a significant advantage over traditional machine learning models, which require a large amount of data to be trained before they can be used effectively. With Azure OpenAI Service, developers can use a wide range of pre-built models that are already optimised for performance and accuracy, saving time and resources.
Use Cases for Azure OpenAI Service:
Azure OpenAI Services are being used across industries and organisations to scale up applications with powerful AI tools. Here are a few use cases:
Copywriting:
The GPT-3.5/4 model speeds up human creativity by producing ideas quickly. This can lead to more efficient product descriptions, headlines, and website content for organisations.
Sentiment analysis:
This involves analysing large volumes of text data to determine the overall sentiment or tone. This could be useful for a variety of applications, including social media monitoring, customer feedback analysis and market research.
Advanced chatbots:
Azure OpenAI services can be used to build powerful chatbots that can handle customer service inquiries, technical support issues and other common tasks. With the ability to understand natural language, these chatbots can provide a more personalised experience for users and can help reduce the workload on customer service and support teams.
Azure OpenAI Service is just one of the many AI/ML services that Azure offers. Other popular tools offered by Azure include Azure Machine Learning Studio, Azure Cognitive Services and Custom Vision. These services enable organisations to build, train and deploy powerful AI tools at scale.
GRADIS is a AI and Data Solutions Company based in the UK, headquartered in London. We can provide the artificial intelligence services you need to deliver new digital products and services at scale.
This is a significant advantage over traditional machine learning models, which require a large amount of data to be trained before they can be used effectively. With Azure OpenAI Service, developers can use a wide range of pre-built models that are already optimised for performance and accuracy, saving time and resources.
