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Generative AI reduces the cost of human-like thought to near zero, this creates huge opportunity to unlocks projects and possibilities that were previously unthinkable.
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Generative AI is revolutionising the way we approach creativity, communication, and collaboration.
At ADSP, we offer cutting-edge solutions that leverage the power of Generative AI to transform your business operations and unlock new opportunities. With our deep expertise and commitment to innovation, we are here to guide you on your journey towards harnessing the full potential of Generative AI.
Standardising grammar and spelling
Voice and tone standardisation
Document summarisation
Personalise ad copy to customer segments
Repurpose content across platforms
Blog posts and content marketing
Personalisation of ad or website copy
Generating images
Generating video summaries and scripts
Ideation for all forms of marketing
Personalise outreach emails
Presentation content generation
Negotiation training
Practise call scripts
Multilingual assistance
Customer service handling
Generating customer service reports
Work order assistance
Customer chat
Talk to your docs
Knowledge base article generation
Generate product descriptions
Accessibility
Product search
Coding assistance
Summarise financial reports
CV summarisation
Legal document summarisation
Contract review assistant
David Foster is co-founder of ADSP and the author of Generative Deep Learning: Teaching Machines To Paint, Write, Compose and Play (O'Reilly), an acclaimed technical textbook focused on novel applications of deep learning to generative modelling.
He holds an MA in Mathematics from Cambridge and an MSc in Operational Research. He is a faculty member of the Machine Learning Institute and winner of several international machine learning awards.
Explore how to leverage this remarkable new technology to create a competitive advantage.
Take the first step by speaking with our team today.
Scratch the surface of what is possible with Generative AI by taking a look at our interactive demo page that showcases the various use cases of Large Language Models (LLMs) across different sectors.
EXPLORE THE INNOVATION CORNER
How can the Roadmap service help my organisation in leveraging Generative AI technologies?
The Roadmap service is designed to craft a tailored strategy for your organisation's Generative AI implementation. It helps identify your organisation's goals, pinpoint opportunities and challenges, and develop future scenarios and execution plans. The Roadmap service provides a clear path to leverage Generative AI technologies effectively and maximise their potential for your business.
What are the benefits of creating a Generative AI prototype for a specific need?
Creating a Generative AI prototype allows you to address a specific need or problem within your organisation. It involves examining and evaluating model options, creating a prototype with user testing, and assessing the measurable impact. The benefits include gaining insights into the feasibility and effectiveness of Generative AI, identifying potential improvements, and paving the way for further development and implementation.
What are the advantages of implementing a Generative AI solution using commercial APIs?
Implementing a Generative AI solution using commercial APIs, such as GPT-4 and DALL.E2, offers several advantages. It allows for robust API-driven model deployment, integration with existing systems, and user training. Commercial APIs provide access to state-of-the-art Generative AI models and ongoing updates, saving time and resources compared to building models from scratch. They also offer scalability and reliability, backed by the expertise of the API providers.
How can a self-hosted Generative AI solution be customised to meet my organisation's requirements?
A self-hosted Generative AI solution offers the flexibility to customise the AI model creation and training process. It allows for the use of pre-trained open-source models or training on your proprietary data. This customisation ensures that the Generative AI solution aligns with your organisation's specific needs, industry requirements, and desired outcomes.
What are the key steps involved in implementing Generative AI in my organisation?
The key steps in implementing Generative AI in your organisation typically include:
How can Generative AI be integrated with existing systems and processes?
Generative AI can be integrated with existing systems and processes through various means, such as API integration, data exchange, and interoperability. Depending on the specific requirements, Generative AI solutions can be designed to seamlessly interact with your organisation's infrastructure, software applications, and data sources. This integration ensures that Generative AI becomes an integral part of your existing workflows and enhances the efficiency and effectiveness of your processes.
What are the different use cases for Generative AI in various industries?
Generative AI has a wide range of use cases across industries. Some examples include:
How can Generative AI enhance the efficiency and productivity of my organisation?
Generative AI can enhance efficiency and productivity in several ways. It can automate repetitive tasks, generate content or designs at scale, assist in decision-making processes, and provide valuable insights from large datasets. By leveraging Generative AI, organisations can streamline operations, reduce manual effort, accelerate innovation, and make data-driven decisions more effectively.
What are the considerations for selecting the right Generative AI solution for my organisation?
When selecting a Generative AI solution, it is important to consider factors such as:
How can Generative AI be customised to meet the specific needs of my organisation?
Generative AI can be customised to meet specific needs through various means, such as training on proprietary data, fine-tuning models, and incorporating domain-specific knowledge. By understanding your organisation's unique requirements, Generative AI solutions can be tailored to generate outputs that align with your desired outcomes, industry standards, and specific use cases.
How can my organisation ensure data privacy and security when implementing Generative AI?
Ensuring data privacy and security when implementing Generative AI involves implementing robust security measures, such as encryption, access controls, and secure data storage. It is important to adhere to relevant data protection regulations and best practices. Additionally, conducting regular security audits, monitoring system activity, and providing employee training on data privacy and security can help safeguard sensitive information.
How long does it take to develop a Generative AI prototype?
The time required to develop a Generative AI prototype can vary depending on the complexity of the specific need and the availability of data and resources. Typically, it can take anywhere from a few weeks to a couple of months to develop and test a Generative AI prototype. The duration may also depend on factors such as the level of customisation required, and the expertise of the team involved in the development process.
What is the difference between API-based and self-hosted Generative AI solutions?
API-based Generative AI solutions utilise pre-built models and APIs provided by third-party vendors. These solutions offer convenience, scalability, and access to state-of-the-art models. On the other hand, self-hosted Generative AI solutions involve building and training models in-house using open-source frameworks or proprietary data. Self-hosted solutions provide more customisation options and control over the model architecture and training process.
