What is Generative AI?

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.

How can you implement Generative AI?

Select a category below to see example use cases for Gen 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




Product search


Coding assistance


Summarise financial reports


CV summarisation


Legal document summarisation


Contract review assistant

The ADSP Offering

Our Technologies

Thought Leadership

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.


Start a conversation

Take the first step by speaking with our team today.

Innovation Corner

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.


Frequently Asked Questions

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:

  • Understanding your organisation's goals and requirements
  • Conducting a feasibility study and assessing the potential impact
  • Identifying suitable Generative AI technologies and solutions
  • Developing a roadmap and strategy for implementation
  • Prototyping and testing the Generative AI solution
  • Integrating the solution with existing systems and processes
  • Providing user training and ongoing support
  • Monitoring and evaluating the performance of the Generative AI implementation
  • 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:

  • Content generation for marketing and advertising
  • Creative design and artwork generation
  • Natural language processing and text generation
  • Image and video synthesis
  • Virtual and augmented reality applications
  • Data augmentation and synthesis for machine learning
  • Personalised recommendations and customer experiences
  • Fraud detection and anomaly detection
  • Predictive analytics and forecasting
  • 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:

  • Alignment with your organisation's goals and requirements
  • Scalability and performance capabilities of the solution
  • Integration capabilities with existing systems and processes
  • Customisation options to meet specific needs
  • Reliability, support, and ongoing updates from the solution provider
  • Data privacy and security measures implemented by the solution
  • 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.

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