Written by Brandon Yu | 7 min read
In the realm of technological innovation, few developments have sparked as much excitement and potential as the rapid evolution of generative AI. And this is not just merely technological advancement, but the paradigm shift that marks a new age of digital transformation.
What is the GenAI Stack
The GenAI Stack is a cutting-edge suite of tools and partners, which seamlessly integrates generative artificial intelligence with traditional software stacks and redefines the boundaries of what's possible in the digital world.
We view the GenAI stack as a four-dimensional offering to large enterprises looking to transform their organization. It’s a seamless combination of intersecting players that all unite to provide high-performing business units with the capabilities they need to succeed and thrive on generative AI.
The four components of the GenAI stack are:
- The Implementation Layer → a consulting partner with deep AI expertise to help uncover the most pressing GenAI use cases in your business and strategies to implement solutions.
- The Application Layer → the end-user AI tools. This presents as a diverse set of tooling for both ease-of-use for non-technical users, but also tooling that enhances the technical user’s existing workflow.
- The Model Layer → the LLMs that power these AI applications.
- The Infrastructure Layer → the cloud infrastructure, and all the computing power behind the Small and Large Language Models.
A successful business has GenAI adoption as one of its principal goals. At a minimum, closing a partnership in each of these four layers will be instrumental in furthering GenAI competencies within the organization and for those they serve.
The 4 Components of the GenAI Stack
We’ve shared the four components of the GenAI stack. Now let’s go through each one in more detail.
1. The Implementation Layer
Description: This layer represents the practical application of AI solutions, focusing on delivering these technologies to end clients. It requires a combination of technical expertise and an understanding of the client's needs to successfully implement AI solutions. This could be small to large consulting companies or boutiques that specifically focus on AI implementation.
This layer will define the overall GenAI strategy and approach, identify strategic partnerships and other companies to collaborate with, and uncover the most impactful use-cases for their clients. Likely to follow is the implementation of such solutions, as well as the upskilling and onboarding of thousands of employees on new and relevant GenAI tooling.
Example: Capgemini, one of our partners, is a prime example in this realm. They have a robust AI practice, offering tailored AI solutions to businesses across various industries. Their approach often involves understanding client-specific challenges and integrating AI technologies to address these issues effectively.
2. The Application Layer
Description: This layer is where AI becomes accessible to the end-user. The tools here are designed to be user-friendly, often enabling interaction in natural language, making them suitable for non-technical individuals. This is likely the layer that the everyday user will be interfacing with - it’s in natural language, does not require vast technical expertise, and can be easily accessible. These are the tools that thousands of enterprise employees will use to boost productivity, unlock creativity, and optimize everyday tasks.
Example: A prominent example is OpenAI's ChatGPT, which allows users to interact using natural language. It's designed for simplicity and accessibility, enabling a wide range of users to leverage AI for various tasks like content creation, coding assistance, and information queries. There are also apps like Vellum and Copy.ai, of which have specific applications to build better prompts or create better marketing copy respectively.
3. The Model Layer
Description: At this layer lie the Large Language Models (LLMs) that are the powerhouse behind AI applications. These models are trained on vast datasets to understand and generate human-like text, making them the core of many AI applications. Most of the technical experts will be working with this layer, whether to incorporate this model in their own applications or through building models of their own.
Example: Google's Gemini and OpenAI's GPT-4 are leading examples of LLMs, as well as Cohere’s Command model.
4. The Infrastructure Layer
Description: This foundational layer is all about the cloud infrastructure and the compute required to support the massive, scalable cloud architecture that AI applications depend on. In large companies, these partnerships are already established and are responsible for several of the organizations’ existing software stacks.
Example: Amazon Web Services (AWS) and Microsoft Azure are key players in this layer. AWS offers extensive cloud services that provide the necessary computing power for running complex AI models. Similarly, Azure's AI and machine learning services support a range of AI-driven applications, offering scalable and efficient cloud computing resources.
The Benefits of the GenAI Stack
The GenAI stack provides different angles to where all members of the organization can leverage the power of GenAI. Below are a few benefits of why it’s crucial to establish these partnerships at each of the four levels.
Firstly, the GenAI Stack redefines the concept of efficiency and productivity. By leveraging AI's predictive and generative capabilities, businesses can automate complex processes, reduce operational costs, and enhance decision-making with unprecedented speed and accuracy. This shift is not just about doing things faster; it's about doing things that were previously unimaginable, opening new avenues for innovation and growth.
Secondly, the GenAI Stack democratizes access to advanced technologies. With user-friendly interfaces and intuitive design, these tools are no longer confined to the domain of AI specialists. This accessibility empowers a broader range of professionals and creatives to harness the power of AI in their work, fostering a more inclusive and diverse technological landscape.
Furthermore, the GenAI Stack is poised to revolutionize customer experiences. By understanding and anticipating customer needs through advanced data analytics and AI-driven insights, businesses can offer personalized, engaging, and seamless interactions. This level of customization and responsiveness will set new standards in customer service and satisfaction.
How to Leverage the GenAI Stack?
Leveraging the GenAI stack can be a game-changer for companies aiming to stay at the forefront of technological innovation. So how can you get started?
Hackathons provide a unique opportunity to experiment with various GenAI providers swiftly and effectively. You’ll be able to have tons of vendors all in the same place, at the same time, with members of your organization having the freedom to work in them all.
At Onova, we recently ran a GenAI hackathon for Capgemini in partnership with Google, where one of the primary objectives was indeed to upskill thousands of developers in the GenAI offerings of the Google Cloud Platform. We’ve ran hackathons for McDonald’s, the Bank of Montreal, and HSBC with different players within the GenAI stack as well.
By bringing in all members of the GenAI stack – a cloud partner, an LLM partner, a couple of AI tools, and an implementation partner to participate alongside your own employees – you can create an environment where you can work with and test solutions from vendors across the entire stack.
This not only fosters innovation but also has the potential to upskill your entire organization, positioning you for success in the ever-expanding world of GenAI.
For questions or inquiries on how you can adopt your own GenAI stack, we’d love to offer an opportunity for you to talk with our CEO and Founder, Victor Li, here.