In this exclusive feature, we’re thrilled to introduce John Shaw, CEO of Add Value Machine (shortly AVM). As a seasoned tech entrepreneur and AI visionary, John is at the helm of AVM’s mission to revolutionize the way businesses utilize Generative AI. The company is dedicated to developing a secure and compliant platform, addressing a critical need for enterprises and accelerating the adoption of AI to drive substantial business value. Throughout this interview, John shares insights about AVM’s unique approach, its value proposition, and how it navigates the evolving landscape of Generative AI.
InsideBIGDATA: Can you describe the vision behind AVM? What drove you to establish this venture?
John Shaw: Enterprises today are grappling with a multitude of security and compliance challenges as they strive to leverage Generative AI tools like ChatGPT for their businesses. The vision for AVM comes from our belief that these challenges shouldn’t hinder them from reaping the immense benefits enabled by this transformational technology. Our mission, thus, is to empower enterprises with a platform solution to exploit Generative AI’s capabilities in a secure and fully compliant manner, thereby driving significant business value.
InsideBIGDATA: What are some of the key security and compliance challenges that enterprises face with Generative AI? Could you share some real-life examples?
John Shaw: To give you an example, users often input sensitive and proprietary data directly into ChatGPT, without a clear understanding of how the tool processes and stores this data. This is especially a business risk for sectors dealing with highly confidential data, such as finance, healthcare, legal etc. These concerns have hit news headlines in several high-profile instances. Companies like Samsung, J.P. Morgan, and Amazon have experienced situations where the misuse of Generative AI tools has led to either banning or restricting the use of these tools, posing a significant hurdle to leveraging AI’s potential for innovation and growth.
InsideBIGDATA: Can you explain how AVM specifically addresses these security and compliance challenges?
John Shaw: AVM follows strict industry standards and integrates effortlessly with existing enterprise infrastructure. We employ Single Sign-On (SSO), using enterprise user credentials from identity provider solutions (IdP), to provide secure access to Generative AI tools. Our stringent data security measures, including robust encryption of data at rest and in transit, ensure that sensitive information remains secure within the platform. Furthermore, we stringently adhere to compliance standards for specific sensitive data types, carrying out checks for PCI, PHI, and PII that align with existing enterprise policies and keep comprehensive logs of all activities. This applies to both prompts and data sent to foundation models. We are also working towards achieving SOC2 and HIPAA compliance.
By offering these security and compliance features, integrated with existing enterprise systems, we provide a tool that not only leverages Generative AI for business enhancement but also bolsters a company’s security and compliance position.
InsideBIGDATA: How do you assure IT leaders who are very concerned with deploying Generative AI?
John Shaw: We understand that security and compliance is not just about having the right measures in place. It’s also about visibility and control. To that end, we’ve designed our platform to offer Chief Security Officers (CSOs) and Chief Compliance Officers (CCOs) direct visibility into the system via dashboards that provide real-time insights into data handling, usage, and compliance. Moreover, these dashboards and logs are integrated with existing enterprise tools like Splunk, which makes it easy for enterprises to monitor and analyze data from our platform within their existing security and compliance frameworks.
InsideBIGDATA: How does your solution deliver value to the enterprises it serves? Could you elaborate on the return on investment businesses can expect?
John Shaw: Unlike generic responses provided by most foundation models, AVM enables enterprises to securely upload their data to provide rich, context-specific interactions. This leads to a wide range of use cases that offer direct business benefits. For example, the marketing function can use AVM to upload customer surveys for sentiment analysis. In addition to saving time, it also provides more nuanced and detailed insights than traditional analysis, leading to more effective marketing strategies.
By creating internal chatbots, employees can perform comprehensive searches across knowledgebases and get prompt responses, reducing time spent on routine queries and allowing them to focus more on strategic tasks. Further, our platform provides prompt galleries organized by function and business type. This allows teams to standardize and customize their interactions with the AI, leading to enhanced productivity.
So, AVM delivers value by enabling secure, context-specific applications of Generative AI, enhancing productivity, driving operational efficiency, and supporting better strategic decisions. The combination of these benefits provides a strong return on investment, making AVM a compelling choice for enterprises looking to harness the power of Generative AI.
InsideBIGDATA: As Generative AI models continues to evolve, how do you plan on keeping your platform up-to-date with the latest developments?
John Shaw: We acknowledge that the field of Generative AI is rapidly evolving, and staying up-to-date with the latest developments is essential. Our strategy involves a two-pronged approach. Firstly, our platform is model-agnostic and supports foundation models from OpenAI, HuggingFace, Cohere, Amazon etc. This ensures our customers have access to a range of top-tier AI models to fit their unique requirements.
However, providing a variety of options is just one aspect of our strategy. We understand that selecting the right model for a specific use case can be a complex decision. Therefore, we also offer guidance to our customers to help them make informed choices. We provide metrics on each model’s performance, including factors like accuracy, bias, cost, and more, enabling businesses to choose a model that best aligns with their objectives and budget.
InsideBIGDATA: What’s next for AVM we can look forward to?
John Shaw: At AVM, we prioritize customer needs while focusing on Generative AI deployment challenges. As this technology evolves, we are committed to understanding its complexities and innovating solutions through collaboration. Our main goal is seamless enterprise adoption of Generative AI by integrating with existing security and enterprise systems, benefiting all stakeholders from IT managers to end-users.
You can learn more about AVM at https://www.addvaluemachine.com/
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