Artificial Intelligence

The DataGuard AI is an omni-present helper on our platform which doesn’t need to be clicked or activated by the user. AI-based suggestions are docked at the bottom of the screen and are easily identifiable from users based on iconography and colour.

Because our AI is docked in a horizontal modal, content and action suggestions are displayed via cards in the expanded modal. If a user doesn’t wish to engage, the modal does not interfere with their experience using the platform.

The AI modal continually replenishes with relevant suggestions and actions as the user moves through the platform to complete their task(s).

• AI-based suggestions must be relevant to the user's context at that moment. Suggestions should align with the user's goals, tasks, or preferences at that moment in time, or be predictive based on analytics.
• Avoid overwhelming users with irrelevant or excessive suggestions that can be perceived as intrusive. AI should be easily accessible, but also easy to ignore.
• Always use AI-specific iconography and colours whenever AI is present to ensure that the user associates and recognises the experience.
• Continuously monitor the performance and impact of AI-based suggestions. Analyze metrics such as user engagement, satisfaction, and task completion rates to assess the effectiveness of the suggestions. Regular evaluation allows for iterative improvements and ensures the suggestions remain valuable and beneficial.

AI can analyse user data and behaviour to provide personalised recommendations and tailored experiences. It can learn from user interactions and adapt the platform to meet individual needs, increasing customer satisfaction and engagement. For example, AI suggests to a user that they should contact the consultant ahead of the jour fixe.

By leveraging AI algorithms, a SaaS platform can analyze large amounts of data to identify patterns, trends, and insights. This enables predictive analytics, empowering users to make data-driven decisions and anticipate future outcomes. For example, suggested meeting times shown to users are based on prior jour fixe meeting times.

AI can automate repetitive and mundane tasks, streamlining workflows and freeing up time for users. For example, AI-powered recommendation systems can automate content curation or the completion of documents and questionnaires.

NLP techniques enable our platform to understand and interpret human language, facilitating features such as voice commands, text analysis, sentiment analysis, and language translation. This enhances user interactions and expands the platform's capabilities.

For example, AI can support DataGuard experts and customer success managers to track customer progress, anticipate next steps and potential upsells or escalations – as well as provide suggestions for how to best serve customers based on analysis of other customer’s data.

AI can power search functionality within the DataGuard platform, enabling users to find relevant information quickly. Additionally, AI-driven recommendation systems can suggest relevant content, products, or services based on user preferences, inputs and historical data.

For example, intelligent search and recommendation on our platform can support users to find and assess compliant software vendors.