AI with AR changes everything
End users can ask contextual questions surrounding any data point (from a reference pdf, or general knowledge), manipulate visualizations on the fly, write Python code to do data processing, all encouraging infinite exploration and discovery.
Building with AI
The fusion of AI and AR/MR empowers us to unlock greater potential of our cognitive abilities, enabling us to solve complex problems, make informed decisions, and share with colleagues.
Flow applies six meaningful functions in our AR/MR + AI data visualization platform:
Flow applies six meaningful functions in our AR/MR + AI data visualization platform:
To learn more, please read this AR Insider article written by our founder Jason Marsh, and contact us below!
- Spoken commands simplify the user interface
- The LLM explains and provides context that goes beyond the data
- Create and manipulate visualizations
- User-uploaded PDF documents ensure expert content-specific accuracy
- Manipulate the underlying data through AI-generated Python scripts
- “What should I notice about my data?” identifies trends & outliers, correlations, and insights
Agentic AI
You’ve seen a number of demonstrations of AI where the user outlines a series of steps the AI needs to take to produce an end goal. Multiple functionalities need to work together to accomplish the goal, including querying a corpus of knowledge, manipulating swarms, writing Python code to process the datasets, running the code, and applying the updated data to the swarm.
AI agents enable multi-step workflows triggered by simple, natural commands, often using compound requests—‘do this and then that’.
AI agents enable multi-step workflows triggered by simple, natural commands, often using compound requests—‘do this and then that’.
Agentic AI doesn’t just process commands—it empowers users. By translating multi-step tasks into AR visualizations, it transforms financial reporting into a collaborative, interactive experience that engages the visual cortex.
AI + AR is magic
AI makes AR more useful, and overcomes some core limitations of AR, which include a constrained field of view within which it is difficult to include more than one panel of UI buttons and interactions.
The inverse is also true: AR helps AI more concrete. In the context of data visualization, LLMs can identify how to process massive amounts of data quickly, identify patterns, and present insights, but the results are usually limited to text. On the other hand, AR can provide an interactive, immersive platform to visualize these insights and/or anchor the insights within the larger context of the scope of the data.
The inverse is also true: AR helps AI more concrete. In the context of data visualization, LLMs can identify how to process massive amounts of data quickly, identify patterns, and present insights, but the results are usually limited to text. On the other hand, AR can provide an interactive, immersive platform to visualize these insights and/or anchor the insights within the larger context of the scope of the data.
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