Improved Efficiency
Boost productivity for everyone from data users to business users, spanning from data modeling to data consumption.
In our recent posts, LLM is not enough and AI-guided Self-service, we’ve discussed our belief in the transformative power of AI, particularly LLMs, to change self-service analytics drastically. However, it’s not a simple plug-and-play integration, it’s more than just throwing a ChatGPT UI into the app. There are significant challenges to ensure reliability, accuracy, and governance. This requires not just a well-designed interface, but also a robust data infrastructure, including a semantic layer.
That’s why we’ve prioritized foundational components like our As-code Semantic Layer and Canvas Dashboard to enable transparency and credibility. Now that these foundational pieces are in place, we are confident to invest further in AI functionalities, leveraging the efficiency of the latest LLM models.
At our core, we believe that AI is here not to replace but to enhance the roles within the analytics workflow. By leveraging AI, we empower everyone to work more efficiently while ensuring accuracy and governance. This enables organizations to make better data-informed decisions and drive meaningful outcomes.
Boost productivity for everyone from data users to business users, spanning from data modeling to data consumption.
All AI-generated outputs are transparent and debug-able with our robust as-code infrastructure.
Our semantic layer offers a higher level of data abstraction for the LLM to ensure governance.
Empower business users to self-serve more effectively with our governed dataset and dashboard exploration mechanisms.
Business users can answer ad-hoc data questions based on selected metrics and dimensions.
Business users can generate dashboards quickly themselves from a prompt.
Boost user engagement by recommending relevant datasets, visualizations, and dashboards, while enabling follow-up on discovered insights.
Assist data teams in developing data models and metrics faster and with less effort.
Boost data teams’ productivity and adoption by generating AQL metrics based on their natural language inputs.
Automatically generate data modeling metadata to reduce data analyst workload and enhance data consumability.
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