Leverage unparalleled insights from unstructured data to enhance your bot interactions and user satisfaction
Moving from an intent based bot to a LLM based bot means moving from a relatively small, knowable model to an open-ended system connected to raw data. New tools are needed to curate raw text data now that people are trying to leverage that data directly.
Dashbot helps unlock wider-spread use of LLMs by providing tools to curate and manage unstructured text.
Understanding the "why" behind your CSAT and NPS survey results is vital in the world of customer experience. However, manually analyzing thousands of open-ended survey responses is time-consuming, resource-intensive, and challenging to maintain over time.
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Deep Topic Insights: Segment user inputs by topics and reasons, aligning your bot's responses with user expectations.
Measuring Satisfaction: Gauge how well your LLM bot meets user needs and keeps conversations on track.
Relevance Checks: Assess the accuracy and relevance of your bot's responses against your knowledge base.
Visualizing User Journeys: Map out user interactions to identify common paths and potential bottlenecks, enabling targeted optimizations.
Scope and Topic Distribution: Understand the breadth and depth of your LLM's knowledge base, ensuring comprehensive and accurate coverage.