Elevate your LLM Strategy with A.I.-Powered Analytics

Leverage unparalleled insights from unstructured data to enhance your bot interactions and user satisfaction

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Structure the Unstructured

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.

Why Analyze LLM Bot Interactions?

LLM bots, while powerful, introduce complexity in understanding and optimizing interactions. Without traditional intents, how do you know if your bot is performing as expected?

1. Monitoring Guardrails
Ensuring conversations stay within desired parameters.
2. Understanding User Intentions
Breaking down conversations to reveal user needs and satisfaction.
3. Optimizing for Relevance
Adjusting your LLM bot to provide more accurate, relevant responses.

{Problem / Solution Headline}
Your VoC program is failing
Uncover the 'Why' Behind Your Customer Feedback

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. 

Benefit #1: 
All of your CX data at your fingertips

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Benefit #2:
Automated insights from unstructured surveys

Benefit explained. Benefit explained. Benefit explained.

Benefit #3: 
Difficulty determining an action plan to improve CX

Benefit explained. Benefit explained. Benefit explained.

Start leveraging Dashbot's LLM Analytics Solutions


User Input & Satisfaction Analysis

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.


LLM Response Analysis

Relevance Checks: Assess the accuracy and relevance of your bot's responses against your knowledge base.


LLM Conversation Flows

Visualizing User Journeys: Map out user interactions to identify common paths and potential bottlenecks, enabling targeted optimizations.


Knowledge Base Analysis

Scope and Topic Distribution: Understand the breadth and depth of your LLM's knowledge base, ensuring comprehensive and accurate coverage.

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