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Build Business Intelligence Solution with Nebula LLM

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Build a data-driven solution that leverages Symbl.ai's Generative AI powered by Nebula LLM for capturing customer sentiments, preferences, and areas of concern. This guide aims to help you integrate Nebula LLM into your existing systems, manage data pipelines, and utilize Conversation Business Intelligence for actionable insights.

Prerequisites

  • Basic understanding of APIs
  • Access to Symbl.ai's Nebula LLM API
  • Data storage solutions like Snowflake, Google BigQuery, or AWS Redshift
  • Analytics tools like Tableau, Looker, or Power BI

Step-by-Step Guide

Step 1: API access

Step 2: Generating insights with Nebula LLM

  • To get access to the Nebula LLM, sign-up here
  • Nebula LLM is available via an API. Use the quick start guide to make API calls
  • Nebula LLM accepts inputs in a prompt format containing the human conversation in a textual format and an instruction of the desired output
  • Generating Transcripts: If you don't have textual transcripts, use Symbl.ai’s Async API and Streaming API to process audio and video conversations. Use the formatted transcript conversation API to generate the transcript for the audio and video conversations you processed
  • Designing prompt: Now, you have the transcript. You need to give the LLM an instruction which gives you a desired output. To learn more about how to give the right instruction, read more about prompt design

Step 3: Accessing insights from Nebula LLM

  • As soon as the conversation is processed and the response is generated, the response is pushed to the Snowflake data cloud
  • Symbl.ai creates roles and access in Snowflake to create a secure shared datastore. To learn more about how Symbl.ai shared data securely, click here
  • To access the shared datastore, you will need to have a snowflake account. To learn more about consuming shared datastore, click here
  • If you do not have a snowflake account, you can still consume the insights from Nebula. Symbl.ai creates a read-only datastore and provides access

Step 4: Analytics and Insights

  • Connect with Analytics Tools: Use the in-built connectors within analytics tools like Tableau, Looker, or Power BI to link your data storage. You can also connect multiple databases, build mapping and perform analysis
  • Train ML Models: Optionally, you can use these insights to train machine learning models for tasks like churn prediction

Updated about 1 year ago

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