LLMOps & Conversational Intelligence for AI

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Dec 6, 2023

Guest Alex Kvamme, CEO @GetPathlight, talks about using AI to unlock market insights from discussions with customers and partners. They discuss the concept of conversational intelligence and its application in organizations, the trade-off between time and resources in gaining customer insights, where data in most organizations is located and how to get started, and the framework behind AI operations and lifecycle management.

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ANECDOTE

Founder Background: Seatme To Yelp

Alex described founding Seatme, the reservation product used by Yelp, and later being acquired by Yelp.
He used that startup experience to later build Pathlight focused on conversation intelligence.

INSIGHT

Customer Conversations Are The Primary Data

Customer conversations are often the richest source of product and market insight for companies.
Analyzing every conversation reveals trends, risks, and opportunities that manual sampling misses.

ADVICE

Apply Conversation Intelligence Cross-Functionally

Use conversation intelligence across functions like sales, support, product, and marketing to surface actionable signals.
Detect drivers of churn, supply chain issues, and product defects to route fixes and campaigns proactively.

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GUEST: Alex Kvamme (@KwameKvamme, CEO @GetPathlight) talks about using AI to unlock market insights from discussions with customers and partners.

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SHOW NOTES:

Topic 1 - Welcome to the show. Alex, Tell us a bit about your background.

Topic 2 - What is the concept of conversational intelligence and how does it apply to most organizations today? What problem is it trying to solve?

Topic 3 - I would think there is a trade off between time and resources to get to a customer issue vs. the value of that insight. How does an organization weigh the opportunity cost? How do you keep the insights generated from being overwhelming

Topic 4 - Let’s move from the concept to practical. Where is the data in most organizations today that will yield results and solve problems? How would you suggest folks get started and what use case are they likely to implement first? Is this data that humans either can’t or won’t get too because it is an enormous amount or maybe too tedious to pay for?

Topic 5 - How does all of this work under the hood? Is this one model or multiple models working in parallel? Is there a framework for the operations and lifecycle managed by an organization?

Topic 6 - Let’s talk about what it takes to get an LLM into production. The the rise of LLM’s and foundational models such as Llama2, there is an interest for organizations to use LLM’s, but going from concept to production still has a high barrier to entry. It’s easy to download a model, it’s much harder to either fine-tune it or set up RAG with a vector database for your specific use case. Would you agree and what are your thoughts?

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