Enterprise, Edge, Insights, Transcription

Why Meeting Transcripts should be stored in your private infrastructure in the age of LLMs?

LLMs like ChatGPT and Bard are taking the world by storm! An LLM like ChatGPT is really good at both understanding language and acquiring knowledge of this content. The outcome of this is almost eerie and scary. Because once these LLMs acquire knowledge, they are able to answer very accurately questions that in the past seemed to require human judgement.

One big use-case for LLMs is in the analysis of business meetings - both internal (between employees) and external (e.g conversations with customers, vendors, etc).

In the past few years, companies have been primarily using multi-tenant Revenue/Sales Intelligence and Meeting AI SaaS offerings to transcribe business conversations and extract insights. With such multi-tenant offerings, transcription and natural language processing takes place on the Vendor cloud. Once the transcript is generated, NLU models offered by the Meeting AI vendor is used to extract insights. E.g, Revenue intelligence products like Gong extract questions and sales blockers in sales conversations. Most meeting AI assistants extract summaries and action items.

Essentially these NLU models - and many of these predate the LLMs - were able to summarize, extract topics, keywords and phrases. Enterprises did not mind using the cloud infrastructure of the vendor to store the transcripts as what this NLU could do seemed pretty harmless.

However the LLMs take this to a whole different level. Our team used Open AI Embeddings API to generate embeddings of our daily meeting transcripts that were conducted over a one-month period. We stored these embeddings in an open-source Vector database (our knowledge-base).  During testing, for each user question, we generated embedding of the question and queried the vector database (i.e knowledge-base) to get related/similar embeddings.

Then we provided these related documents as context and the user question as a prompt to GPT 3.5 API so that it could generate the answer. We got really really good results.

We were able to get answers to the following questions

1. Provide a summary of the contract with <Largest Customer Name>.

2. What is the progress on <Key Initiative>?

3. Did the Company hire new employees?

4. Did the Company discuss any trade secrets?

5. What is the team's opinion on Mongodb Atlas vs Google Firestore?

6. What new products is the Company planning to develop?

7. Which Cloud provider is the Company using?

8. What is the progress on a key initiative?

9. Are employees happy working in the company?

10. Is the team fighting fires?

ChatGPT's responses to the above questions was amazingly and eerily accurate. For Question 4, it did indicate that it did not want to answer the question. And when it do not have adequate information (e.g. Question 9), it did indicate that in its response. 

At Voicegain, we had always been a big proponents of why Voice AI needs to remain on the Edge. We had written about it in the past.

Meeting transcripts in any business is a veritable gold mine of information. Now with the power of LLMs, they can now be queried very easily to provide amazing insights. But if these transcripts are stored in another Vendor's cloud, it has the potential to expose very proprietary and confidential information of any business to 3rd parties.

Hence for businesses it is extremely critical that such transcripts are stored only in private infrastructure (behind the firewall). It is really important for Enterprise IT to make sure this happens in order to safeguard proprietary and confidential information.

If you are looking for such a solution, we can help. At Voicegain, we offer Voicegain Transcribe, an enterprise-ready solution for Meeting AI. With Voicegain Transcribe, the entire solution can deployed either in a datacenter (on bare-metal) or in a private cloud. You can read more about it here.

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