This article describes how users on free or unpaid Zoom plans can get AI generated meeting transcripts, summaries, and actions items.

Cloud-based Meeting AI SaaS solutions do not work on free Zoom accounts

There are many compelling generative AI powered SaaS offerings for transcription, summarization, and action item extraction for Zoom Meetings. These include companies like Otter, Grain, Read, Fireflies, Krisp, Superhuman and others. However, all these cloud-based Meeting AI SaaS solutions require paid Zoom accounts – and this because they integrate with Zoom Cloud recording which is a feature in the paid Zoom plan.

Why is a this a bigger problem than it seems?

Now paid Zoom plans are quite affordable – the Pro Zoom plan(as of the date of this post) is priced at $16/month. However, many businesses – whether they are a small startup, a mid-size business or an enterprise customer– use free Zoom plans for a vast majority of the employees in the company. In speaking with prospective customers, we estimate that for many businesses only 5 – 10% of the employee base has a paid Zoom plan.

Meetings on a free Zoom plan can only be up to 40 minutes – which is adequate for most meetings. Hence it works quite well for a large segment of users. Now if these meetings need to be transcribed and summarized, users would need to upgrade to a paid plan. For many businesses, since 90%+of the users are on free Zoom plans, upgrading all them to a paid plan can be a very significant expense.

How does Voicegain Transcribe address this challenge?

Voicegain Transcribe is an AI meeting assistant that integrates with Zoom Local Recording. Zoom Local Recording allows users to save the Zoom recording to their local computer instead of Zoom’s Cloud. A big advantage of Zoom Local Recording is that it is available on free Zoom plans. As a result, there is no need to upgrade to a paid Zoom license. Voicegain Transcribe also has a free tier that is good for 5 hours (300 minutes) every month. As a result, users that host or attend up to 10 half-hour Zoom meetings can get transcription and LLM-powered insights like summarization and action item extraction for free.

The added benefit – Data Privacy

Of-course, the other major benefit of local recording is data privacy. Many businesses do not like to store sensitive meeting content on Zoom’sCloud or for that matter on any another vendor’s cloud – but they are forced to do so because of lack of options. Especially in the age of AI and LLMs, there is a lot of concern and paranoia around proprietary information being used to train AI models.  

While any business can started a trial with Voicegain’s multi-tenant cloud SaaS offering, our entire solution can be deployed as a single-tenant solution in your private cloud. Voicegain transcribe can operate fully independently - without the need to connect to our cloud for any service.

Sign up today with a free plan of Voicegain Transcribe!

You can get started and evaluate our offering by clicking here. As shared above, we offer 5 hours (300 minutes) of free transcription and LLM powered summarization every month.

If you have any questions, please send us an email to support@voicegain.ai

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This article describes ideas for a business with a speech-enabled IVR to plan its upgrade/transition to a modern generative AI powered conversational Voice Bot on its own timeline and at an affordable cost.

Businesses of all sizes have an IVR system that acts as a front-door for all their customer voice conversations. In terms of functionality, these IVRs systems vary widely; they can range from performing basic call-routing and triaging to automating simple calls - like taking payments, scheduling appointments, or providing account balance etc. While most of them accept touch-tone/DTMF as input, the more advanced ones also accept natural language speech as input and hence referred to as speech-enabled IVRs.

However these IVRs are getting obsolete and there is a growing demand to upgrade to a more conversational experience.

1. Traditional IVR/ASR Stack is getting obsolete

Traditionally Speech IVR applications were deployed on-premise; built on the same platform as the main contact center ACD/Switch. But soon, IVRs were deployed on the Cloud too. The on-premise IVR vendors include Avaya, Genesys and Cisco and cloud-based IVRs include vendors like Five9, RingCentral, Mitel and 8x8.

For speech recognition, the most popular option in the past had been Nuance. Nuance’s ASR technology – which gained popularity in the early2000s - preceded today’s neural-network-based engines. It was pre-Alexa and pre-Siri– and so both the vocabulary (i.e what the customer could actually say in response to a prompt) and the accuracy was limited compared to today’s neural-network-based speech-to-Text. In addition, the protocol for communication between Nuance and the telephony stack was MRCP – a protocol that is not being actively developed for many years now.

2. Modern Conversational AI Stack is being reimagined with Gen AI

Modern Conversational AI Stack for Voice Bots include a modern neural ASR/Speech-to-Text engine and neural Text-to-Speech and a NLU based Bot Framework. It is much more capable than what was available to build directed dialog Speech IVRs in the past.

Today’s neural ASR/STT engines can transcribe not just a few words or phrases, but entire sentences and they also do it very accurately. As consumers get used to such experiences with their voice assistants at home or in their cars, they expect the same when they contact a business over the phone.

There also been significant advances with modern no-code NLU Bot frameworks that are used to build the Bot Logic and conversation flow. These Bot frameworks are also evolving with the advent of generativeAI technologies like ChatGPT.

While the above two paragraphs describe good reasons to upgrade IVRs, there are some key factors that are driving a rather rushed timeline for businesses to plan this IVR migration 

3. Factors  driving a rather rushed timeline for IVR Migration

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a. Contact Center platforms focused on Cloud sales

Companies with on-premise Contact Centers are increasingly migrating to the Cloud. Even the on-premise contact center vendors too are focused on migrating their install base to the Cloud. So when an enterprise plans to migrate the contact center platform to the cloud, they would need to migrate the IVRs too.

b. Modern ASR/STTs focused on selling their AI/neural-network-based offerings

As explained above, modern AI/neural-network-based ASR/STT engines are more accurate and support a conversational experience. Hence ASR/STT vendors are focused on selling these newer offerings. It is not possible for businesses to use these newer ASRs with existing telephony stack. Both the protocol support (Web sockets and gRPC vs MRCP) and the application development method (grammar based vs. large vocabulary transcription with intent capture) are very different.

c. Demand to use a single Application/Bot Framework for both Chat and Voice

In the past companies built the application logic for Chatbot and IVR independently; very often different vendors provided the Chatbot and VoiceBot. However, given the powerful and flexible Conversational AI platforms that are available in the market, they want to use the same platform to drive the conversation turns of a Chatbot interaction and a Voice Bot interaction.

4. Taking Control of when to upgrade the IVR

As explained above, migrating from the traditional IVR stack to a modern Conversational AI stack entails not just rewriting the application logic but it is also likely to involve moving the infrastructure from on-premise to the cloud. This can be an expensive undertaking.

At Voicegain, we think that can help companies should be able to this at their own timeline.

We have developed an ASR that can support both (a) grammar-based recognition using MRCP and (b) large vocabulary transcription on audio streamed using modern protocols like Websockets. Also our platform can be deployed on-premise or in your VPC. So our platform supports both an existing application without any rewrite while also being capable of supporting a conversation voice bot when it is developed at some point in the future.

As a result, customers can take control of when to migrate/upgrade their IVRs. Most importantly, they would not be forced into invest in an upgrade/migration of their entire IVR application just because an existing ASR vendor would stop supporting an older version of the software.

If you have any questions or you would like to schedule a discussion to understand your IVR upgrade options, contact us on support@voicegain.ai.

To test our MRCP grammar-based ASR or our large vocabulary ASR, please sign up for a free developer account. Instructions are provided here.

Voicegain, the leading Edge Voice AI platform for enterprises and Voice SaaS companies, is thrilled to announce the successful completion of a System and Organizational Control (SOC) 2 Type 1 Audit performed by Sensiba LLP.

Developed by the American Institute of Certified Public Accountants (AICPA), the SOC 2 Information security audit provides a report on the examination of controls relevant to the trust services criteria categories covering security, availability, processing integrity, confidentiality, and privacy. A SOC 2 Type I report describes a service organization's systems, whether the design of specified controls meets the relevant trust services categories. Voicegain’s SOC 2 Type I report did not have any noted exceptions and was therefore issued with a “clean” audit opinion from Sensiba.

"As a Privacy first Voice AI Platform, we take security very seriously here at Voicegain. As a developer using our APIs or as a user of our platform, you shouldn’t have to worry about the controls in place for your sensitive voice data." said Dr Jacek Jarmulak, Co-founder, CTO & CISO Of Voicegain.

"At Voicegain, we have maintained a robust information security program for over a decade now and this has been communicated throughout our organization for quite some time now. Earlier this year, we achieved PCI-DSS compliance for our Developer platform and today's successful completion of the SOC 2 Type 1 Audit marks a significant milestone in our security and compliance journey." continued Dr Jarmulak.

What Is SOC 2?

Service Organization Control 2(SOC2) is a set of criteria established by the American Institute of Certified Public Accountants (AICPA) to assess controls relevant to the security, availability, and processing integrity of the systems a service organization uses to process users’ data and the confidentiality and privacy of the information processed by these systems. SOC 2 compliance is important for Voice AI platforms like Voicegain, as it demonstrates that we have implemented controls to safeguard users’ data.

There are two types of SOC 2 compliance:

1. SOC 2 Type 1: Validates that an organization has established appropriate controls at a specific point in time. Voicegain's successful audit established this as of Jul 14 2023.
2. SOC 2 Type 2: Confirms that an organization has maintained and operated those controls over a period of time, typically 6 to 12 months.

Implications for Voicegain Users

From a functional standpoint, achieving SOC 2 Type 1 compliance doesn’t change anything. Our APIs and Apps will work exactly as they always have and as expected. However SOC 2 Type 1 compliance means that we have established a set of controls and processes to ensure the security of our users’ data. This compliance demonstrates that we have the necessary measures in place to protect sensitive information from unauthorized access and disclosure.

What’s Next? SOC 2 Type II

Our commitment to security doesn’t end with SOC 2 Type 1. We are already working towards achieving SOC 2 Type 2 compliance, which we plan to accomplish in Q1 2024. Thiswill further validate that we maintain the highest levels of security, ensuring that our users can continue to rely on and trust Voicegain.

Voicegain's speech recognition technology has been widely recognized for its innovation and impact across industries. From call centers and customer service applications to transcription of Zoom Meetings in enterprise and healthcare and transcription of classroom lectures, Voicegain's solutions have demonstrated their ability to transform audio data into actionable insights. The attainment of SOC 2 Type 1 compliance further solidifies Voicegain's position as a reliable and responsible provider of cutting-edge speech recognition services.

"We understand that in today's digital landscape, data security is non-negotiable," added Arun Santhebennur, Co-founder & CEO of Voicegain. "By achieving SOC 2 Type 1 compliance, we aim to set an industry standard for ensuring the confidentiality and integrity of the data entrusted to us. Our customers can have full confidence that their sensitive information is protected throughout its lifecycle."

To request a copy of our SOC 2 Type 1 report, please email security.it@voicegain.ai

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Today we are really excited to announce the launch of Voicegain Whisper, an optimized version of Open AI's Whisper Speech recognition/ASR model that can be accessed using Voicegain APIs. The same APIs currently process over 60 Million minutes of audio every month for leading enterprises in the US including Samsung, Aetna and several Fortune 100 enterprise. Generative AI developers now have access to a well-tested accurate, affordable and accessible transcription API. They can integrate Voicegain Whisper APIs with LLMs like GPT 3.5 and 4 (from Open AI) PaLM2 (from Google), Claude (from Anthropic), LLAMA 2 (Open Source from Meta), and their own private LLMs to power generative conversational AI apps. Open AI open sourced several versions of the Whisper models released. With today's release Voicegain supports Whisper-medium, Whisper-small and Whisper-base. Voicegain now supports transcription in over 99 different languages that are supported by Whisper.

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There are four main reasons for developers to use Voicegain Whisper over other offerings:

1. Support for Private Cloud/On-Premise deployment (integrate with Private LLMs)

While developers can use Voicegain Whisper on our multi-tenant cloud offering, a big differentiator for Voicegain is our support for the Edge. The Voicegain platform has been architected and designed for single-tenant private cloud and datacenter deployment. In addition to the core deep-learning-based Speech-to-text model, our platform includes our REST API services, logging and monitoring systems, auto-scaling and offline task and queue management. Today the same APIs are enabling Voicegain to processes over 60 Million minutes a month. We can bring this practical real-world experience of running AI models at scale to our developer community.

Since the Voicegain platform is deployed on Kubernetes clusters, it is well suited for modern AI SaaS product companies and enterprises that want to integrate with their private LLMs.

2. Affordable pricing - 40% less expensive than Open AI 

At Voicegain, we have optimized Whisper for higher throughput. As a result, we are able to offer access to the Whisper model at a price that is 40% lower than what Open AI offers.

3. Enhanced features for Contact Centers & Meetings.

Voicegain also offers critical features for contact centers and meetings. Our APIs support two-channel stereo audio - which is common in contact center recording systems. Word-level timestamps is another important feature that our API offers which is needed to map audio to text. There is another feature that we have for the Voicegain models - enhanced diarization models - which is a required feature for contact center and meeting use-cases - will soon be made available on Whisper.

4. Premium Support and uptime SLAs.

We also offer premium support and uptime SLAs for our multi-tenant cloud offering. These APIs today process over 60 millions minutes of audio every month for our enterprise and startup customers.

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About OpenAI-Whisper Model

OpenAI Whisper is an open-source automatic speech recognition (ASR) system trained on 680,000 hours of multilingual and multitask supervised data collected from the web. The architecture of the model is based on encoder-decoder transformers system and has shown significant performance improvement compared to previous models because it has been trained on various speech processing tasks, including multilingual speech recognition, speech translation, spoken language identification, and voice activity detection.

Source

Getting Started with Voicegain

Any developer - whether you are a one person startup or a large enterprise - can access the Voicegain Whisper model by signing up for a free developer account. We offer 15,000 mins of free credits when you sign up today. This should allow you to build and test your app. Here is a link to get started on Voicegain Console, our developer focused web application. Here is also a link to our Github

There are two ways to select Voicegain Whisper. The first is to configure the settings in Voicegain Consoler, our developer focused UI. The second method is to configure Whisper as the model in API settings. If you would like more information or if you have any questions, please drop us an email support@voicegain.ai

We are super excited to announce the release of two new features with our Voicegain Transcribe app.

(i) Summarization powered by LLMs.

(ii) Single Sign On (Currently available for Voicegain Edge/On-Prem customers only)

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1. Summarization & Key Item Extraction

Summarization of a transcript is extremely valuable for various types of audio content. Whether a user is transcribing a business meeting, a classroom lecture, a podcast or a web event, reviewing just the summary of the transcript is a big time-saver compared to having to read the entire transcript. With this release, every transcript generated by Voicegain Transcribe will be summarized accurately using powerful state-of-the-art LLMs.

In addition to the summary of the transcript, Voicegain also supports extraction of key items like Actions, Issues, Risks, and Dependencies.

For users of Voicegain Transcribe Cloud, the summarization is powered by ChatGPT (GPT 3.5 Turbo APIs). Essentially we submit the meeting transcript to ChatGPT and we ask it to summarize the meeting. We display and store the returned summary in Voicegain Transcribe.

For users of Voicegain Transcribe Edge/On-Prem, we offer an open-source LLM model that has been fine-tuned on meeting data. This fine-tuned LLM model gets deployed along with the entire Voicegain platform behind the customer's firewall (whether in a private cloud or datacenter).

2. Single Sign On using OIDC

With this new release, Voicegain Transcribe also supports the SSO feature using the OIDC protocol. Most popular Identity Management software solutions - like Okta, Ping Identity, Microsoft, Oracle, RSA etc support the OIDC protocol.

This feature is currently available only to Voicegain Edge/On-Prem customers and it will be made available very soon to Voicegain Cloud customers too.

About Voicegain Transcribe

Voicegain Transcribe is a privacy-first Meeting AI platform that can be deployed "fully behind" the firewall of a company/business. It is also available for access as a cloud service.

Get Started for Free today!

By signing up today, you will be signed up on our forever Free Plan - which makes you eligible for 120 mins of Meeting Transcription free every month . Once you are  satisfied with our accuracy and our user experience, you can easily upgrade to Paid Plans or contact us for On-Premise/Virtual Private Cloud options.

If you have any questions, please email us at support@voicegain.ai

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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.

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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).

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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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