Enterprises are increasingly looking to mine the treasure trove of insights from voice conversations using AI. These conversations take place daily on video meeting platforms like Zoom, Google Meet and Microsoft Teams and over telephony in the contact center (which take place on CCaaS or on-premise contact center telephony platforms).

What is Voice AI?

Voice AI or Conversational AI refers to converting the audio from these conversations into text using Speech recognition/ASR technology and mining the transcribed text for analytics and insights using NLU. In addition to this, AI can be used to detect sentiment, energy and emotion in both the audio and text. The insights from NLU include extraction of key items from meetings. This include semantically matching phrases associated with things like action items. issues, sales blockers, agenda etc.

Over the last few years, the conversational AI space has seen many players launch highly successful products and scale their businesses. However most of these popular Voice AI options available in the market are multi-tenant SaaS offerings. They are deployed in a large public cloud provider like Amazon, Google or Microsoft. At first glance, this makes sense. Most enterprise software apps that automate  workflows in functional areas like Sales and Marketing(CRM), HR, Finance/Accounting or Customer service are architected as multi-tenant SaaS offerings. The move to Cloud has been a secular trend for business applications and hence Voice AI has followed this path.

However at Voicegain, we firmly believe that a different approach is required for a large segment of the market. We propose an Edge architecture using a single-tenant model is the way to go for Voice AI Apps.

Why does the Edge make sense for Conversational AI?

By Edge, we mean the following

1) The AI models for Speech Recognition/Speech-to-Text and NLU run on the customer's single tenant infrastructure – whether it is bare-metal in a datacenter or on a dedicated VPC with a cloud provider.

2) The Conversational AI app -which is usually a browser based application that uses these AI models is also completely deployed behind the firewall.

We believe that the advantages for Edge/On-Prem architecture for Conversational/Voice AI is being driven by the following four factors

1.    Privacy, Confidentiality and Data Residency requirements

Very often, conversations in meetings and call centers are sensitive from a business perspective. Enterprise customers in many verticals (Financial Services, Health Care, Defense, etc) are not comfortable storing the recordings and transcripts of these conversations on the SaaS Vendor's cloud infrastructure. Think about a highly proprietary information like product strategy, status of key deals, bugs and vulnerabilities in software or even a sensitive financial discussion prior to the releasing of earnings for a public company. Many countries also impose strict data residency requirements from a legal/compliance standpoint. This makes the Edge (On-Premises/VPC) architecture very compelling.

2. Accuracy/Model Customization

Unlike pure workflow-based SaaS applications, Voice AI apps include deep-learning based AI Models –Speech-to-Text and NLU. To extract the right analytics, it is critical that these AI models – especially the acoustic models in the speech-recognition/speech-to-text engine are trained on client specific audio data. This is because each customer use case has unique audio characteristics which limit the accuracy of an out-of-the-box multi-tenant model. These unique audio characteristics relate to

1.    Industry jargon – acronyms, technical terms

2.    Unique accents

3.    Names of brands, products, and people

4.    Acoustic environment and any other type of audio.

However, most AI SaaS vendors today use a single model to serve all their customers. And this results in sub-optimal speech recognition/transcription which in turn results in sub-optimal NLU. 

3. Latency ( for Real-time Voice AI apps) 

For real-time Voice AI apps - for e.g in the Call Center - there is an architectural advantage for the AI models to be in the same LAN as the audio sources.

4. Affordability

For many enterprises, SaaS Conversational AI apps are inexpensive to get started but they get very expensive at scale.

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Voicegain’s Edge Offering

Voicegain offers an Edge deployment where both the core platform and a web app like Voicegain Transcribe can operate completely on our clients infrastructure. Both can be placed "behind an enterprise firewall".

Most importantly Voicegain offers a training toolkit and pipeline for customers to build and train custom acoustic models that power these Voice AI apps.

Have a question? Or just want to talk?

If you have any question or you would like to discuss this in more detail, please contact our support team over email (support@voicegain.ai) 

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