This article is for companies building Voice AI Apps targeting the Contact Center. It outlines the key technical features, beyond accuracy, that are important while evaluating an OEM Speech-to-Text (STT) API. Usually, most analyses focus on the importance of accuracy and metrics like benchmarks of word error rates (WER). While accuracy is very important, there are other technical features that are equally important for contact center AI apps.
There are multiple use-cases for Voice AI Apps in the ContactCenter. Some of the common use cases are 1) AI Voicebot or Voice Agent 2) Real-time Agent Assist 3)Post Call Speech Analytics.
This article is focused on the third use-case which is Post-Call Speech Analytics. This use-case relies on batch STT APIs while the first two use-cases require streaming transcription. This Speech Analytics App helps the Quality Assurance and Agent-Performance management process. This article is intended for Product Managers and Engineering leads involved in building such AI Voice Apps that target the QA, Coaching and Agent Performance management process in the call center. Companies building such apps could include 1) CCaaS Vendors adding AI features, 2) Enterprise IT or Call Center BPO Digital organizations building an in-house Speech Analytics App 3) Call Center Voice AI Startups
Very often, call-center audio recordings are only available in mono. And even if the audio recording is in 2-channel/stereo, it could include multiple voices in a single channel. For example, the Agent channel can include IVR prompts and hold music recordings in addition to the Agent voice. Hence a very important criterion for an OEM Speech-to-Text vendor is that they provide accurate speaker diarization.
We would recommend doing a test of various speech-to-text vendors with a good sample set of mono audio files. Select files that are going to be used in production and calculate the Diarization Error Rate. Here is a useful link that outlines the technical aspects of understanding and measuring speaker diarization.
A very common requirement of Voice AI Apps is to redact PII – which stands for Personally Identifiable Information. PII Redaction is a post-processing step that a Speech-to-Text API vendor needs to perform. It involves accurate identification of entities like name, email address, phone number and mailing addresses and subsequent redaction both in text and audio. In addition, there are PCI – Payment Card Industry – specific named entities like Credit Card number, 3-digit PIN and expiry dates. Successful PII and PCI redaction requires post-processing algorithms to accurately identify a wide range of PII entities and cover a wide range of test scenarios. These test scenarios need to cover scenarios where there errors in user input and errors in speech recognition too.
There is another important capability related to PCI/PII redaction. Very often PII/PCI entities are present across multiple turns in a conversation between an Agent and Caller. It is important that the post-processing algorithm of the OEM Speech-to-Text vendor is able to process both channels simultaneously when looking for these named entities.
A Call Center audio recording could start off in one languageand then switch to another. The Speech-to-Text API should be able to detect language and then perform the transcription.
There will always be words that are not accurately transcribed by even the most accurate Speech-to-Text model. The API should include support for Hints or Keyword Boosting where words that are consistently misrecognized can get replaced by the correctly transcribed word. This is especially applicable for names of companies, products and industry specific terminology.
There are AI models that measure sentiment and emotion, and these models can be incorporated in the post-processing stage of transcription to enhance the Speech-to-Text API. Sentiment is extracted from the text of the transcript while Emotion is computed from the tone of the audio. A well-designed API should return Sentiment and Emotion throughout the interaction between the Agent and Caller. It should effectively compute the overall sentiment of the call by weighting the “ending sentiment” appropriately.
While measuring the quality of an Agent-Caller conversation, there are a few important audio-related metrics that are tracked in a call center. These include Talk-Listen Ratios, overtalk incidents and excessive silence and hold.
There are other LLM-powered features like computation of theQA Score and the summary of the conversation. However, these are features are builtby the developer of the AI Voice App by integrating the output of the Speech-to-TextAPI with the APIs offered by the LLM of the developer’s choice.
This article is for companies building Voice AI Apps targeting the Contact Center. It outlines the key technical features, beyond accuracy, that are important while evaluating an OEM Speech-to-Text (STT) API. Usually, most analyses focus on the importance of accuracy and metrics like benchmarks of word error rates (WER). While accuracy is very important, there are other technical features that are equally important for contact center AI apps.
There are multiple use-cases for Voice AI Apps in the ContactCenter. Some of the common use cases are 1) AI Voicebot or Voice Agent 2) Real-time Agent Assist 3)Post Call Speech Analytics.
This article is focused on the third use-case which is Post-Call Speech Analytics. This use-case relies on batch STT APIs while the first two use-cases require streaming transcription. This Speech Analytics App helps the Quality Assurance and Agent-Performance management process. This article is intended for Product Managers and Engineering leads involved in building such AI Voice Apps that target the QA, Coaching and Agent Performance management process in the call center. Companies building such apps could include 1) CCaaS Vendors adding AI features, 2) Enterprise IT or Call Center BPO Digital organizations building an in-house Speech Analytics App 3) Call Center Voice AI Startups
Very often, call-center audio recordings are only available in mono. And even if the audio recording is in 2-channel/stereo, it could include multiple voices in a single channel. For example, the Agent channel can include IVR prompts and hold music recordings in addition to the Agent voice. Hence a very important criterion for an OEM Speech-to-Text vendor is that they provide accurate speaker diarization.
We would recommend doing a test of various speech-to-text vendors with a good sample set of mono audio files. Select files that are going to be used in production and calculate the Diarization Error Rate. Here is a useful link that outlines the technical aspects of understanding and measuring speaker diarization.
A very common requirement of Voice AI Apps is to redact PII – which stands for Personally Identifiable Information. PII Redaction is a post-processing step that a Speech-to-Text API vendor needs to perform. It involves accurate identification of entities like name, email address, phone number and mailing addresses and subsequent redaction both in text and audio. In addition, there are PCI – Payment Card Industry – specific named entities like Credit Card number, 3-digit PIN and expiry dates. Successful PII and PCI redaction requires post-processing algorithms to accurately identify a wide range of PII entities and cover a wide range of test scenarios. These test scenarios need to cover scenarios where there errors in user input and errors in speech recognition too.
There is another important capability related to PCI/PII redaction. Very often PII/PCI entities are present across multiple turns in a conversation between an Agent and Caller. It is important that the post-processing algorithm of the OEM Speech-to-Text vendor is able to process both channels simultaneously when looking for these named entities.
A Call Center audio recording could start off in one languageand then switch to another. The Speech-to-Text API should be able to detect language and then perform the transcription.
There will always be words that are not accurately transcribed by even the most accurate Speech-to-Text model. The API should include support for Hints or Keyword Boosting where words that are consistently misrecognized can get replaced by the correctly transcribed word. This is especially applicable for names of companies, products and industry specific terminology.
There are AI models that measure sentiment and emotion, and these models can be incorporated in the post-processing stage of transcription to enhance the Speech-to-Text API. Sentiment is extracted from the text of the transcript while Emotion is computed from the tone of the audio. A well-designed API should return Sentiment and Emotion throughout the interaction between the Agent and Caller. It should effectively compute the overall sentiment of the call by weighting the “ending sentiment” appropriately.
While measuring the quality of an Agent-Caller conversation, there are a few important audio-related metrics that are tracked in a call center. These include Talk-Listen Ratios, overtalk incidents and excessive silence and hold.
There are other LLM-powered features like computation of theQA Score and the summary of the conversation. However, these are features are builtby the developer of the AI Voice App by integrating the output of the Speech-to-TextAPI with the APIs offered by the LLM of the developer’s choice.
This Article provides an overview of how AI Voice Agents can lower call center operating costs and also simultaneously elevate the brand perception and customer service reputation of the health plan or the TPA. These AI Voice Agents can automate routine inquiries like Claim Status, Eligibility Verification and Benefits Inquiries.
Health Plans and TPAs face intense pressure to lower operating costs. There are several reasons 1) Medicare (& Medicare Advantage) and Medicaid reimbursement rates are going down. 2) Commercial Groups are pushing back on decades of price increases. 3) Lucrative revenue sources like pharmacy rebates are drying up.
There is also an urgent need to elevate the member experience and improve the Net Promoter Score (NPS) . Newer products like level-funded, direct primary care and ICHRAs are directly competing with Health plans and TPAs and member experience is increasingly the source of competitive advantage.
A modern LLM-Powered AI Voice Agent can transform the call center experience. It can answer all the calls received at the call center - whether they are from members or providers. Callers can speak in full sentences with the AI Voice Agent and describe the reason for their call in their own language.
If the call is a routine inquiry like verifying eligibility or getting claims status, an AI Voice Agent can easily engage callers in a conversational experience, provide the answers and complete the call. In order to fully automate or answer these calls, the AI Voice Agents needs to integrate with the Payer's backend systems. These include member and eligibility databases, the CCaaS System and the CRM System.
Also AI Voice Agents is no longer a technology that will only become practical in the future. Unlike other technologies, AI is gaining rapid acceptance and such natural conversational interactions are a reality today. This Generative AI based Voice Agent has already been implemented in some of the fast-moving TPAs and health plans.
Any Health Plan or TPA will want an AI Voice Agent that seamlessly integrates with the phone system or CCaaS platform being used. Modern CCaaS platforms include Five9, Genesys Cloud, Dialpad, Nice CXOne, RingCentral and Avaya.
The AI Voice Agent should be able to transfer a call over the PSTN to the appropriate queue in the CCaaS platform based on the reason for the call. And most importantly, when an Agent actually becomes available and is able to take a call that is transferred by the AI Voice Agent, the Agent should receive a "Screen Pop" of all the information or context of the interaction with the AI Voice Agent. The most frustrating thing from a user-experience standpoint is to design a system or process where the caller has to repeat information that was already provided to the AI Voice Agent.
Even after the call is answered by the human agent, the AI Voice Agent should continue to monitor and listen to the conversation between the caller and the Live Agent. In other words, it is not sufficient to just provide the context of the caller's interaction with the AI Voice Agent. It is also very important to guide and help the AI Agent in real-time. In order to do this, the AI needs to have access to the real-time audio stream, stream the audio to a Large Language model secured with adequate guardrails. As context, the LLM needs to be provided with internal knowledge-base or support articles as context.
After the call is answered by the human agent, the AI Voice Agent should automatically extract sentiment and key audio and NLU metrics and also score or rate the interaction between the caller and the Live Agent for Quality Assurance purposes.
If you are at a Health Plan or a TPA? You can experience how Casey, Voicegain's AI Voice Agent for Payers, interacts with callers in call centers today.
Here is a link to experience our demo. All it needs is 5 minutes. In-depth instructions to interact with the Demo are provided on the website.
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CMS uses the Medicare Advantage & Part D (MA-PD) Star Rating system to steer more than 31 million beneficiaries toward high-quality plans and to decide who receives quality-bonus payments worth billions each year. Contracts at 4-start (★★★★) or 5-star (★★★★★) get marketing benefits, extra rebate dollars, and enrollee growth, while plans that slip below ★★★ can be sanctioned or terminated.
Voicegain Casey—launched April 2025— is an AI Voice Agent that handles every incoming provider or member call. It understands the intent of the call, performs HIPAA validation and automates routine inquiries like claims status, eligibility and benefits inquiries. For calls that need live human agent assistance, it transfers calls to live agents with a real-time screen-pop and continues to assist the Agents. It shaves 2–3 minutes of after-call work and boosting CSAT/NPS for health plans and TPAs.
In addition it records and analyzes each and every interaction and flags an interaction that has not met the standard. This can be of immense value to the MA Health Plan.
CMS Star Ratings increasingly reward real-time, patient-centric service. A 24/7 AI Voice Agent acts as a tireless first-responder—answering every call, closing care gaps, and feeding analytics back to your Stars team. For plans chasing ★★★★ or trying to stay above ★★★, deploying voice AI isn’t just a CX upgrade; it’s a direct lever on revenue-critical metrics.
If this topic is of interest and if you want to see how other health plans are using Voicegain Casey, get in touch with us
Voicegain is excited to announce the launch of Voicegain Casey, a payer focused AI Voice Agent that transforms the end-to-end call center experience with the power of generative AI. Voicegain Casey is a software suite of the following three Voice AI SaaS applications that helps a health plan or TPA call center improve operational efficiency and increase the CSAT and NPS (Net Promoter Score):
The AI Voice Agent replaces a touch-tone IVR with a modern LLM-powered human-like conversational voice experience. The AI Voice Agent can answer all calls that are received at a Health Plan or TPA Call center. It engages callers in a natural conversation and automates routine telephone calls like Claims Status, benefits inquiries and eligibility verifications. There is a very compelling business case to automate Provider phone calls in Health Plan and TPA call centers. Voicegain Casey has been specifically designed and developed for this goal. The AI Voice Assistant is also trained to perform HIPAA Validation and triaging of calls. So if the AI has not been trained to answer a specific question, it routes the call to the call center for live assistance.
Voicegain AI Co-Pilot is a browser extension that runs as a browser side-panel of Call Center Agent's CRM. This Co-Pilot is integrated with the Contact Center/CCaaS platform used in the Call Center. When a call transferred by the AI Voice Agent is eventually answered by a Live Agent, all the information collected by the AI Voice Assistant is presented as a "Screen-Pop" on the Desktop of the Live Agent (also referred to as CTI). This CTI/Screen pop feature ensures that the front-line call center staff can continue the conversation from where the AI Voice Agent left off. In addition to this Screen-Pop, the AI Co-Pilot also guides the front-line call center staff in real-time by listening, transcribing and analyzing the conversation and providing real-time guidance . The AI Co-Pilot also generates a summary of the conversation within five seconds of the completion of the call. This automated summarization easily saves 1-2 mins of wrap-up time or after call work which is very common in these health plan and TPA call centers.
Voicegain AI QA & Coach is a browser-based AI SaaS application that is used by Team-leaders, QA Call Coaches/Analysts and Operations Managers in a call center. This AI SaaS app records, transcribes and analyzes the entire conversation. It measure the sentiment of the callers and computes the QA score. Voicegain uses the latest open-source reasoning LLMs (like LLAMA 3, Gemma) and closed-source reasoning models like o-3 from Open AI. With the power of modern reasoning models, almost the entire QA score-card (approximately 80% of the questions) can be easily answered using AI. This SaaS App also provides a database of all whole-call-recordings of the entire conversation of the customer - which includes the AI Voice Assistant part, the transfer to the specific Call Center queue and eventually the entire conversation between the Live Agent and the Caller.
Voicegain Casey requires the following 3 key integrations to help with automation and real-time assistance.
Voicegain Casey integrates with modern CCaaS platforms. Current Integrations include Aircall, Five9 and Genesys Cloud. Planned integrations include Ringcentral, NICE CXOne and Dialpad.
Voicegain Casey integrates with the CRM software of the Health plan or the TPA. This can be an off-the-shelf CRM like Zendesk or Salesforce. It can also be a proprietary/homegrown CRM. As long as the CRM is a browser-based SaaS application, this should not be an issue. Voicegain Casey AI Co-Pilot is a browser-extension that is installed in the side-panel of the same browser tab as the CRM. At the end of the call, the summary of the call is automatically generated and available on the browser extension within 5 seconds of the end of the call.
Voicegain Casey needs access to the member eligibility and claims data.
For further information on Voicegain Casey, including a demo, please visit this link
If you would like to understand Voicegain Casey in more detail or if you would prefer a detailed product demo over a Zoom video call, please do not hesitate to send us an email. You can reach us at sales@voicegain.ai or support@voicegain.ai
Voicegain, the leading Privacy-first Voice AI platform for enterprises and AI SaaS companies, is thrilled to announce the successful completion of a System and Organizational Control (SOC) 2 Type 2 Audit performed by Johanson LLP. This achievement underscores Voicegain's ongoing commitment to the highest standards of security, availability, and confidentiality for customer data.
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. Voicegain’s SOC 2 Type 2 report did not have any noted exceptions and was therefore issued with a “clean” audit opinion from Johanson LLP. The SOC 2 Type 2 certification is widely recognized as a rigorous measure of an organization’s systems and controls over an extended period.
"As a Privacy first Voice AI Platform, data protection and trust are at the heart of Voicegain. Whether you are a developer working for a startup using our APIs or a Fortune 500 enterprise user of our platform, you shouldn’t have to worry about the controls in place for your sensitive voice data. It has been close to 2 years since we embarked on our SOC 2 Type 1 journey and I am really proud of what our team has accomplished. We look forward to providing businesses worldwide with Voice AI solutions that deliver true peace of mind when it comes to security and compliance" said Dr Jacek Jarmulak, Co-founder, CTO & CISO Of Voicegain.
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:
From a functional standpoint, achieving SOC 2 Type 2 compliance doesn’t change anything. Our APIs and Apps will work exactly as they always have and as expected. However SOC 2 Type 2 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.
Our commitment to security doesn’t end with SOC 2 Type 2. We are looking forward to increasing the maturity of the entire security process. This includes the following:
"We understand that in today's fast moving industry landscape, data security is non-negotiable," added Arun Santhebennur, Co-founder & CEO of Voicegain. "By achieving SOC 2 Type 2 compliance, we aim to set a high watermark in the Voice AI market. Our customers can have full confidence that their sensitive information is protected throughout its lifecycle."
To request a copy of our SOC 2 Type 2 report, please email security.it@voicegain.ai
This article outlines how the modern Voicegain deep-learning based Speech-to-Text/ASR can be a simple and affordable alternative for businesses that are looking for a quick and easy replacement to their on-premise Nuance Recognizer. Nuance has announced that its going to end support for Nuance Recognizer, its grammar-based ASR which uses the MRCP protocol, sometime in 2026 or 2027. So organizations that have a Speech-enabled IVR as their front door to the contact center need to start planning now.
With the rise of Generative AI and highly accurate low latency speech-to-text models, the front door of the call center is poised for major transformation. The infamous and highly frustrating IVR phone menu will be replaced by Conversational AI Voicebots; but this will likely happen over the next 3-5 years. As enterprises start to plan their migration journey from these tree-based IVRs to an Agentic AI future, they would like to do this on their timelines. In other words, they do not want to be forced to do this under the pressure of a deadline because of EOL of their vendor.
In addition, the migration path proposed by Nuance is a multi-tenant cloud offering. While a cloud based ASR/Speech-to-Text engine is likely to make sense for most businesses, there are companies in regulated sectors that are prevented from sending their sensitive audio data to a multi-tenant cloud offering.
In addition to the EOL announcement by Nuance for their on-premise ASR, a major IVR platform vendor like Genesys has also announced that its premise-based offerings - Genesys Engage and Genesys Connect - will also approach EOL at the same time as the Nuance ASR.
So businesses that want a modern Gen AI powered Voice Assistant but want to keep the IVR on-premise in their datacenter or behind their firewall in a VPC will need to start planning very quickly what their strategy is going to be.
At Voicegain, we allow enterprises that are in this situation and want to remain on-premise or in their VPC with a modern Voicebot platform. This Voicebot platform runs on modern Kubernetes clusters and leverages the latest NVIDIA GPUs.
Rewriting the IVR Application logic to migrate from a tree-based IVR menu to a conversational Voice Assistant is a journey. It would require investments and allocation of resources. Hence a good first step is to simply replace the underlying Nuance ASR (and possibly the IVR platform too). This will guarantee that a company can migrate to a modern Gen-AI Voice Assistant on its timelines.
Voicegain offers a modern highly accurate deep-learning-based Speech-to-text engine trained on hundreds of thousands of hours of telephone conversations. It is integrated into our native modern telephony stack. It can also talk over the MRCP protocol with VoiceXML based IVR platforms and it supports the traditional Speech grammars (SRGS, JJSGF). Voicegain also supports a range of built-in grammars (like Zipcode, Dates etc).
As a result, it is a simple "drop-in" replacement to the Nuance Recognizer. There is no need to rewrite the current IVR application. Instead of pointing to the IP address of the Nuance Server, the VoiceXML platform just needs to be reconfigured to point to the IP address of the Voicegain ASR server. This should take no more than a couple of minutes.
In addition to the Voicegain ASR/STT engine, we also offer a Telephony Bot API. This is a callback style API that includes our native IVR platform and ASR/STT engine can be used to build Gen AI powered Voicebots. It integrates with leading LLMs - both cloud and open-source premise based - to drive a natural language conversation with the callers.
If you would like to discuss your IVR migration journey, please email us at sales@voicegain.ai . At Voicegain, we have decades of experience in designing, building and launching conversational IVRs and Voice Assistants.
Here is also a link to more information. Please feel free to schedule a call directly with one of our Co-founders.
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Voicegain helps developers build awesome voice enabled apps by providing them with the most accurate, affordable and accessible Speech-to-Text platform.
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