New unified platform combines AI voice agent automation with Real-time agent assistance and Auto QA, enabling healthcare payers to reduce average handle time (AHT) and improve first contact resolution (FCR) in their call centers.

IRVING, Texas and SAN FRANCISCO, Jan. 7, 2026 /PRNewswire-PRWeb/ -- Voicegain, a leader in AI Voice Agents and Infrastructure, today announced the acquisition of TrampolineAI, a venture-backed healthcare payer-focused Contact Center AI company whose products supports thousands of member interactions. The acquisition unifies Voicegain's AI Voice Agent automation with Trampoline's real-time agent assistance and Auto QA capabilities, enabling healthcare payers to optimize their entire contact center operation—from fully automated interactions to AI-enhanced human agent support.

Healthcare payer contact centers face mounting pressure to reduce costs while improving member experience. The reasons vary from CMS pressure, Medicaid redeterminations, Medicare AEP volume and staffing shortages. The challenge lies in balancing automation for routine inquiries with personalized support for complex interactions. The combined Voicegain and TrampolineAI platform addresses this challenge by providing a comprehensive solution that spans the full spectrum of contact center needs—automating high-volume routine calls while empowering human agents with real-time intelligence for interactions that require specialized attention.

"We're seeing strong demand from healthcare payers for a production-ready Voice AI platform. TrampolineAI brings deep payer contact center expertise and deployments at scale, accelerating our mission at Voicegain." — Arun Santhebennur

Over the past two years, Voicegain has scaled Casey, an AI Voice Agent purpose-built for health plans, TPAs, utilization management, and other healthcare payer businesses. Casey answers and triages member and provider calls in health insurance payer call centers. After performing HIPAA validation, Casey automates routine caller intents related to claims, eligibility, coverage/benefits, and prior authorization. For calls requiring live assistance, Casey transfers the interaction context via screen pop to human agents.

TrampolineAI has developed a payer-focused Generative AI suite of contact center products—Assist, Analyze, and Auto QA—designed to enhance human agent efficiency and effectiveness. The platform analyzes conversations between members and agents in real-time, leveraging real-time transcription and Gen AI models. It provides real-time answers by scanning plan documents such as Summary of Benefits and Coverage (SBCs) and Summary Plan Descriptions (SPDs), fills agent checklists automatically, and generates payer-optimized interaction summaries. Since its founding, TrampolineAI has established deployments with leading TPAs and health plans, processing hundreds of thousands of member interactions.

"Our mission at Voicegain is to enable businesses to deploy private, mission-critical Voice AI at scale," said Arun Santhebennur, Co-founder and CEO of Voicegain. "As we enter 2026, we are seeing strong demand from healthcare payers for a comprehensive, production-ready Voice AI platform. The TrampolineAI team brings deep expertise in healthcare payer operations and contact center technology, and their solutions are already deployed at scale across multiple payer environments."

Through this acquisition, Voicegain expands the Casey platform with purpose-built capabilities for payer contact centers, including AI-assisted agent workflows, real-time sentiment analysis, and automated quality monitoring. TrampolineAI customers gain access to Voicegain's AI Voice Agents, enterprise-grade Voice AI infrastructure including real-time and batch transcription, and large-scale deployment capabilities, while continuing to receive uninterrupted service.

"We founded TrampolineAI to address the significant administrative cost challenges healthcare payers face by deploying Generative Voice AI in production environments at scale," said Mike Bourke, Founder and CEO of TrampolineAI. "Joining Voicegain allows us to accelerate that mission with their enterprise-grade infrastructure, engineering capabilities, and established customer base in the healthcare payer market. Together, we can deliver a truly comprehensive solution that serves the full range of contact center needs."

A TPA deploying TrampolineAI noted the platform's immediate impact, stating that the data and insights surfaced by the application were fantastic, allowing the organization to see trends and issues immediately across all incoming calls.

The combined platform positions Voicegain to deliver a complete contact center solution spanning IVA call automation, real-time transcription and agent assist, Medicare and Medicaid compliant automated QA, and next-generation analytics with native LLM analysis capabilities. Integration work is already in progress, and customers will begin seeing benefits of the combined platform in Q1 2026.

Following the acquisition, TrampolineAI founding team members Mike Bourke and Jason Fama have joined Voicegain's Advisory Board, where they will provide strategic guidance on product development and AI innovation for healthcare payer applications.

The terms of the acquisition were not disclosed.

About Voicegain

Voicegain offers healthcare payer-focused AI Voice Agents and a private Voice AI platform that enables enterprises to build, deploy, and scale voice-driven applications. Voicegain Casey is designed specifically for healthcare payers, supporting automated and assisted customer service interactions with enterprise-grade security, scalability, and compliance. For more information, visit voicegain.ai.

About TrampolineAI

TrampolineAI was a venture-backed voice AI company focused on healthcare payer solutions. The company applies Generative Voice AI to contact centers to improve operational efficiency, member experience, and compliance through real-time agent assist, sentiment analysis, and automated quality assurance technologies. For more information, visit trampolineai.com.

Media Contact:

Arun Santhebennur

Co-founder & CEO, Voicegain

press@voicegain.ai

Media Contact

Arun Santhebennur, Voicegain, 1 9725180863 701, arun@voicegain.ai, https://www.voicegain.ai

SOURCE Voicegain

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This article highlights the technical challenges in redaction of PII, PCI and PHI information in call center recordings for compliance requirements. It is focused on CIOs, CISOs and VP Info-Secs of Enterprises and BPOs that are responsible for compliant recording and storage in their Call Centers. It is also relevant to Product and Engineering leaders of Voice AI software companies targeting the call center.

PII redaction is a major requirement in regulated industries like telecom, financial services, health care and government. These call routinely deal with a lot of Personally Identifiable Information (PII) and Personal Health Information (PHI). In addition if a call center is processing payments, it needs to adhere to standards of PCI-DSS.

How does Redaction work?

Redaction of Call Center recordings involves 3 main steps, 1) Transcription 2) Named-Entity Recognition of PII/PHI/PCI entities and 3) Redaction (in both Audio & Text) of these entities. In order to be compliant with standards like PCI and HIPAA, it is important that before storing the audio data and text transcript long-term, all such PII information is masked in text and removed in audio prior to storage.

Key Technical Challenges in Redaction

1. Simplistic Algorithms designed for mono channels will not work

Most modern call center recordings are 2-channel or stereo. A simplistic algorithm designed for mono-channel recording will not work. For example, establishing that a credit card is being requested by the Agent can span multiple turns in the conversations. The NER algorithm while establishing context has to consider both channels. Also establishing where the context starts and ends is an important criterion.

2. PII information is provided in imprecise ways

In real-world conversations, customers are not very precise while sharing PII information. For example when they share their credit card number they can make mistakes while reading out the 15 or 16 digits. The Agent may not hear certain digits and ask the user to confirm or repeat certain digits. So when you are designing the algorithm to identify the PII entities it needs to be ablet to correct for all of this. 

3. Speech Recognition Errors

Transcription accuracy especially in telephone conversations, which encode the audio in 8kHz, may result in missed or additional digits. Having a simple rule related to digit length - say 15 for American Express and 16 for Visa will not work. Also sometimes digits may get recognized as words based on accents. For example "eight" may get recognized as "ate" if spoken with an accent.

4. Context Windows

As shared in the first point above, the problem becomes one of establishing a context window where the PII information is shared. In the first place, it needs to be long enough to even recognize that PII information is being requested. However extending the context window too much could start to introduce false positives. There may be other important numbers - for example say a tracking number or confirmation number that is also spoken not too far from the PII information.

Net-net designing an algorithm that is able to look across two channels for stereo recordings, account for speech recognition errors and perform accurate PII entity recognition over turns of a conversation with a well-balanced context window is the key to successful PII redaction.

Achieved 95% Redaction Accuracy for Sutherland Global

We partnered with Sutherland Global, a Top 10 BPO, to build a compliant recording for their large install base of Fortune 500 companies. The Voicegain platform - which performs both transcription and PII compliant redaction- is deployed in their VPC. We tuned our algorithm over several months to get it to pass stringent test criteria.

Today our PII Redaction has achieved an accuracy of over 95%.

Get in touch

If you are looking to build a PII/PCI/PHI compliant recording solution, please give us a shout. We would love to share our experiences. Email us at sales@voicegain.ai

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Voicegain is releasing the results of its 2025 STT accuracy benchmark on an internally curated dataset of forty(40) call center audio files. This benchmark compares the accuracy of Voicegain's in-house STT models with that of the big cloud providers and also Voicegain's implementation of OpenAI's Whisper.

In the years past, we had published benchmarks that compared the accuracy of our in-house STT models against those of the big cloud providers. Here is the accuracy benchmark release in 2022 and the first release in 2021 and our second release in 2021. However the datasets we compared our STT models was a publicly available benchmark dataset that was on Medium and it included a wide variety of audio files - drawn from meetings, podcasts and telephony conversations.

Since 2023, Voicegain has focused on training and improving the accuracy of its in house Speech-to-Text AI models call center audio data. The benchmark we are releasing today is based on a Voicegain curated dataset of 40 audio files. These 40 files are from 8 different customers and from different industry verticals. For example two calls are consumer technology products, two are health insurance and one each in telecom, retail, manufacturing and consumer services. We did this to track how well the underlying acoustic models are trained on a variety of call center interactions.

Why a separate benchmark for Call Center Audio Data ?

In general Call Center audio data has the following characteristics

1. Narrowband: Most telephony systems used in call center encode the audio in a limited bandwidth 8 kHz format. Unless AI models are trained on such audio, the recognition accuracy can be limited.
2. Noisy data: There is significant background noise and over-talk in call center audio recordings.
3. Accents: Call Center agents work in different international locations. Even the end customers in the US have different accents. So the STT engine needs to be tuned to different accents.

Results of our Benchmark:

How was the accuracy of the engines calculated? We first created a golden transcript (human labeled) for each of the 40 files and calculated the Word Error Rate (WER) of each of the Speech-to-Text AI models that are included in the benchmark. The accuracy that is shown below is 1 - WER in percentage terms.

Most Accurate - Amazon AWS came out on top with an accuracy of 87.67%

Least Accurate - Google Video was the least trained acoustic model on our 8 kHz audio dataset. The accuracy was 68.38%

Most Accurate Voicegain Model - Voicegain-Whisper-Large-V3 is the most accurate model that Voicegain provides. Its accuracy was 86.17%

Accuracy of our inhouse Voicegain Omega Model - 85.09%. While this is slightly lower than Whisper-Large and AWS, it has two big advantages. The model is optimized for on-premise/pvt cloud deployment and it can further be trained on client audio data to get an accuracy that is higher.

Custom Acoustic Model Training

One very important consideration for prospective customers is that while this benchmark is on the 40 files in this curated list, the actual results for their use-case may vary. The accuracy numbers shown above can be considered as a good starting point. With custom acoustic model training, the actual accuracy for a production use-case can be much higher.

Private Cloud/On-Premise Deployment

There is also another important consideration for customers that want to deploy a Speech-to-Text model in their VPC or Datacenter. In addition to accuracy, the actual size of the model is very important. It is in this context that Voicegain Omega shines.

Additional Result of our Streaming Speech-to-Text

We also found that Voicegain Kappa - our Streaming STT engine has an accuracy that is very close to the accuracy of Voicegain Omega. The accuracy of Voicegain Kappa is less than 1% lower than Voicegain Omega.

Reproducing this Benchmark

If you are an enterprise that would like to reproduce this benchmark, please contact us over email (support@voicegain.ai). Please use your business email and share your full contact details. We would first need to qualify you, sign an NDA and then we can share the PII-redacted version of these audio call recordings.

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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.

Introduction

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

1. Accurate Speaker Diarization

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.

2. Accurate PII/Named Entity Redaction and PCI Compliance

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.

3. Language Detection

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.

4. Hints/Keyword Boosting

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.

5. Sentiment and Emotion

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.

6. Talk-Listen Ratios, Overtalk and Other Incidents

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.

7. Other Optional LLM-Powered Features

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.

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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.

Introduction - Why optimize your call center now?

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.

The Answer - AI Voice Agent focused on Health Plans and TPAs

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.

Designing the AI Voice Agent to seamlessly handoff calls to Live Agents

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.

Improving Productivity of Live Agents with real-time assistance

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.

Analyzing and Rating the end-to-end caller journey

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.

Are you at a Health Pan or a TPA? Call Casey, our AI Voice Agent today.

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.

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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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Why the Stars Matter

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.

A Quick Primer on How CMS Calculates the Rating

Where Plans Lose Stars

• Member experience & complaints (currently 4× weight): CAHPS scores are sensitive to long hold times, language access gaps, and unresolved grievances.
• Access & call-center metrics: CMS audits interpreter availability, TTY performance, and after-hours answer rates.
• Preventive-care process gaps: Screenings, vaccines and follow-up calls drag ratings if members can’t be reached.
• Medication adherence & MTM: Three adherence measures remain 3× weighted; missed refill reminders hurt scores.

Enter the Always-Available AI Voice Agent

Voicegain Casey, an AI Voice Agent that can maintain and boost CMS Star Rating

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.

Implementation Tips for Health-Plan Leaders

1. Start with high-volume intents (eligibility, claim status, pharmacy refills) to free agents for complex CAHPS-sensitive calls.
2. Integrate with your CCaaS & CRM so AI can pre-populate member data and log screenings automatically.
3. Set Stars-oriented KPIs—e.g., hold time < 30 s, outbound reach rate > 70 %, refill reminder completion—to quantify lift.
4. Monitor sentiment & compliance; modern voice AI can redact PHI, track interpreter usage, and surface grievances before they hit CTM.
5. Plan for 2026 weight changes: Even with Patient-Experience weight dropping to 2×, CMS still ties >30 % of Stars to access and experience—so keep delighting callers.

The Bottom Line

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.

Get in touch

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

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