Case Studies

How AI-powered Smart Transcription and QA translates into significant time savings for Aloware’s customers

Leveraging AssemblyAI, Aloware shipped fast, accurate call transcription and smarter Quality Assurance tools to its customers in just 6 weeks.

How AI-powered Smart Transcription and QA translates into significant time savings for Aloware’s customers

As a Contact Center Software as a Service, Aloware specializes in helping companies turn more leads into deals for companies worldwide. Aloware also supports company compliance and efficiency initiatives and has helped power over 20 million calls, send over 30 million SMS/MMS, and reach over 15 million contacts.

Facilitating customer calls and messages was a great starting point for Aloware’s product roadmap. But these calls and messages generated gigabytes of unstructured data that were sitting unused. Aloware’s product team wondered if AI models could help them turn this unstructured data into meaningful insights for their many customers.

Thanks to recent advances in AI research and innovation, AI models are more accurate today than ever before. The most sophisticated models serve as the brains behind today’s impressive tech, such as self-driving cars, ChatGPT, automated fraud detection, personalized recommendations, and more. The principles behind these models are also being integrated into technology such as speech recognition and automated text analysis.

For example, AssemblyAI’s AI model for automatic speech recognition, Conformer-2, is trained on 1.1M hours of audio data, resulting in significant improvements to proper nouns, alphanumerics, and robustness to noise.

With this in mind, Aloware began searching for the right AI tech that could help them serve their customers better. Their product team knew automated, accurate transcription was a good fit for their product roadmap, but wondered what else they could do on top of this transcription data that would help their customers unlock meaningful insights that could positively affect business outcomes.

The product team also had a short time frame to deployment, so they needed a condensed AI stack that could meet all of their requirements and speed the time to go-live.

Aloware’s search led them to AssemblyAI. AssemblyAI’s API for AI models like Core Transcription and Audio Intelligence helps product teams like Aloware’s more easily access the latest AI research to build new features that deliver winning outcomes for their customers— across performance, productivity, and efficiency.

Learn about AssemblyAI’s new framework for LLMs, LeMUR

Deploying AI in Just 6 Weeks

Leveraging AssemblyAI’s AI models, Aloware was able to ship Smart Transcription to its customers in just 6 weeks. Now, each call Aloware’s customers receive is transcribed automatically and at near human-level accuracy.

The AssemblyAI Conformer-2 transcription model, for example, demonstrates a 31.7% improvement on alphanumerics (number sequences like credit cards), a 6.8% improvement on proper noun error rate, and a 12% improvement in robustness to noise compared to Conformer-1.

Read more about the results customers are seeing with Conformer-2

For Aloware’s customers, this means call reviews and tedious tasks like QA can be expedited. The automation also reduces the potential for human error in the process, significantly increasing the accuracy and utility of the analysis.

AssemblyAI also offers a suite of AI models for Audio intelligence, in addition to a newly released framework for LLMs, LeMUR, that helps product teams build high-ROI tools on top of audio data.

For Aloware, these models–particularly Auto Chapters and Sentiment Analysis–enabled its team to fill key gaps in its current offering. The product team was able to build new AI-powered tools that help their customers gain insights into customer sentiment, sales representative performance, and call analysis to improve the customer experience.

“The accuracy was strong,” explains Nathan Webb, Senior Product Manager at Aloware. But the “great documentation and unique models like Auto Chapters and Sentiment Analysis is what really won us over,” he continues.

Auto Chapters is a Text Summarization model that automatically surfaces key highlights and summaries from audio and video streams. The Auto Chapters model works by first segmenting the audio/video stream into logical, time-stamped chapters, or points where the topic of conversation naturally changes. The model then generates a short summary for each of these chapters. The result is similar to what YouTube displays beneath videos when automatic chapters are enabled.

For Aloware, the Auto Chapters model speeds up Quality Assurance (QA) by making call transcripts easier to digest and process.

“Auto Chapters is especially helpful to customers looking to quickly and intelligently perform Quality QA on their recorded calls,” Webb explains.

AssemblyAI’s Sentiment Analysis model detects and labels:

  • Positive sentiments
  • Negative sentiments
  • Neutral sentiments

Sentiment Analysis is useful for tracking customer opinions and attitudes across various locations, time zones, products, support agents, and more.

Aloware also liked that these AI models came from a single provider, condensing their AI stack and making their smart tools easier and faster to build and deliver to their customers. In addition, Webb explains that AssemblyAI’s demonstrated commitment to continuous model and feature improvement through its AI research was a big deciding factor to go with the startup’s services.

Integrating AI into Contact Centers

Aloware has been thrilled with the accurate transcription and AI features it can now offer customers with AssemblyAI’s state-of-the-art AI models.

In addition, working with AssemblyAI has gone smoothly, says Webb: “The ongoing support has been strong and AssemblyAI continues to act like real partners, not just vendors.”

Aloware’s results have been just as impressive. “AssemblyAI is the first true Machine Learning feature we have developed and provided to our customers,” explains Webb. “It saves our customers hours of call listening on lengthy calls. Moreover, the tool has opened a new world of unforeseen insights and performance tracking for call reviews. Customers consistently tell me that this is one of the coolest things that Aloware has ever built,” he continues.

For example, Aloware has seen customers like JobNimbus increase lead-to-close rate by 27% with the addition of these AI-powered tools.

What’s next for Aloware? “In the immediate future, our team is developing aggregated reporting for managers to quickly view agent call performance,” says Webb. “In the longer term, we want to use AssemblyAI to provide in-moment notifications for relevant poor call quality. There may be more exciting features on the horizon.”

These improvements will continue to positively impact the bottom line for Aloware’s customers.