Sayint.ai: Enter Conversational Analytics to Uncover Valuable Insights
Prasad Wagle, Engineering Manager
In order to improve customer experiences and business processes, organizations are increasingly turning to speech and text analysis to leverage the unstructured text held in openended survey questions, contact center notes, social media posts, and other sources of feedback data. Similarly, speech analytics technology provides significant features like speech engine, mood detection, speaker isolation, query engine, search engine, analysis and indexing, dashboards and reports, which deliver real-time analytics to the customer for instant decision making. While this segment slowly catches pace, Hyderabad headquartered, Sayint is working its way up to the peak of this rising phenomenon. Sayint provides analytics and insights from customer interactions to companies looking to optimize their workforce, manage their customer experience, analyse performance or improve their compliance with regulations. Sayint’s expertise lies in analysing conversations across a variety of mediums, including phone calls, chats, text messages, video chat, social media and in person conversations. The company customizes data analysis and collection for every client, leading to useful insights such as upsell opportunities or performance reviews. This in turn allows businesses to make better training and hiring decisions, reduce compliance risk, optimize processes, and make better management decisions based on this new available data.
Tailor-made Cost Effective Model
Sayint’s ability to customize their offering to each customer at an affordable price proposes a new perspective to the speech and text analytics market. As vendors recognize and deliver speech and text analytics solutions and functionality either, embedded in, integrated with, or tied to other applications, end stakeholders still struggle to cope with the price quotes and business specific requisites. Sayint is looking to morph the industry by taking a more customer-oriented approach wherein the cus¬tomer’s specific requirements are met through tailor made solutions. Moreover, this value is delivered in a cost-effective manner because of the distributed, pluggable and the open source architecture of the solution. However, an obvious challenge in speech processing technology is the diverse set of accents,
languages, domain/industry and cultural impact on the speech. This diversity impacts the accuracy of speech recognition and hence the overall analytics. To solve this problem, Sayint deploys tailor-made speech and language models for every customer. These customized models deliver accuracy rates of close to 90 percent, which helps draw out more accurate conversation insights.
Manoj kanumuri, Founder& CEO
Given the magnitude of time and resources that goes into a supervised approach to model training, Sayint chooses to go with semi-supervised model training, wherein the company seeks to bootstrap its training data set with various AI advancements. Besides, for the call center industry, more than the availability of data the challenge lies around labelling of data and identifying conversational patterns and insights specific to the customer. Sayint’s preference of semi-supervised learning approaches helps it to improve the labelling efficiencies. “We have also developed tools that aide in identifying new patterns and insights from conversations”, adds Manoj Kanumuri, CEO, Sayint. Some of Sayint’s other AI based areas of innovation include Online Realtime keyword spotting, Call flow analysis, and automated Call/segment Classification etc.
In the near future, Sayint is objectively working to expand its footprint to cater to non-call center scenarios as well, for instance, Face to Face conversations.
Manoj kanumuri, Founder& CEO
Given the magnitude of time and resources that goes into a supervised approach to model training, Sayint chooses to go with semi-supervised model training, wherein the company seeks to bootstrap its training data set with various AI advancements. Besides, for the call center industry, more than the availability of data the challenge lies around labelling of data and identifying conversational patterns and insights specific to the customer. Sayint’s preference of semi-supervised learning approaches helps it to improve the labelling efficiencies. “We have also developed tools that aide in identifying new patterns and insights from conversations”, adds Manoj Kanumuri, CEO, Sayint. Some of Sayint’s other AI based areas of innovation include Online Realtime keyword spotting, Call flow analysis, and automated Call/segment Classification etc.
Sayint’s ability to customize their offering to each customer at an affordable price proposes a new perspective to the speech and text analytics market
In the near future, Sayint is objectively working to expand its footprint to cater to non-call center scenarios as well, for instance, Face to Face conversations.
