October 2022 Summaries

The insurance industry is facing significant challenges in delivering positive customer experiences, with many companies struggling to understand their customers' needs and desires, develop omnichannel sales processes, use IoT for personalization, build user-friendly apps and websites, and improve the claims process. To address these challenges, insurers are taking steps such as gathering intelligence through AI-powered tools, building knowledge bases for self-service resources, and using conversation AI to deliver personalized experiences. With 90% of insurers having a Chief CX or CCO to drive customer success, it is crucial for companies to prioritize customer experience and implement strategies that meet the evolving demands of their customers.

Symbl.ai has been recognized with the 2022 AI TechAward for Natural Language Processing (NLP) Technology, a prestigious award that acknowledges innovation in NLP technology. The platform's API allows developers and businesses to improve their conversation analysis and intelligence extraction capabilities. Symbl.ai will be presenting at AI DevWorld 2022 on October 25, where it will also attend API World 2022, the world's largest API and microservices event, alongside thousands of global technical leaders and industry experts. The award is part of the 20 categories celebrated by the 2022 AI TechAwards, which recognize technical innovation, adoption, and reception in the AI and machine learning industry.

The developers of the Symbl.ai platform have released a beta version of their Go SDK for initial evaluation by the community. The release provides significant functionality, but it's still considered beta due to some work left to be done. The main reason for creating a Go SDK is to enable processing conversations on backend servers that can scale out in enterprise applications, which will transform the conversation and voice industry. The SDK introduces an asynchronous API that allows developers to interact with the platform without blocking each other, making it suitable for edge devices and headless devices. The Async API provides examples of how to post a file to the Symbl.ai platform, manage trackers, and obtain topics from audio files. A second blog post will cover the Streaming API in more detail.

Modern speech-to-text software can not only transcribe audio, but also organize and manage that audio content, and add conversation intelligence to mine data for contextual insights. Using conversation AI, businesses gain access to high-quality speech-to-text services capable of performing state-of-the-art transcriptions for both audio and video conversations. Transcription features include Symbl.ai's Transcription Plus, which enables developers to use accurate speech-to-text capabilities across many use cases and platforms with no upfront training or custom models needed. Real-time transcription requires a keen ear and attention to detail, but enhanced AI can improve accuracy when combined with factors such as clear audio and sentence boundary detection. Asynchronous transcription allows organizations to access untapped value from recorded files. The limitations of speech-to-text include missing contextual details without the AI component analyzing conversations. Symbl.ai's conversation intelligence platform empowers businesses to truly understand and extract insights from human conversations at scale, offering features such as streaming API, speaker diarization, custom vocabulary, and multi-language support. By choosing a pre-built solution like Symbl.ai, businesses can add capabilities without spending time training models.

The key to creating personalized and differentiated customer experiences is finding ways to capture data that empower brands to measure performance and identify opportunities for improvement. Traditional methods of gathering customer feedback, such as surveys, are limited, reactive, ambiguous, unfocused, and non-transparent, whereas real-time conversation intelligence can provide deep, granular insights in real time. This enables businesses to act in the moment while customers have their attention, improve collaboration between sales, support, and product teams, enhance products or services based on customer feedback, create better communication experiences using real-time analytics and AI assistance, and ultimately drive business growth by winning over competitors.

AWS Lambda is a serverless tool offered by Amazon Web Services (AWS) that allows developers to call APIs, integrate with external services, serve static and dynamic websites, and process data without having to manage servers or scale infrastructure. Google Cloud APIs provide access to various services on the Google Cloud Platform (GCP), including machine learning products, which can be integrated with AWS Lambda to take advantage of the best features of both systems. Integrating AWS Lambda with Google Cloud APIs enables developers to use GCP's powerful APIs for machine-learning tasks, such as optical character recognition, translation, and developing customer service chatbots. However, this integration also presents challenges, including cross-origin resource sharing issues, complex debugging experiences, data overload, and the need to set up monitoring tools to debug Lambda functions. To overcome these challenges, developers can follow a step-by-step guide to integrate AWS Lambda with Google Cloud APIs, which includes setting up accounts, configuring credentials, packaging code, and deploying Lambda functions. Alternatively, Symbl.ai's Asynchronous Text API can provide a straightforward solution for analyzing conversational data and generating transcripts or summaries without requiring complex integrations.

Kira Hunter, the new Marketing Content Manager at Symbl.ai, is excited to share her experience with developers and others about Symbl.ai's wide range of voice AI solutions and conversation intelligence. She has pivoted into the tech world after writing and marketing content for a B2B natural products industry trade shows company, appreciating simplicity, accessibility, and solution-oriented articles and videos. To get up to speed on the Summarization API, Kira took the Summarization API Challenge, reviewing Symbl.ai's documentation pages, downloading Postman, and generating an authentication token. She then successfully implemented the API, pushing a video of a recorded meeting through the Async API and receiving a summarized conversation almost instantly. The summary included key moments from the conversation, such as topics discussed and decisions made, with options to copy and paste specific conversation IDs to return granular pieces of the conversation. Kira hopes to continue sharing her experience and knowledge about Symbl.ai's product offerings in future blog posts.

The business process outsourcing (BPO) industry is evolving to prioritize customer experience, with emerging trends including customer-centricity, omnichannel strategies, and real-time communication. To enhance CX, BPOs can leverage artificial intelligence (AI) tools that provide personalized recommendations, support real-time decision-making, and analyze customer behaviors and patterns. AI-powered chatbots can also be used as filters to drive leads to sales and support teams, while conversation AI tools offer real-time insights into phone and chat communications, sentiment analysis, and emotionally intelligent CX. By integrating these technologies, BPOs can improve customer satisfaction, increase conversion rates, and stand apart from competitors.

Machine learning drift refers to the degradation of a model's performance over time, which can be caused by changes in user behavior, biased data, or inaccurate representations of actual data. There are two main types of model drift: concept drift, where input data hasn't changed but user behavior has, and data drift, where the properties of input or output data have changed. Detecting drift is crucial, and methods such as the Kolmogorov-Smirnov test and population stability index can be used to identify changes in the model's performance. To mitigate drift, companies should monitor their models, ensure consistent training and test data, retrain and redeploy models when necessary, implement data quality checks, and develop statistical metrics to track performance. By implementing these mechanisms, companies can curtail drift and improve the overall accuracy of their machine learning models.