We are in a world where immediate, personalized support is not just desired but expected. Providing fast and personalized assistance has always been important across various industries and especially became critical in the realms of sales and customer support where the stakes are high and every interaction counts. 

Sales representatives are often in high-stakes situations where they need to make quick decisions, access product details, or respond to customer queries on the fly. Any delay or inaccuracy can make prospects lose credibility and make or break a deal losing a potential customer. Meanwhile, in customer support, long wait times and impersonal interactions can lead to customer dissatisfaction and lost opportunities for upselling or retention.

What if you could eliminate these bottlenecks and elevate your sales and support experiences to new heights?

Here is Symbl.ai’s Real-Time Assist! This isn’t just another customer service tool; it’s an automated assistant designed to understand your specific needs—such as immediate answers to queries, real-time guidance during tasks, and efficient customer service—and provide tailored assistance in real-time.

The Technology Behind Real-Time Assist

Symbl.ai’s Real-Time is powered by Generative AI with Nebula LLM along with Web SDK and Trackers. From streaming conversations to indexing your knowledge base, Real-Time Assist is built to provide the most accurate and timely assistance.

How Does It Work?

Index the Knowledge Base

Break Down Documents: Your knowledge base may contain a wide range of topics. Break these down into smaller, meaningful chunks.

Vectorize Text: Use Symbl.ai’s Nebula Embedding API to convert these text chunks into vectors via the Nebula embedding model.

Data Storage: Store these indexed vectors and their associated content in a datastore for retrieval based on triggers.

For more on embeddings, check out the Embedding API guide.

Configure the Triggers

Automatic Detection: Symbl.ai detects questions and trackers during an ongoing conversation between the Customer Service Agent (CSA) and the customer.

Customization: Trackers can be configured by customer success managers to identify phrases like ‘competitor mentions’ or ‘overcharge’.

Out-of-the-Box Trackers: Symbl.ai provides 40 default trackers, both general and specific to contact centers.

For more on trackers, see the Trackers guide.

Stream the Conversation to Symbl.ai

Bi-Directional Stream: Use Symbl.ai’s Web SDK to stream the conversation and display knowledge base results to the CSA.

SDK Installation: Install the Web SDK with a simple npm command and import the latest version.

Event Identification: During the support conversation, when a question or tracker is identified, events are triggered along with the callback response object.

For more on Web SDK implementation, see the Web SDK reference.

Core Features:

Instant Feedback: Get immediate responses to your queries.

Contextual Assistance: Receive support that understands the context of your needs.

Problems Solved by Real-Time Assist:

User Friction: No more searching for help. Real-Time Assist is there when you need it.

Support Efficiency: Complete tasks faster with real-time guidance.

User Experience: Feel understood and supported, enhancing overall satisfaction.

Support Costs: Reduce the need for human intervention, saving on support costs.

Real-Time Assist in Action: Use Cases

For Sales Teams:

Instant Information Access: Get product details, pricing, and competitor information at your fingertips.

Reduced Response Time: Let the AI handle initial queries, freeing you to focus on closing deals.

For Customer Support Teams:

Knowledge Base Access: Instantly pull up articles or solutions, improving first-call resolution rates.

Scripting Assistance: Get AI-suggested scripts based on customer queries.

Compliance Monitoring: Ensure all conversations adhere to industry regulations.

Why Choose Real-Time Assist?

User Retention: Keep your customers coming back with an unmatched user experience.

Increased Revenue: Convert more leads and prospects with streamlined processes and instant access to right data..

Data-Driven Insights: Make informed decisions with valuable user data.

Scalability: Easily scale to accommodate a growing user base.

How Does It Work?

As interactions between customers and representatives unfold, Real-Time Assist identifies questions, topics, and pre-set markers—such as “payment issues” or “technical support”—that serve as triggers during the conversation.

Utilizing the Nebula Embedding API, these triggers are transformed into vectors, which are then matched against a pre-existing vector database from your knowledge base to find contextual similarities. Once a match is found, the associated content is sent to Nebula LLM. Nebula then synthesizes this information to generate the most relevant and accurate response based on the identified trigger.

This Real-Time, AI-generated guidance is then sent to your backend server via Web SDK and displayed directly on the representative’s dashboard, ensuring that they have the best possible information at their fingertips, exactly when they need it.

Interested to implement Real-Time Assist for your teams? Here is the step-by-step “How-To” guide for you.

The post Real-Time Assist with Generative AI: Powered by Nebula LLM  appeared first on Symbl.ai.

1. AI-generated sales content to support entire sales cycle

The sales cycle often involves a number of conversations and substantial amounts of content generation, from email templates and sales scripts to marketing collateral and case studies to personalized offers and services. Creating this content can be time-consuming and resource-intensive, especially when attempting to tailor it to specific clients or industries.

Generative AI can streamline this process by creating personalized, targeted content quickly and efficiently, and identifying the right moments to use it for better sales engagement and outcomes. By inputting a few key parameters or prompts, sales teams can use LLMs to generate a wide array of content across the sales cycle, including:

• Customized email templates that address clients’ unique pain points and interests.
• Sales scripts that weave in relevant industry trends and challenges – or personalized content.
• Marketing collateral that showcases the specific benefits of your product or service for a given client.
• Case studies that demonstrate how your solution has helped similar organizations overcome obstacles.

By using AI-generated conversation intelligence and content, sales teams can save time, reduce the burden on content creators, and focus on building closer relationships with clients and prospects.

2. Building personalized sales coaching plans based on ongoing conversations

Sales coaching is an essential aspect of sales team development and success, as it helps to identify areas for improvement and fine-tune sales strategies. Generative AI can play a significant role in developing personalized coaching plans by analyzing ongoing conversations between sales reps and clients or prospects.

By using LLMs to process the text or audio data from these conversations, organizations can gain valuable insights into:

• Communication styles and techniques that resonate with clients.
• Common objections and concerns that arise during the sales process.
• The most effective ways to address these objections and concerns.

Armed with this information, sales managers can create coaching plans tailored to each rep’s strengths and weaknesses, thereby improving overall sales performance.

3. Onboarding, real-time support, and continued training to make your sales team your product experts

A well-rounded sales team needs more than just technical product knowledge; they must also be skilled in presenting the product, handling objections, and building relationships. Generative AI can help organizations create comprehensive onboarding and training programs that empower sales reps to become true product evangelists. This includes:

• Onboarding training: Generative AI can be used to create customized training materials that cover the product, industry, and target markets. By using LLMs to generate training content based on the rep’s role, experience, and learning preferences, organizations can ensure that their sales teams hit the ground running.
• Real-time support: Sales reps often face unexpected questions or objections during client interactions. Generative AI can provide real-time support by quickly generating accurate, persuasive responses based on the context of the conversation. This ensures that sales reps are equipped to handle any situation that arises and can maintain momentum in the sales process.
• Continued product training: As your product evolves, it’s essential to keep your sales team up-to-date on new features and use cases. Generative AI can help facilitate this ongoing training by generating relevant, easy-to-understand content that keeps your team informed and engaged.

Choosing Between Using Existing LLMs and Building Your Own

Deciding whether to use an existing large language model or create an internal LLM strategy depends on factors such as resources, time to market, and the level of customization and control required. Some criteria to consider include:

• Cost: Building your own LLM can be expensive, both in terms of the computational resources required and the expertise needed to develop, train, and maintain the model. Using an existing LLM can be more cost-effective, especially for smaller organizations or those with limited AI expertise.
• Time to market: Developing and fine-tuning a custom LLM can be a lengthy process. If speed is a priority, utilizing an existing LLM may be the best option, as it allows you to quickly integrate the technology into your sales processes and start reaping the benefits.
• Customization: While existing LLMs are highly versatile and can be fine-tuned to some extent, building your own LLM allows for greater customization to meet your organization’s specific needs. If your sales processes or product offerings are highly specialized, a custom LLM may provide better results.
• Control: Building your own LLM affords you more control over the data used for training, which can be important for organizations operating in highly regulated industries or those with strict data privacy requirements. Using an existing LLM may require sharing your data with third-party providers, which could pose risks.
• Scalability: As your organization grows and your sales processes evolve, you may require an LLM that can scale accordingly. While existing LLMs can handle a wide range of tasks, building your own LLM may offer greater scalability and the ability to adapt to your organization’s changing needs.
• Integration: Utilizing an existing LLM may require less integration effort, as many providers offer APIs and other tools that allow for seamless integration with your existing sales and CRM systems. Building your own LLM may necessitate more extensive integration work, which could impact the overall cost and time to market.
• Support and maintenance: Maintaining and updating an LLM is an ongoing task. If you build your own LLM, your organization will need to allocate resources for this purpose. Using an existing LLM typically comes with support and maintenance from the provider, which can help ensure the model remains effective and up-to-date.

Ultimately, the decision to use an existing LLM or build your own will depend on your organization’s specific needs, resources, and goals. By carefully considering the factors outlined above, you can make an informed choice that sets your sales team up for success.

Conclusion

Generative AI has the potential to revolutionize the world of sales by providing valuable support throughout the sales cycle, generating sales intelligence and content, fostering personalized coaching plans, and enabling comprehensive onboarding and training programs. Whether you choose to use an existing LLM or build your own custom model, the benefits of leveraging AI to make your sales team smarter and more effective are clear. By embracing this technology, organizations can transform their sales teams into highly knowledgeable product evangelists that know how to effectively engage with customers, driving growth and success in the increasingly competitive sales landscape.

If you’d like to learn more, check out how Symbl.ai is helping Salesroom empower sales teams with real-time coaching and buyer engagement analysis in this recent blog post.

The post How Generative AI Makes Sales Reps Smarter? appeared first on Symbl.ai.

We’re exploring ChatGPT‘s capabilities and discovering integration opportunities with the Symbl.ai platform that will enhance your application’s ability to understand conversations and demonstrate how complementary these two platforms can be.

So, during the April meeting we plan on:

• Reviewing the high-level basics of ChatGPT for foundation
• Dissecting OpenAI’s APIs and SDKs
• Offering up some design strategies for combining the two platforms
• Exploring why these platforms naturally lend themselves to generating feedback and reinforcement training

After getting through all of this material, we’ll also go through a demo (yes! A live demo!) that implements this possible design in code. You will find this demo quite helpful for when you’re going through the process of building your own Symbl.ai and ChatGPT integration.

This month’s meetings will be taking place at the following times (please note the time change for Asia-Pacific):

• Americas – Wednesday, April 26th at 11 a.m. PST (Convert to your Timezone)
• Asia-Pacific – Thursday, April 27th at 8 a.m. IST (Convert to your Timezone)

If you’re interested in learning how these two great platforms unlock some pretty amazing use cases, I encourage you to stop by because this is a Community Meeting you don’t want to miss. This meeting is for you, so feel free to bring any questions you might have!

What is the best way to catch the Community Meeting, you ask?

That’s easy! Just…

• Join the Symbl.ai Google Group
• Add the Community Calendar to your Google Calendar
• Then if you have topics for discussion, add them to Agenda Doc

It’s as simple as that.

What is ChatGPT?

ChatGPT is a natural language processing tool driven by AI technology that allows you to have human-like conversations and much more with a chatbot. The ChatGPT language model can answer questions and assist you with tasks including composing emails, essays, and code.

To say there has been a lot of hype behind ChatGPT would be a massive understatement. OpenAi’s user base went from practically zero to 100 million ACTIVE users in the two months the platform was open to the public. To put this into perspective, it’s the fastest-growing application ever created in the history of applications. People are touting that ChatGPT has the potential to revolutionize how humans engage with technology and communicate.

Having used the platform extensively by now, I don’t disagree with that statement. However, the real magic will happen when people start to integrate ChatGPT into existing applications. One great example is Microsoft integrating it into its search engine, Bing. Microsoft has nothing to lose in this venture because Bing barely registers as a blip on the search engine radar, but if I were Google, I personally would be shaking in my boots right about now. These are the kinds of powerful integrations that I’m talking about.

Join us, friends!

Our Community Meetings hit upon some pretty hot topics and are chock-full of vital information. If you want to chat beforehand or if you have any discussion ideas for future Community Meetings, drop by our Slack channel and let us know.

Until then, I look forward to seeing you soon!

The post Understand Your Business Better with ChatGPT and Symbl.ai Platform Integration appeared first on Symbl.ai.

The Call and Contact Center Expo showcases the latest tools, products, and solutions for customer experience and support as well as advancements and developing strategies in telecommunications, blockchain, cyber, and smart security solutions, and many other key factors in managing a call center or customer engagement business or department. 

Artificial intelligence is revolutionizing the way we manage and run contact centers, bringing performance and efficiency levels higher than ever before. Here at Symb.ai, our team is loaded with CX and contact center expertise and we’re building conversation intelligence solutions for the contact center that are literally changing the game. 

At a high level, Symbl.ai’s human intelligence platform eliminates the bulk of after call work by generating call summaries automatically, creating action item lists and pre-populated call notes for agents, and processing key customer information, such as supporting CRM system of record updates, in a secure and lightning-fast manner. 

We’re also bringing new levels of performance and success to outbound calling with up to 98% accuracy for detecting humans or machines, using AI-powered call progress analysis (CPA) and call intelligence. The result is better business outcomes for outbound calling, including improved customer engagement and increased sales. 

Eliminate after call work

Eliminating after call work is no small accomplishment when it comes to optimizing agent efficiency and increasing productivity. Symbl.ai’s Customer Service Intelligence API delivers an call transcript and summary in real time and delivers pre-populated after call notes to agents that includes key data points, such as important questions and key actions to take next—this saves agents both time and effort and delivers significant reductions in overall average handle times (AHT). . Additionally, contact center leaders can use Symbl.ai call intelligence to automatically keep their CRM systems updated with the latest  customer information as opposed to revising it manually. 

Detect humans or machines with 98% accuracy

The Symbl.ai Communications Intelligence API connects outbound calls with a 98% accuracy rate, meaning that it can identify whether a human or a machine is picking up the phone and either connect the caller to the correct agent or leave a message at exactly the right time. With AI classifying each call and determining the correct course of action, agents have a higher chance of landing their offers and, more importantly, speaking to decision makers. 

Where to find Symbl.ai at the Call and Contact Center Expo

Come visit booth 4020 and our experts show you what’s possible using Symbl.ai in the contact center, including nd demos and tips on how Symbl.ai’s Human Intelligence Platform can take your call and contact center operations to the next level. 

We hope to see you there!

The post Symbl.ai at Call and Contact Center Expo 2023: We Make Your Agents Smarter appeared first on Symbl.ai.

The first video covers Symbl.ai’s Transcription feature; transcription is simply the process of converting speech into text. In the video, we discuss what transcription is, its common use cases, and how it can be applied in real-world scenarios. We also take a deep dive into the feature’s API and demonstrate how to use it in code via our SDKs.

The second video digs into Redaction, which is the process of removing sensitive information from a document or other piece of content. This feature is particularly useful for applications that manage personal data, such as healthcare- or finance-related information. In the video, we cover the basics of redaction, including its use cases and how to apply it to specific examples. We also show how to use the feature in code via our SDKs.

We believe that these videos will be extremely useful for anyone who wants to use transcription or redaction in their applications. Whether you’re building a healthcare app that needs to redact sensitive patient information or a customer service tool that requires transcription, these videos will give you the knowledge and skills you need to get started!

The upcoming chapters in the video training series are Topics, Questions, Follow-Ups, and Action Items. We’re committed to providing you with the best possible resources to help you succeed, so please don’t hesitate to let us know how we can support you.

We hope you enjoy these new videos. Cheers!

The post Symbl.ai Training Series Expands with Videos on Redaction, Transcription appeared first on Symbl.ai.

Symbl.ai is a conversation analytics platform that helps you understand and improve customer conversations. Our platform can be used to analyze conversations ranging from online meetings to video conferencing to support calls. You will then gain access to insights that will help you make better decisions, improve the customer experience, and anticipate customer needs.

Conversation analytics is a powerful tool for businesses of all sizes. By understanding the ways that customers interact with your business, you can not only better understand the customer experience, but underlying customer needs as well. This can help you identify underperforming areas and improve upon your customer service strategies.

In this video training series, we will go over the basics of the platform, how it works, and how you can use it to enhance your customer conversations. We’ll also look at some best practices and strategies to optimize your use of the platform.

I plan to have a new training video released each week covering a wide variety of topics. We hope you find this series useful and look forward to helping you make the most of Symbl.ai!

The post Introducing the Symbl.ai Video Training Series: Learn How to Unlock the Conversation Intelligence Platform’s Capabilities appeared first on Symbl.ai.

CHALLENGE

Salesroom Co-Founder and CEO Roy Solomon noticed a glaring market opportunity for Meeting Intelligence software that gives sellers the ability to course correct in real time as they’re talking to customers during video sales meetings. With salespeople typically having a mere 5% of a customer’s time during the entire sales cycle, making the most intelligent and human connections on digital sales calls is more critical than ever.

When salespeople are made smarter in real time by receiving guidance on what to say and how buyers may be feeling—e.g., is the customer expressing disappointment? Are they mentioning competitors? Is the decision maker asking questions and seemingly engaged? Is the salesperson coming across negatively?—they can make deeper human connections with buyers and develop more meaningful relationships.

SOLUTION

Salesroom uses Symbl.ai’s AI-powered transcription, summarization, topic identification, and trackers features to provide salespeople with real-time signals, context, and conversation insights during meetings. According to Solomon, it’s the real-time, AI-driven component of the solution that makes it powerful.

“I think that transcription is getting commoditized,” Solomon explains, “It’s what you do with the transcription that matters now, and the combination of AI and real-time actions enables companies to overcome the loss of in-person sales engagements by helping buyers and sellers stay more engaged during video meetings.” 

The Salesroom platform brings sales playbooks directly into meetings with conversational and sales intelligence delivered in app, it calculates buyer engagement scores and next best actions, presents coaching cards in real time, and generates sales meeting summaries and video clips of key moments to save time doing after call work—all of which empowers salespeople to stay present and make more human connections during sales meetings. 

Specifically, the key conversational intelligence capabilities of the solution include:

• Real-time coaching and playbooks “in-app” to improve engagement in sales meetings and conversations, i.e. alerting a salesperson that they are talking too fast and should slow down or stop using the term “like”—all in app, in real time.

• Identifies key moments in sales conversations and actions to take, i.e., if the decision maker goes silent, the platform automatically provides the sales rep with suggested questions to “re-engage” and keep conversations going.

• Enables conversation and sentiment scoring for both buyers and sellers with question detection—if scores go below customer-set thresholds, automated actions can be triggered to save the conversation.

Solomon continues, “We’re using Symbl.ai to detect in a matter of milliseconds what the key moments are in sales conversations in order for sellers to drive better engagement. The platform’s overall engagement score for the call is then updated in real time.” 

Powered by Symbl.ai, the Salesroom platform is also able to flag who’s asking questions so that salespeople can determine if certain decision makers are engaged on the call. Symbl.ai also supports the detection of next steps and customer sentiment. “If we see that the customer sentiment is going down and the engagement score is going down as well, then you have to save the conversation one way or the other. We also will tell the seller in real time if he or she is coming across as negative,” says Solomon. 

RESULT

By teaming up with Symbl.ai, Salesroom is managing thousands of conversations per month with AI-powered sales intelligence and actions provided to sellers in real-time. Benefits to Salesroom customers include:

• Boosted conversion rates by 15%
• Accelerated sales cycles by 20%
• Accelerated onboarding time by 25%

If you’re interested in learning more about Symbl.ai’s capabilities for sales intelligence solutions–or know more about Salesroom–contact us here.

The post Symbl.ai Enables Real-Time Sales Intelligence For Salesroom’s Video Platform appeared first on Symbl.ai.

Many of these enterprise companies are looking to solve problems such as predicting human decision-making, observing patterns related to behavior, and even strategically guiding people toward desired outcomes. My previous blog explained why we care about this. Today, we will delve into the “how” through examples using this Enterprise Reference Implementation repo on GitHub.

Breaking Down the Problem

If we are interested in segmenting conversations, a minimal set of requirements is necessary to achieve a predictive or associative form of conversation analytics.

The minimal set of requirements for higher-order conversation perception are listed below:

• Conversation ingress (i.e. the input)
• Extracting insights from conversations
• Historical conversation and associated insights
• Conversation aggregation and analytics
• Action (i.e. the output or what we hope to achieve)

These topics are all important, and we can (and will in subsequent blog posts) do a deep dive into the nitty-gritty details of each one. For now, it’s essential to look at the ten-thousand-foot views of these requirements to level set on what we are talking about. We’re doing this because some of these topics, at face value, seem trivial but in fact have a great deal of nuance to them.

First, let’s take a look at a block-style architectural diagram.

We Love Architecture Diagrams

Personally, I love architecture diagrams. I like diagrams or pictures of any kind because I’m a visual learner. When discussing software systems and complex platforms, these diagrams are a great way to see all the components are involved and visualize the control and data path for these systems. Let’s look at the architecture for a traditional or simple system for deriving conversation insights, and then compare that with architecture attempting complex conversation aggregation.

Before we begin, this blog will focus on real-time conversation analytics in a streaming-type capacity, even though asynchronous conversation would look reasonably similar in terms of how the conversation data is ingested by the system.

Simplistic Architecture

The architecture diagram below represents how conversation insights are derived today across many applications. The design is simplistic and, more often than not, meets the needs of your typical real-time conversation use cases. Some goals are to coach, prompt individuals, or perform actions based on what is being said in a given conversation.

That last thought, analytics for this specific conversation, is often overlooked frequently (and by coincidence intentionally). Analytics for multiple conversations typically is out of scope. Why is that?

First, the system that does the conversation aggregation over time is complex to build. Second, to offset that design and implementation complexity we fall back on using humans to accomplish complex aggregation. As you might realize, relying on humans to perform this task can lead to inconsistent and error-prone results despite the best efforts to mitigate this using reporting hierarchy, generating reports, providing searchability, spreadsheets, etc. However, it generally gets the job done.

It turns out that these limitations are perfectly OK, because this model is able to address a good portion of in-demand use cases. These include systems such as chatbots, (simple) call center enablement, and applications creating simple triggers—to name just a few. Because these applications are simpler, most of them run client-side and, in turn, that’s the biggest reason one sees so many React, Angular, and Vue SDKs for these CPaaS platforms.

In summation:

• It’s simple to build this type of application
• They are transactional type applications (input mapped to output)
• They offer Point-in-Time conversation analysis
• The conversation insights are usually processed client-side
• The conversations are isolated from other conversations

Conversation Aggregation with Enterprise Scale

If you examine the use cases in the previous architecture, you are looking at extracting conversation insights from a finite point in time. Those conversations are typically more transactional in nature. Let’s look at the call center enablement use case for an internet service provider; these conversations are framed in a very predictable and finite way.

In almost all cases, a customer initiates this transaction via phone, chat, etc., and then contacts the support technician with a particular problem. Let’s use “my internet connection is no longer working” as an example. The customer will provides some details about what they are experiencing, and the technical support staff might ask additional questions to refine remediation steps. Upon completing the conversation, the customer receives the desired output, which is being reconnected to the internet.

For more complex use cases where we are looking to make connections, establish patterns, and aggregate insights over many conversations throughout time, we need to address a critical difference between these two architectures: persisting conversation data. It is a straightforward, logical, and natural step if you want to make connections between what is said in real time and what was said in prior conversations.

This very simple realization has very significant implications. How do we store this conversation data? What kind of data storage do we use? What’s more important, access speed for our insights or association via putting the dots close enough together to aggregate insights intelligently? What mechanisms do we want to use to detect patterns in conversations?

To understand the complexities in this architecture captured via the Enterprise Reference Implementation repo, we must dive into and understand the minimal set of requirements we have been dancing around.

In summation:

• More effort is required to build these types of applications
• Conversations can be aggregated
• One can build applications with a historical conversation context
• One can have more control over the conversation data
• They are better for building scalable conversation applications
• The company’s business rules/logic are pushed into backend server microservices

If you’d like to get more detailed about both of these architectures, watch the informational video below:

Ingress Data for Conversations

This topic is the most trivial aspect of any conversation processing design, but it also happens to be the most incomplete or overlooked element because it seems simple enough. 

These days, we often think of our conversation data coming from an audio stream in a communication platform such as Zoom, Twilio, Vonage, etc. Still, in reality, there are many more forms of real-time conversation sources that we should remember. These include Telephony via Session Initiation Protocol (SIP) and Public Switched Telephone Network (PSTN), team collaboration applications such as Slack, the unending sea of video/chat platforms including Discord, the one friend who uses email like it’s an instant messenger application, and many more.

These data streams feed into this Analyzer component, which effectively takes the conversation that is embedded in these streams and extracts the contextual insights, as well as enacts some pre-processing of discovered insights. It so happens that the Symbl.ai platform plays a huge role in this component, and, because of that role, there are several Symbl.ai SDKs that can assist with ingesting these data streams in the form of Websocket SDKs for Streaming APIs, Telephony SDKs, and even Asynchronous APIs for handling text and other inputs.

Mining Conversation Data

Many complex details are associated with extracting conversation insights from these various real-time forms of communication. This Analyzer component aims to ingest the data, create any additional metadata to associate with these insights, and then save the context to recall later.

There happens to be an excellent platform that does all of the heavy lifting for us. We can extract these conversation insights without having to train models, have expertise in artificial intelligence or machine learning, or require a team of data scientists. Of course, I’m talking about using the real-time streaming capabilities on the Symbl.ai platform.

Some capabilities that would be invaluable to leverage within your application would be:

• Trackers for honing in on topics specific to your business 
• Custom Entity Detection to see how your products, capabilities, and perhaps even feature gates are discussed and utilized
• Conversation Groups, which is a new feature on the Symbl.ai platform, and could be used to process lower-priority batch-style analytics
• Summarization for distilling down larger conversations and creating tiered topics, because this would remove less relevant subjects

The second feature we alluded to with regard to this Analyzer component is being able to save all these insights and metadata. Because this is a vast topic, we will cover it in the module below.

Preserving Conversation Insights

Preserving insights represents the first of two significant pieces of work in this design. In order to aggregate conversation insights from external conversation sources and through historical data, we need to have a method for persisting this data to recall and make associations to conversations happening now.

This requirement naturally lends itself to using some form of database, but what kind? If we are talking about the aggregation of millions or even billions of conversations, we need scalable backend storage. This means that more storage or database nodes can be brought online to expand capacity. Because we are talking about enterprise applications, this form of expansion needs to be completed without interrupting the application’s availability (or performance), without (much) human intervention, and, as always, in as simple a manner as possible.

We want to note that, in terms of the application’s performance, storage is just one aspect of any data storage system. Another equally crucial dimension is access performance. There are different flavors of databases out there offering their unique take or capability for persisting and recalling data. Still, we need to break down the requirements even further to make better storage choices.

Processing millions of conversations at scale necessitates the ability to quickly store (AKA write) contextual insights in order to keep pace with each conversation. The querying or reading of these insights occurs at the same frequency as the database writes them down. This makes sense, because each new insight invokes a write operation to record what has been discovered, and it will also trigger a read operation to see if there are any prior incidents of the newly discovered insight.

Another data access requirement unique to this application and the conversation intelligence domain we are working with is that the data storage platform needs to be able to quickly and efficiently query for relationships between data points. It would make sense to either select a data storage platform that provides users with this capability natively, or build something that farms out the work to reduce the complexity of the search.

Below are several key takeaways from a storage perspective:

• One needs extensible and scalable backend storage that doesn’t impact availability
• Performant data access requires significant read/write access to a ratio roughly that of 50/50
• The ability to query for relationships between data points is a must

Performing Real-time Analytics

The previous section discussed the need to archive conversation insights that can be recalled by real-time conversations happening in the present moment. This section expands on the final, but extremely significant, feature required in this Enterprise Architecture for Conversation Analysis: making associations or defining the relationships between contextual insights. That functionality happens in this Middleware component in the Enterprise Architecture diagram above.

The best way to visualize this Middleware component is via specific use cases. If we go back to our Internet Service Provider scenario, let’s say in this particular conversation a Tracker insight relating to a “flashing light on cable modem” is recognized and triggered by the Symbl.ai Platform. This information, a “flashing light”, isn’t surprising and is probably even expected in this technical support call involving a customer without internet access.

In an application based on our Simple Architecture definition, we could display a popup to the support technician advising the customer to reboot the cable modem by unplugging and plugging the modem back in. However, in an application based on our Enterprise Architecture that considers historical data, we could query to see which other conversations are associated with Tracker insight “flashing light”.

It could be that there has been a large number of conversations that have triggered this Tracker insight from users originating from Long Beach, California, in the past 30 minutes. Situations like this could indicate a local outage, and our application could dispatch a higher-tier support technician to look into the problem and notify the technician speaking with the customer within the application’s user interface that there is a possibility of a general outage in the area.

In the above example, the Tracker insight “flashing light,” or the data itself, wasn’t significant. However, the relationship or association with that particular Tracker to other conversations that had taken place recently is the noteworthy piece of information. That’s the value proposition for this type of application architecture.

As you can see, this Middleware component is deeply tied to what your business cares about. This component, either in code or interfacing with another external system, captures your company’s specific business rules. These business rules can then be used to notify others within the company to take action, create events that you might want to pass along to other software systems, or trigger actions you want to perform directly in this component.

Although there is a generic implementation provided in this Middleware component, the intent of this Enterprise Reference Implementation is only to be just that—a reference. This Middleware component contained in the repo should either, at minimum, be modified to capture your business rules or in practice, be re-implemented to fit your specific business needs.

Should you choose to use this Reference Implementation as a starting point, the interfaces into and out of this Middleware component use an industry-standard system, which means this Middleware component can be implemented in any language your organization has the most expertise with.

Next Up: A Deep Dive into Data Storage for Conversations

This blog post has shown you solid comparisons between a simple design for a conversation application and what an enterprise conversation-based application architecture would look like. The Enterprise Reference Implementation cited in this blog post is open source and open for use to serve as a template, fire off the creative energy to build your own implementation, and even be used as-is with no strings attached.

The next topic in this series will be a deep dive into the storage or archival aspects of the Analyzer component. Although this blog post has been an incredible start to describing the requirements and functionality necessary for this component, there are far more details that are beneficial to discuss. Those learnings will enable others to make highly informed decisions in terms of designing and selecting an intelligent storage platform to meet their needs.

I hope this has been an enlightening discussion. The big takeaway of this article is to understand the purpose of each component within this higher-level block diagram, and be able to extrapolate your own implementation to put the dots of knowledge close enough together to predict and create desired outcomes for your business. Cheers!

The post Everything to Know About Enterprise Reference Implementation for Conversation Aggregation appeared first on Symbl.ai.

We announced the winner on the last day of the VOICE22 conference on Oct. 12, 2022, on the mainstage: S. Deepak Kumar won for his project LiveCaptioner.

A little about S. Deepak Kumar in his own words

“I’m a self-taught coder who loves to do web and app Development stuff. I’ve worked on and completed numerous client projects, including startups, schools/universities, and NGOs. I love to provide the simplest solution to any given problem.

I’m the winner of TCO21 Velo By Wix Hackathon, Common Ninja Virtual Hackathon, Context is Everything Hackathon by Vonage and Symbl.ai, Wix Skill Builder Challenge, and more. I was a Shining Star Member (February 2022 newsletter) of Topcoder.

Topcoder invited me as a special guest on the Topcoder Nation Show #12 (hosted by Luis Millan).

I also love exploring new places and immersing myself in other cultures.”

Deep dive: LiveCaptioner project

LiveCaptioner helps you get real-time captioning, complete transcription, topics, and questions asked during a one-to-one video call/meeting. It’s built using Symbl.ai’s Conversation Intelligence platform and Vonage’s Video APIs, and Kumar used HTML and JavaScript to build this project.

Features of the LiveCaptioner:

• Live captioning is enabled by default and provides a real-time transcription of your audio content during meetings.
• The app allows you to connect with your friends, family, or colleagues with real-time video and audio via the web browser.
• What you talked about during the meeting will all be stored in the chat section and can be exported.
• Topics and questions asked will be stored in a corresponding tab for access.
• After you connect to your device’s microphone, you can mute or unmute when you want.

LiveCaptioner Deep Dive

Kumar developed this app using HTML, CSS, JAVASCRIPT, Vonage Client Web SDK and Symbl.ai’s Web SDK.

To complete this project yourself, you will need to have the following:

• A Symbl.ai account: Sign up on the Symbl.ai Platform for free to gather your credentials (App ID and App Secret).
• A Vonage account: You will need to have a Vonage Video API account, which you can also create for free.

What was the inspiration To Build LiveCaptioner?

Because of trouble understanding accents, Kumar occasionally had difficulty understanding things during video meetings and recognized that many people must also be facing this problem. Sometimes meetings run long, and it can be challenging to follow what happened during the meeting, especially when one is hosting. Reviewing the video or asking others to repeat what has been said can slow down the meeting’s flow.

Kumar wished there was an app that could provide real-time captions while talking and export the whole conversation happening while the meeting takes place (with a timestamp) in the form of text. I came to know about this hackathon and worked on this idea. It was only possible with Vonage and Symbl.ai.

Challenges Kumar faced

Creating a project, deploying it, and adding functionality led Kumar to experience some hurdles. Kumar was aware of Symbl.ai but had never used its API, and he also had little prior knowledge of Vonage. So, Kumar faced several difficulties while building LiveCaptioner. Per Kumar, it was tough to complete the project in the last two days. However, he notes, Vonage and Symbl.ai API documentation is fantastic; it helped Kumar to understand their usage quickly.

Taking an application from an idea to a functioning thing is a multi-step process that Kumar knew nothing about before beginning his journey with this hackathon. In just a few short hours, he taught himself to use Symbl.ai’s Conversation Intelligence platform and Vonage’s Video APIs and used the two of them to build LiveCaptioner.

LiveCaptioner demo

You can find a demo of the LiveCaptioner below:

Congratulations!

Once again, a huge congratulations to S. Deepak Kumar for his winning project LiveCaptioner in the Context is Everything Hackathon by Vonage and Symbl.ai. We look forward to seeing more amazing projects from you in the future!

The post VOICE22 Hackathon Winner S. Deepak Kumar Talks Code appeared first on Symbl.ai.

Enter Symbl.ai’s Bookmarks API—a convenient way to pinpoint the beginning of key phrases or moments within human-to-human conversations and make it simple for everyone on your team to revisit them as well. 

If you are familiar with Symbl.ai’s practical Summary API, you’ll also be happy to know that summaries can be quickly generated for each Bookmark you implement as well. In the case of an interview, the summaries of particular interviewee answers can be shared with others in your organization who have a say regarding who will get the job without them having to A) watch the recording or B) rely on your partial recounting of their answers. 

But that’s just the tip of the iceberg when it comes to Symbl.ai’s Bookmarks API! Think about how helpful this function would be for salespeople to be able to instantly flag when a customer mentioned a competitor, an interesting use case, or a negative reaction to the product. 

Once again, summaries of these moments can then be shared with others along the sales pipeline to shape the overall sales strategy. Additionally, on future calls with these customers, salespeople can pull up previously made bookmarks and their summaries to enhance the overall interaction. 

Imagine a supervisor asking a salesperson for an overview of customer pain points and that salesperson is able to pull up an entire folder of Bookmarks labeled “Pain Points” that are pulled directly from customer conversations—how unbelievably helpful is that?

Another important Bookmarks use case exists for customer experience and support. Relevant moments—for instance, product critiques or other forms of feedback—in customer conversations can be bookmarked and shared with product teams, managers, and support teams. These insights are then easily translatable to new sales opportunities, and they can also be used to highlight much-needed improvements for a given product or service. 

Symbl.ai’s Bookmarks API allows users to not only bookmark parts of a conversation that they find valuable, but to discover others’ bookmarks as well. Unlike Symbl.ai’s Trackers API, which relies on users to determine the larger themes they’d like to track in the conversation as a whole, the Bookmarks API gives customers the opportunity to flag even just a few seconds’ worth of information, label that part of the conversation, then share its summary with others.

Users can bookmark parts of a conversation both in real time and after it has been recorded. Notably, whatever bookmarks are created can also be linked to a user profile, so there is no risk of being confused about who bookmarked which part of a conversation.

The Bookmarks API also perfectly complements Symbl.ai’s other conversation intelligence features—for instance, a sales rep might have a Tracker set for all customer mentions of a particular competitor. With Bookmarks, that sales rep can take things one step further and be able to flag the pain point from earlier in the conversation that led to the mention of the competitor on the fly.

Conversations can be unpredictable, we all know this, so don’t limit yourself to tracking the topics that you foresee. With Bookmarks, you can choose the moments that are most relevant to YOU to summarize and share with others, or revisit on your own at any time. You can get even more up to speed on Bookmarks via our documentation page.

The post How Symbl.ai’s Bookmarks API Makes Data Labeling Key Conversation Points Easy and Shareable appeared first on Symbl.ai.