Conversation intelligence isn’t just indexing or keyword matching, nor is it merely paraphrasing engines, transcript analysis, or rudimentary chatbots. These could be considered part of conversation intelligence, but the whole is much greater than the sum of its parts.

At, you can essentially add a layer of AI ML on top of multi-modal, unstructured human communication, enabling you to perform and manage key actions without needing to build and scale your own ML model. These actions include generating automated transcripts, recognizing elements within the conversation, performing analytics, and combining all of that into an actionable pipeline. This type of holistic conversation intelligence system can offer your company invaluable insights that you couldn’t have hoped to gain even six or seven years ago.

Below is a non-exhaustive list of’s conversation intelligence capabilities:

  • Leverage speech-to-text to generate transcripts with speaker diarization, even in real time.
  • Recognize topics in conversations as well as topic hierarchies.
  • Perform sentiment analysis.
  • Recognize and suggest action items and even follow-ups.
  • Identify questions.
  • Recognize the use for and supply trackers.
  • Identify conversation groups.
  • Perform conversation analytics.

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So what would an actionable pipeline look like if you’re using Imagine you have a meeting that a key decision-maker can’t attend. You can let

  1. Run real-time transcription.
  2. Generate an interface where you can view the transcript (as well as who’s speaking), the topics discussed, any questions raised, and any action points and follow-ups required — among other things.
  3. Send all this annotated, neatly categorized information in a fully-packaged bundle.

Better yet, with asynchronous and batch processing, you can always pull up a transcript, an audio recording, or even a video call and do the same thing almost instantly. Additionally, with the right application programming interface (API), you can plug in these AI ML capabilities and automate workflows. Following the above example, you can forward action items or follow-ups directly into Trello or Jira, for instance, as tasks to be completed.

How Conversation Intelligence Works

At a fundamental level, conversation intelligence at is straightforward. After plugging in via an API or software development toolkit (SDK), you send recorded conversation data or feed real-time information into the system, and you extract the conversation intelligence you require.

This simplicity, however, belies complex programmable layers stacked one another. In the foundational layer, there’s natural language processing (NLP) and speech recognition, as well as accompanying approaches to formatting and ensuring accuracy. Next are the extraction and abstraction of insights from the recorded conversations. Finally, there’s more advanced domain intelligence that can help developers directly build on the data extracted.

And the speed, scale, and effectiveness of all of this are underpinned by AI and machine learning algorithms. The staple neural networks that power modern-day NLP underwent significant growth in 2010, then again in 2014 and 2015. Finally, a definitive breakthrough in 2016 occurred in the field of machine translation. Since then, technologies like NLP have supercharged conversation intelligence as a practical application of machine learning.

Benefits of Conversation Intelligence

While that all sounds very impressive on paper, there are real, ongoing use cases that illustrate how conversation intelligence can benefit your team and give you an insight into the technology that underpins it.

Optimize Sales Conversations

Conversation intelligence can surface insights and recommend actions to your sales reps to not only close deals faster but also increase average deal size. Sales reps can even make use of real-time coaching during calls with customers. Additionally, some of the more recent innovations in sales involve advanced chatbots that automate structured conversations.

Improve the Customer Experience

Some of the first examples of speech-to-text productization were in contact centers. To this day, real-time agent assistance and coaching use conversational intelligence to improve agent handling and issue resolution times in contact centers — all of which directly impact customer experience and customer loyalty.

Save Time Qualifying Leads

The impressive indexing, sentiment analysis, and conversation analytics capabilities of conversation intelligence can significantly reduce the time it takes to qualify leads. Sentiment analysis can show your sales teams which leads are primed. Identified topics, action items, and follow-ups can direct your pitches by automatically recognizing key opportunities for pushing sales — even at scale.

Enhance Remote Collaboration

Conversation intelligence fosters enhanced collaboration both in real time and asynchronously, in scope and at scale. It can automatically generate even real time transcripts, process batches of meeting minutes and other documentation, and analyze multi-modal, unstructured human conversations. It can then present structured insight or forward the data to automate workflows that support teamwork.

Increase ROI Through Various Function and Process Improvements

Conversation intelligence clearly provides a number of function and process improvements. All put together, these directly affect your bottom line from the perspective of increasing breadth of scale and depth of effort — or typically both — depending on how it’s applied.

Common Challenges of Conversation Intelligence

At, we use highly customized conversation intelligence solutions to mitigate some of the common challenges faced by conversation intelligence:

  • Technical limitations in technology — Some key examples of hard boundaries when it comes to conversation intelligence, include the difficulty to parse complex human language and maintaining accuracy in sentiment analysis. Interestingly, both of these issues stem from similar factors: linguistic elements like intonation, jargon, and accents, among other things. Fortunately, ML researchers around the world are continuously improving these technical capabilities.
  • Developing a downstream, task-specific application — You’ll need to program a task-specific downstream application from a generic ML model, which is too high a hurdle for many companies. addresses this concern by letting you use an extensive collection of prepared APIs — or you can DIY with SDKs. You can literally add Symbl’s ML layer on top of your data so you won’t need to program your own downstream application.
  • Unstructured, unscripted conversations — In the same way templated chatbots and interactive voice response systems can only address scripted concerns, generic conversational intelligence can only process structured data. One of the critical technological leaps made by AI ML was the ability to parse unstructured, and even multi-modal data sources. This is what does.

It’s true that conversion intelligence is an extremely complex (and constantly evolving) technology that is well outside the scope of most organizations’ abilities to develop in-house. That’s why addresses these challenges and removes the major barriers to entry.

Examples of Conversation Intelligence 

We’ve successfully deployed conversation intelligence at to provide our clients with solutions tailored to their individual needs. There are many ways you can translate this success to your own organization. 

For example, to bolster revenue and support sales enablement, conversation intelligence can:

  • Offer key conversation drivers during real-time calls.
  • Analyze call behavior and perform agent coaching in real-time or asynchronously.
  • Analyze trends and reveal insights by crunching thousands of calls.

To increase productivity in remote collaboration platforms, conversation intelligence works to:

To build supercharged recruiting processes and gather better HR intelligence, conversation intelligence works to:

And these are just three use cases. Practically every part of every vertical can leverage conversation intelligence to solve unique challenges.

How Can Help is a comprehensive, domain-agnostic platform of conversation intelligence that makes it easy for developers to support use cases like:

  • Real-time agent coaching
  • Compliance and moderation
  • Search and indexing
  • Accessibility and live captions
  • Meeting notes and summarization

The developer-friendly platform doesn’t require extensive data training or manual labeling, which reduces the time needed to implement solutions at scale.
If you want to learn how your team can make use of’s powerful conversation intelligence capabilities, you can request a demo today.

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Surbhi Rathore

Surbhi is co-founder and CEO of, a technology that makes it simple to deploy contextual AI and analytics across voice, text and video communication, for any stage software. Symbl is now a Series A startup with $24M in venture financing and 70+ team members across the globe.