AiThority Interview with Jayaram Bhat, Co-Founder and CEO at Squelch

Know My Company

How would you describe your interactions with smart technologies like AI and Cloud-based analytics platforms?

Throughout my career working with many high-tech companies, I have interacted with many smart technologies like AI and cloud-based analytics. In my current role as CEO and co-founder of Squelch, I have enjoyed working closely with our product team to learn even more about AI and ML. To help customer-facing agents input queries and locate the information they need to expedite customer resolutions as quickly as possible, our software applies AI and ML to continuously improve, which goes above and beyond most text-based search software.

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How did you start in this space?  What galvanized you to co-found Squelch?

I’ve been involved in the high-tech space in Silicon Valley for over 37 years in a variety of roles, including serial entrepreneur, C-suite executive, and venture partner at Shasta Ventures. Before co-founding Squelch, I had actually retired. During retirement, I met one of my co-founders, Giorgina Gottlieb, at a wine tasting. Many conversations later, we pitched and developed a product that would help people connect with others to surface tribal knowledge within their networks. The technology was so effective that we started receiving requests to apply it to the enterprise. As a result, we brought on our other co-founder, Ilan Raab, and evolved our concept to focus on customer experience with an initial emphasis on customer support and success agents. Retirement has nothing on being the CEO of a company that has such an exciting concept and use of technology!

How do you project Squelch, in the crowded SaaS/Cloud landscape?

Although the cloud-based SaaS landscape is very saturated, our technology and approach are uniquely differentiated from others in the space. The Squelch software surfaces actionable intelligence at the precise moment of customer interaction by querying traditionally disjointed data sets to provide the most relevant information while filtering out extraneous data. As the name suggests, our software slices through the “noise” of superfluous data that lives across multiple, disparate systems within an organization.

As I previously mentioned, the Squelch software also goes above and beyond most text-based search software by applying AI and ML to continuously improve as customer-facing agents input queries and locate the information they need to expedite customer resolutions. Additionally, Squelch’s software has connectors to most SaaS-based applications including Salesforce, Zendesk, Slack, Box, Confluence, Jira, Exchange, and Google Drive, and also integrates directly with Salesforce and Zendesk to enable customer-facing agents to swiftly access relevant intelligence within their existing CRM dashboard.

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What is the current state of NLP technology? How much has it evolved since the time you first started?

As technology evolves, NLP is also undergoing a rapid transformation as it adopts, adapts, and explores the applications of deep learning. After image processing and classification tasks, NLP has proven to be one of the most fruitful areas of deep learning research. Although classical techniques are still highly relevant for many applications, the quality of generic language models has gone up enormously. There is great potential for its use with many different downstream ML tasks — classification, clustering, sentiment analysis, etc. Access to those techniques is also getting easier, and all of these aspects have accelerated since we first started developing Squelch.

How do you envision Customer Experience (CX)? How does Squelch enable customers to comply with standards of data governance to deliver on CX promises?

Great customer experiences and customer-centricity as a whole is crucial to reducing churn and improving renewals and upsells. I think any company discussing customer experience initiatives should take a good look at their Customer Support and Success agents. These individuals are on the front lines, engaging in key customer interactions at a pivotal moment where speed, knowledge, and empathy are essential. At Squelch, we believe these customer-facing agents are a company’s heroes. They deserve to be celebrated and empowered with tools that help them do their job well, which is why we created a solution to help them find the exact insights they need in any situation to provide personalized and valuable customer experiences.

Squelch takes data security and privacy seriously and has a number of policies and procedures in place. Any customer data accessed by the Squelch software continues to be in compliance with data governance standards in part because all source material continues to reside in its original location. In addition, all existing service and file access rights and restrictions are recognized without any additional permissions administration. Squelch is certified under the EU-US Privacy Shield Framework and recently completed its SOC 2 Type 1 compliance certification.

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Tell us more about your vision into Customer Experience (CX). How does Squelch enable customers to comply with standards of data governance to deliver on CX promises?

Including both AI and ML technologies in the stack budget will be a natural progression as these technologies are becoming increasingly enterprise-ready in terms of security, implementation, and affordability. Much like how previous technological innovations have increased productivity or improved decision making, AI and ML continue to push the boundaries of automation and insight which, at the end of the day, is the end goal of most of the technologies.

How do you differentiate between technologies for Customer Success vs. Customer Support? Who are you competing within this landscape?

While both customer success and customer support are essential to delivering exceptional customer service, they each represent a different piece of the puzzle in understanding the customer. A simple way to differentiate between the two is to categorize customer support as reactive while customer success is proactive.

The technologies behind the two are also different. Customer Support tends to center around problem resolution and is often measured on productivity and customer satisfaction, whereas Customer Success centers on having a 360-degree view of the customer, measured by renewal rates or upsells. However, the line separating these two is becoming blurry as Customer Support teams are increasingly being asked to report on the types of problems causing friction for customers, and the data gathered as a result is critical for Customer Success teams.

Ultimately, these two puzzle pieces come together as Customer Experience, which influences a customer’s decision to renew or expand their relationship with a product or company. Managing this effectively is what will affect business results. Squelch augments existing customer-focused tools such as Zendesk or Gainsight for both reactive and proactive servicing of customer needs.

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What are the biggest challenges and opportunities for businesses in leveraging technology to optimize their Customer Support and Customer Success?

As is the case with most parts of a business, leveraging technology has the power to present a variety of opportunities, but when implemented incorrectly, can pose a long list of challenges. Customer Support and Customer Success is no exception.

Digital transformation is both an opportunity and a challenge when leveraging technology to optimize Customer Support and Customer Success. When digitally transforming these areas of the business, the benefits are immediate. Solutions become easy to implement, which is compelling for the end user — the customer, and as a result customers quickly see a significant increase in the value they receive from the company.

However, on the other hand, digital transformation has the ability to move incredibly fast and can be overwhelming. When companies struggle to keep up with the digital transformation, significant friction is felt throughout the entire organization. Given the customer-facing nature of Customer Support and Success teams, they feel this friction even more acutely. There is very little time for error and no time to waste.

How should young technology professionals train themselves to work better with automation and AI-based tools?

How deeply a person should dive into the AI depends entirely on how they plan to engage with the technology — are they an end user? Perhaps an admin? Or a developer? Nevertheless, as obvious as it may seem, the best way to train is to simply do. At a very high level, “doing” means exposing oneself to the latest and greatest technologies at every opportunity, learning how to make them work, and then understanding the basics of how they work. We’re seeing traditionally non-technical roles becoming more and more technical, and having a thirst for knowledge and understanding when it comes to technologies like automation and AI is critical for any young professional to stay competitive.

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What is the biggest challenge to Digital Transformation in 2019? How does Squelch contribute to a successful digital transformation?

Technology is evolving by the minute and for some companies, the rate at which new technology becomes available can be overwhelming. A common challenge for companies going through digital transformation is seen behind the scenes, where there is tremendous effort going into implementing new tools into a company’s existing stack and training existing teams to make use of these new tools.

At Squelch, we address this challenge by making sure our product is as easy as possible to implement and adapt. For Customer Support or Success agents, this means they can use the Squelch product without having to necessarily understand the technologies that power it — it just works. And for IT teams, we’ve designed our product to be as frictionless as possible, from security to ongoing management.

How potent is the Human-Machine intelligence for businesses and society? Who owns machine learning results?

I like this term, “Human-Machine Intelligence”! If I am understanding it correctly, I think it encapsulates the most interesting parts of AI and ML technology and how they can be applied. The notion that all human endeavors can be enhanced with smart technology is incredibly potent, and of course, has an incredible breadth of implications.

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Where do you see AI/Machine Learning and other smart technologies heading beyond 2020?

I think automation and related smart technologies in the near future will make difficult jobs much easier to perform and help improve productivity across the board. We’re already seeing the impact of these technologies in factories and manufacturing environments where the implementation of smart technologies has led to time savings.

What is the Good, Bad and Ugly about AI that you have heard or predict?

With AI, the good is that difficult tasks will become easier and the technology will augment all the good we already see. Take Alexa and other personal voice assistants, for example. In a connected home, a homeowner will ask “Alexa, what’s the weather?” or say “Alexa, turn on the kitchen lights.” Smart technologies are automating typical daily tasks and making people’s lives easier.

As for the bad side (I think the next question covers the ugly), on the heels of convenience is the dependency on this type of technology. In the same smart home, if Alexa doesn’t understand your command to turn on the kitchen lights, they won’t be turned on. And if it’s pitch black in your house, you’ll fumble around the kitchen to find the manual backup of hitting the kitchen light switch. On a larger scale, say satellite technology, this problem becomes much worse. That’s why manual back-ups and fail-safes must accompany any smart technology.

Despite this issue, the benefits outweigh the drawbacks of AI and smart technologies. With Squelch software, the application of AI and ML helps us build the kind of software that helps our customers deliver outstanding customer experiences to their customers.

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The Crystal Gaze

What Cloud Analytics and SaaS start-ups and labs are you keenly following?

I follow a number of startups, but I think it’s important to mention OpenAI and Looker. OpenAI, a non-profit research company with a mission to create safe artificial general intelligence (AGI), focuses on long-term research and transparency and addresses the question of how to advance AGI safely and responsibly. Looker is an interesting generic BI tool powered by AI that has done great work unifying various data sources while offering the ability to understand data without formal training in SQL or other data analytics languages.

I also follow a lot of the big names such as Google as they are contributing not only open source code but models that we, at Squelch, can directly use in our product.

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What technologies within AI/NLP and Cloud Analytics are you interested in?

There’s a lot to be excited about. Like I mentioned before, deep learning language models are where some of the best work is happening today. At the same time, deep learning runtime frameworks like tensorflow, pytorch, and so forth are getting easier to use and deploy in cloud environments. I’m also interested in classical NLP  we’re still uncovering new potential and new applications for older techniques, like topic modeling.

As a tech leader, what industries do you think would be fastest to adopting Analytics and AI/ML with smooth efficiency? What are the new emerging markets for these technology markets?

Overall, I think younger SaaS companies are often the fastest in adopting newer technology. For them, there are far fewer restrictions on development and, to some degree, this often applies to security requirements as well. That being said, I think the subscription-based model is also rapidly spreading into many traditional markets like real estate and finance. As a result, these changes will naturally affect how quickly other industries can adapt to moving forward. Industries whose customers have large data stores will probably experience initial friction in getting their data situated so that the new technology can process it. Once these industries get to that point, I imagine we’ll see quick progress since AI and ML models need considerable amounts of data to be truly successful.

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What’s your smartest work related shortcut or productivity hack?

I think the smartest thing you can do to increase your own productivity is to hire smart people for your team — it goes a long way. But in all seriousness, surrounding yourself with the best and brightest is an incredibly important thing to do in any work environment. Nothing happens in a vacuum, and a great team is a crucial component to success, so my “hack” is to always build a great and talented team that can work together to move the needle forward in achieving your goals.

Tag the one person in the industry whose answers to these questions you would love to read:

John Ragsdale, TSIA

Thank you, Jayaram! That was fun and hope to see you back on AiThority soon.