Zingly wants to revolutionize customer service with AI collab rooms

Customer service is the cornerstone of many successful businesses across sectors. If you do it right, you can build long-term relationships and brand loyalty.

However, despite the advances in technology over the last decade or so, customer experience (CX) has been declining, with customers finding it hard to reach out and get the assistance they need on time.

Today, Zingly, a California-based startup working to address this imbalance with the power of AI and data, emerged from stealth. 

The company also announced it has raised $10 million in seed funding, led by WestWave Capital with participation from Dell Technologies Capital, Scribble Ventures, Formus Capital, and multiple other venture capital firms.

While many companies are already helping enterprises leverage AI to assist agents in addressing customer queries, Zingly is taking a different approach by putting end customers at the driver’s seat.

The company has developed an AI-powered collaborative space that saves customers from the hassle of dealing with long wait times on automated interactive voice response (IVR) calls or rigid text-based online chatbots that do not understand the depth of the problem at hand.

It is still in the early stages but is already delivering positive outcomes for its first customers.

How Zingly addresses the CX problem

Today, with hundreds of thousands of brands out there and billions of customers, the problem of customer service has become one of scale.

If you rely on traditional IVR calls (which are getting expensive over time), there aren’t enough agents to address all the callers just in time.

On the other hand, if you’ve built or bought a chatbot to solve for scale, the customer might feel the experience is not personalized to their needs. 

Gaurav Passi, who previously worked at companies like Avaya and Amdocs, saw the broken CX issue firsthand and decided to team up with CX, CRM and CCaaS leaders from Five9, Talkdesk and Glia to launch Zingly.

The startup provides brands with a collaborative space, where their agents can engage with customers while having the power of data and AI in their hands at the same time.

“The math is clear, there are approximately 16.5 million call center agents globally handling potentially billions of customers. The frustration and long wait times associated with traditional calling and rigid workflow-driven chatbots have created what we call a fear of reaching out among customers…Zingly represents a significant leap forward from the traditional trade-off between personalization and scalability in CX,” Passi added. 

At the core, the company provides a low-code platform that integrates into any tech stack (CRM, existing contact center technologies, customer support systems, and core operations) and funnels customer data into the Zingly interface integrated with the brand’s app/website.

This way, when the customer hits the contact button on the service in question, they get a full-fledged panel showcasing their journey with the brand, complete with past orders, previous open/closed cases, overall relationship with the company (based on ratings), and the options to launch a new Zingly-Room to get in touch.

Once a customer launches the room, a generative AI assistant called Buddy, powered by a combination of off-the-shelf and in-house models, comes and asks the user about the issue at hand, with options for uploading photos/videos.

The bot analyzes the responses and tries to solve the problem on its own. However, if the matter is too complicated for AI and requires an expert, it marks the room as urgent to bring a human agent in the loop via call/chat.

This is executed with the company’s proprietary Relationship AI, which analyzes different parameters, including what is being discussed with the bot and where they are in the process to determine when an agent should be engaged.

After a human agent comes into the room, the user is notified to come back. The agent, who also has a similar interface with the entire customer story and context, can then use gen AI-generated replies to continue the conversation and solve the case. They can also rope in technical experts into the conversation to figure out details of the resolution and execute follow-up processes like payments and scheduling visits.

Notably, when the issue is resolved, the customer can go to the open room and close the case on their own. If it’s not done perfectly or the issue has reappeared, they can reopen the same room and pick up where they left off via call/chat. Previously, all these things were handled by agents after calls, messages or emails from customers.

Impact yet to be seen

While the approach from Zingly is different, it remains to be seen how impactful the technology would be in the real world, especially against next-gen LLM chatbots coming to the scene. 

Companies like Sierra are already building these AI agents that can connect to enterprise data and systems to reason, problem-solve and make decisions while also having humans in the loop. The only difference with Zingly is that it gives customers full control over how they interact with an enriched panel of their relationship and journey with the brand. 

According to Zeus Kerravala, principal analyst at ZK Research, this approach from Zingly reimagines the dynamics of brand-customer interactions. 

“Incremental improvements to CX were not enough. A deep technology solution intelligently combining data, GenAI, and humans, as well as a dedicated team of CX experts together with forward-looking Fortune 500 design partners was required for such a revolution,” Kerravala said. 

Currently, Zingly is focusing on high-value industries such as financial services, healthcare and product companies. One of its early customers is a Fortune 500 provider, which added the platform on top of its core infrastructure systems Salesforce and Avaya. The company has since been able to engage with 5x more customers than traditional 1:1 phone conversations, while also reducing typical conversion time from 60 to 18 days, resulting in tens of millions of dollars of revenue uplift. 

The same technology can also enable enterprises to handle other business-critical use cases like customer acquisition, onboarding and success. However, as of now, the company is looking to build on its work while expanding teams in data science and engineering, sales, and marketing departments. It plans to double the total company headcount by the end of the year.