Lang.ai looks to help orgs extract value from customer conversations, with AI

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Turning a conversation – from customer support requests to user feedback – into tangible business value is not an easy task. It is also an ideal use case for AI-based automation.

Among the vendors helping organizations use AI to get value from customer conversations is San Francisco-based Lang, which announced today that it has raised $ 10.5 million in a Category A round. Lang’s platform is integrated with help desk, customer relationship management and user-facing operations for feedback and requests. The system uses the Unsupervised Learning model to adapt to the ever-changing flow of information by classifying the data and then helping to determine what should be done with the data to help improve the user experience and business results.

“There’s been an increase in the amount of conversations that business teams have to deal with, especially things like customer support, which have been raised during the epidemic,” Lang’s CEO, George Penalva, told VentureBeat. “Sure, there are a lot of AI technologies out there, but in general, it’s built by engineers for engineers – so there’s a lot of complexity. We believe that there should be a better way for business users to use AI. “

Lang is definitely not alone in its corner of the market. Zendesk, for example, has built up its AI capabilities in recent years to help its customer service platform. A key element of its capabilities comes from the company’s 2021 acquisition of Cleverly.ai.

CRM giant Salesforce is also very active in the AI ​​space with its Einstein platform. Contact Center technology vendor Genesys continues to actively enhance its AI capabilities with its Google partnership.

A recent report by Fortune Business Insights estimates that the global customer experience management market size will reach $ 11.3 billion by 2022. The report predicts that the market will grow at a compound annual growth rate (CAGR) of 16.2% over the next seven years, reaching .5 35.5. Billion by 2029.

How Lang uses AI to get value from a conversation

Penalva is keenly aware of market potential and competition. According to him, Lang offers a different approach thanks to the use of unsupervised AI model.

A common approach to enabling AI is to use a supervised model that trains against a given set of data. The challenge with the supervised model is that AI is often trained on static data. Penalva noted that while data changes rapidly and organizations actually respond to users, training on static data is not good enough. That’s why his company has developed a purpose-built unsupervised learning model that is constantly looking at ever-changing data.

A Glimpse of Lang.ai's Customer Conversation Management Platform
A Glimpse of Lang.ai’s Customer Conversation Management Platform
Credit: Lang.ai

How it works: Lang connects to customer data and analyzes unsupervised model data, converting it into simple “concepts” – which Penalva explained is the business term for the item or operation that the company needs to track. . For example, the concept could be delivery date, product or credit rating. AI models automatically exclude key concepts in a conversation, so they can be grouped into categories that make sense for a particular business.

The series interface is provided to users in a no-code model, enabling the organization to group items as needed. The no-code interface also helps to provide an explanatory AI format, so that users can easily see how the unmanned model extracted the concepts and in what categories the concepts were placed.

Scaling operation

Using AI to derive business value from conversations can also help organizations measure performance.

An example is with Lang Customer Ramp, which offers online tracking services for cost. According to Penalva, Ramp’s challenge was that he wanted to increase efficiency quickly. With Lang, Ramp was able to quickly categorize customer requests into categories and then provide automated workflows to speed up resolution. For example, Ramp can ensure that inquiries about a credit issue are sent to an agent who can quickly respond to such a request.

Ramps also use Lang to understand customer feedback. As Ramp builds new products, responses and requests are analyzed by Lang to better understand how the new product is being received and what to do if a change needs to be made to optimize the user experience.

“We really use their support data for automation and for insights that other teams can use,” he said.

With the new Category A funding, Pilwa seeks to continue helping organizations retrieve business value from data more easily and automate repetitive tasks.

“We think a lot of companies are thinking about how they can be more efficient these days,” he said. “There’s a lot of inefficiency when you people think about repetitive tasks in their daily jobs, when they really should focus on more high-level tasks,” Penalva said.

The new funding round was led by Nava Ventures and involved partnerships with Oceans Ventures, Forum and Flexport Fund.

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