With AI, Lang.ai hopes to help organizations extract value from customer conversations

With AI, Lang.ai hopes to help organizations extract value from customer conversations ...

Turning conversations from customer support requests to user feedback into tangible business value is no easy task. It''s also an ideal method for AI-based automation.

Lang, a San Francisco-based startup, has raised $10.5 million in a series A round of funding. The platform seamlessly integrates with a help desk, customer relationship management, and user-facing processes to adapt to the constantly changing flow of information. By categorizing data, and then determining what should be done with the data, users can benefit from user experience and business outcomes.

A growing business is a result of generating value from customer conversations.

Lang is certainly not alone in its niche in the market. Zendesk, for example, has expanded its AI capabilities in recent years to assist with its customer service platform. A core component of its capabilities came from the company''s 2021 acquisition of Cleverly.ai.

Salesforce, a CRM company, is also extremely active in the AI space with its Einstein platform. Genesys, a contact center technology company, continues to grow its AI capabilities with its Google partnership.

According to a recent survey from Fortune Business Insights, the global customer experience management market will increase at a compound annual growth rate (CAGR) of 16.2% in the next seven years, achieving $35.5 billion by 2029.

How Lang utilises AI to drawbacks from conversations

Penalva is fully aware of the marketplace potential and the competition. Lang is looking for a different approach, owing to the use of a supervised AI model.

A common approach to enabling AI is the use of a supervised model that trains against a specific set of data. Penalva noted that data is often altered quickly, and training on static data isn''t adequate. That''s why his company developed a purpose-built unsupervised learning model that is constantly looking at data.

Lang connects to the customer data and the unsupervised model investigates the data, transforming it into simple concepts which Penalva described as a business term for an item or business. A concept may be a delivery date, a product, or a credit rating, according to the AI model. These concepts can be grouped into categories that fit into a specific business.

In a no-code model, the user interface to the categories is provided to them, which allows an organization to group things as required. A form of explainedable AI is also available, allowing users to understand how the unsupervised model extracted concepts and what sections the concepts are divided into.

Scaling operations

Using AI to extract business value from conversations might help organizations in weighing their operations.

Ramp, an online tracking service, was able to rapidly categorize customer requests into categories and then provide automated workflows to speed resolution. For example, Ramp can ensure that an inquiry about a credit issue is sent to an agent who can respond quickly to that type of request.

Ramp employs Lang to gather customer feedback. Lang tries to understand how the new product is being received, and what if any modifications must be made to enhance user experience.

He said, we really optimize their support data for automation as well as internal insights that could be used by other teams.

Penalva hopes to continue to assist organizations more easily desive business value from data and as a way to automate repetitive tasks with the new series A financing.

These days, a lot of organizations are going to be thinking about how they become more efficient, according to Penalva. There are a lot of gaps when you think about the repetitive tasks that people do in their daily tasks, when they really should focus on higher-level tasks.

The new funding round was led by Nava Ventures and included the participation of Oceans Ventures, the Forum, and the Flexport Fund.

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