SAN FRANCISCO – (COMMERCIAL LINE) – Lang.ai announced the completion of a $ 10.5 million Series A led by Nava Ventures featuring new and existing investors including Oceans Ventures, Forum, Flexport Fund, as well as industry leaders – Mike Murchison (CEO of Ada), Joaquim Lecha (CEO of Typeform) and Javier Mata (CEO of Yalo), senior engineering and sales executives from pioneering AI-powered companies including Google, Weights & Biases, Looker and Ocrolus.
For fast-growing brands, scaling customer support has never been this difficult, but never more important. The pandemic has increased the breadth of support demanded by brands, while the large resignations have made it even more difficult to find the talent to assist them.
Through applications of Lang’s technology, CX teams are able to scale more efficiently. Lang automatically tags every customer conversation in real time. By marking each ticket, companies can extract more insights into customer interactions and solve problems more intelligently through easy-to-implement automation rules. Existing customers include Stitch Fix, Ramp, Good Eggs, Novo, Petal Card, Hippo Insurance, and Pair Eyewear.
Lang’s automation is linked to existing help desk solutions such as Zendesk and Intercom. It does not require code and technical resources to get started. It’s a low-impact, high-impact solution for leveraging the growing amount of data and automation potential for customer service teams.
Some examples of real-life customers include Fintech routing tickets to the product operations team when it launches new products, ecommerce brands automatically submitting canceled orders to reduce fulfillment costs, and brands automatically responding to zero-tickets. touch via email for issues an agent doesn’t need to be involved with.
In all of these use cases, Lang helps CS teams scale. Ramp is one example, as stated by Tony Rios, Ramp Customer & Product Lead; “When we onboarded Lang, we were a team of 2 support agents, including myself, and we simply configured our Zendesk instance to our needs. Although in the last year we have scaled the company massively. , we always think about how to scale operationally without involving more people in the problem. Lang can handle many of the most time-consuming tasks, such as coding, routing to the right agent team, and delivering playbooks to our agents .
“Lang’s mission is to empower anyone to take advantage of the power of artificial intelligence and we have taken a different approach tailored to business users and built visually, rather than through traditional machine learning approaches that rely on data sets. large size and labeling / training, “says Jorge Penalva, CEO of Lang.
“Even companies with strong data-driven cultures and dedicated insight teams understand the power to flexibly define their tags to gain deeper and faster insights without having to tap into scarce and costly technical resources.”
“The modern business is awash with unstructured data, from emails to SMS to tickets. The customer service team is at the heart of it all, but they don’t have an easy way to manage, let alone leverage, this data. Lang is leading the way for an emerging category of companies that enables CX teams to generate actionable information and uniquely act on that information automatically. This transforms CX from a cost center to a customer loyalty center. “Manish Patel, Nava Ventures
The purpose of the funding is to expand Lang’s ability to assist CX teams with a Control Center for Revenue and Automation Opportunities. Understanding what each individual customer is saying at any point in their purchase journey, Lang wants to help CX teams correlate this data to purchase history, as well as recommend automations for the tedious workflows agents have to go through when facing problems. specific. To do this, Lang will invest heavily in R&D and GTM teams.
Ultimately, Lang.ai’s goal is to become a core level of the CX stack to create automations and extract better insights by structuring qualitative data. Lang will continue to work with both front-end players (e.g. self-service automation platforms) and back-end players (data warehouses and BI tools) to help customers generate value from data with a unified view.