Lang.ai Raises $10.5M Series A to Help CX Teams Turn Conversations Into Insights and Automation

Lang.ai has announced its completion of a $10.5M Series A led by Nava Ventures with participation from 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 leaders at pioneering AI-based companies including Google, Weights & Biases, Looker, and Ocrolus.

“Even companies with strong data-driven cultures and dedicated insights teams, understand the power of flexibly defining their tags to gain deeper and faster insights without having to tap into scarce and expensive technical resources.”

For high-growth brands, scaling customer support has never been harder, yet never more important. The pandemic has increased the breadth of support required by brands while the great resignation has made finding the talent to service those even more difficult.

Through the applications of Lang’s technology, CX teams are able to scale more efficiently. Lang automatically tags every customer conversation in real-time. By tagging each ticket, companies are enabled to extract more granular insights about their client interactions and more intelligently resolve their issues through easy-to-deploy automation rules. Existing customers include Stitch Fix, Ramp, Good Eggs, Novo, Petal Card, Hippo Insurance and Pair Eyewear.

Lang’s automation is connected to existing help desk solutions such as Zendesk and Intercom. It requires no code and no technical resources to get started. It’s a low lift, high impact solution to tap into the growing amount of data and automation potential for customer service teams.

Some real-life customer examples include Fintechs routing tickets to their Product Operations team when launching new products, E-commerce brands automatically escalating canceled orders to reduce fulfillment costs, and brands auto-responding to zero-touch tickets via email for issues where an agent doesn’t need to be involved.

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Across all these use cases, Lang helps CS teams scale. Ramp is one example of this, as put by Tony Rios, Customer & Product Lead at Ramp; “When we onboarded Lang, we were a team of 2 support agents, including myself, and just setting up our Zendesk instance to our needs. While we’ve scaled the business massively over the past year, we’re always thinking through how we scale operationally without throwing more people at the problem. Lang is able to handle many of the most time-consuming tasks like tagging, routing to the right team of agents and handing playbooks to our agents.

“Lang’s mission is to empower anyone to benefit from the power of AI, and we’ve taken a different approach tailored for business users and done visually, instead of via traditional machine learning approaches that rely on large data sets and labeling/training,” says Jorge Penalva, CEO of Lang.

“Even companies with strong data-driven cultures and dedicated insights teams, understand the power of flexibly defining their tags to gain deeper and faster insights without having to tap into scarce and expensive technical resources.”

“The modern enterprise is inundated with unstructured data from emails to SMS to tickets. The customer service team is at the center of this but has no easy way to manage, let alone leverage, this data. Lang is pioneering an emerging category of companies that empower CX teams to generate actionable insights and uniquely take action on those insights automatically. This turns CX from a cost center to a customer retention 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. By understanding what each individual customer is saying at any point in their buying journey, Lang wants to help CX teams correlate this data to their purchase history as well as recommend automations for tedious workflows agents must follow when dealing with specific issues. To do this, Lang will be investing heavily in R&D and GTM teams.

Ultimately, the goal of Lang.ai is to become a core layer of the CX stack to build automations and extract better insights by structuring qualitative data. Lang will continue to partner with both the frontend players (e.g. Self-Service Automation platforms ) and the backend players (Data warehouses and BI tools) to help clients drive value from data with a unified view.