Encore AI raises $30M to build AI agents that learn from customer calls

Encore AI, a startup that analyzes customer interactions to train and deploy AI voice agents for sales and support teams, has raised $30 million in a Series A funding round led by Team8. The company, which rebranded from Insait IO, uses a process it calls “interaction mining” to study call recordings, emails, and text messages, identifying which conversational approaches lead to successful outcomes and using those findings to train its AI agents.

How Encore AI’s interaction mining works

Founded in 2022 by CEO Dvir Ginzburg, Encore AI originally built recommendation software for financial advisers. The company now collects and analyzes customer interactions across multiple channels, dividing them into stages to determine which parts of a conversation helped move a process forward and which failed. This allows the platform to build AI agents that replicate the most effective playbooks used by an organization’s top performers.

“Sometimes our agents even tell the jokes that the relationship managers are telling, or give the anecdotes or examples that the relationship managers are giving, because we literally run by the playbooks that we see working,” Ginzburg told Bitcoin World in an exclusive interview. The result, he explained, is an AI agent that leverages the strongest parts of an organization’s employee playbooks.

Market position and competitive landscape

Encore AI has more than 40 enterprise customers globally, the majority of which are financial institutions. The company’s annual recurring revenue has increased more than 5x since its seed round less than 18 months ago, though Ginzburg declined to disclose exact revenue figures or valuation. The startup faces potential competition from large CRM providers such as Salesforce, SAP, Zoho, and HubSpot, which could build similar AI capabilities around their customers’ data.

However, Ginzburg contends that access to data alone is not enough. “The biggest players that we are competing against, they don’t see [conversational] history as a data point that they are utilizing. For them to start asking for conversational data with their current employees will require changing their entire implementation stack and technological stack,” he said.

Strategic implications for enterprise AI

The funding round also included participation from Planven, Lukatz, and Garage, as well as several banks and insurers. Notably, some of the financial institutions that invested first used Encore’s product before deciding to invest, signaling strong product-market fit in the financial services sector. The startup plans to use the Series A proceeds to expand its U.S. sales operations and deploy its platform with more large financial institutions.

The approach highlights a growing trend in enterprise AI: using proprietary customer interaction data to train specialized agents rather than relying solely on general-purpose large language models. By focusing on what actually works in real conversations, Encore aims to build agents that are more effective and contextually aware than those trained on generic data.

Conclusion

Encore AI’s $30 million Series A round reflects investor confidence in the company’s interaction mining approach to building AI agents for customer-facing teams. With a focus on financial services and a platform that learns from successful employee playbooks, the startup is positioning itself in a competitive but rapidly evolving market. The company’s ability to scale its U.S. operations and defend its niche against larger CRM vendors will be key to its long-term success.

FAQs

Q1: What is Encore AI’s interaction mining process?
Encore AI collects and analyzes call recordings, emails, and text messages, dividing customer interactions into stages to identify which conversational approaches lead to successful outcomes. These findings are used to train AI agents that replicate effective playbooks.

Q2: Who led Encore AI’s Series A funding round?
The $30 million round was led by Team8, with participation from Planven, Lukatz, Garage, and several banks and insurers.

Q3: How does Encore AI differ from large CRM providers?
Encore AI argues that large CRM providers would need to overhaul their technology stacks to make historical customer conversations the foundation of their AI agents, giving Encore a competitive advantage in the near term.