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

Israeli startup Encore AI has raised $30 million in a Series A round led by Team8 Capital to expand its platform that trains AI agents by mining successful customer interactions. Founded in 2022 as Insait IO by CEO Dvir Ginzburg, the company originally built recommendation software for financial advisors before pivoting to what it calls "interaction mining."

The Encore platform collects call recordings, emails, text messages, and CRM data, then breaks down each customer interaction into stages to determine which conversational tactics drove positive outcomes and which fell flat. This allows the company's AI agents to replicate the best practices of an organization's top-performing employees.

"Sometimes our agents even tell the jokes that the relationship managers are telling, or give the anecdotes that the relationship managers are giving, because we literally run by the playbooks that we see working," Ginzburg told TechCrunch in an exclusive interview. "The agent we build is a package of many different playbooks that have worked throughout the process."

Ginzburg notes that different employees may be more or less effective at different stages of a sale or customer success process. By aggregating the winning patterns across the entire organization, Encore's AI can draw from a broader arsenal of effective techniques than any single employee could master.

Beyond powering autonomous voice and text agents that interact directly with customers, the platform serves as an analytical tool. Companies can identify where their existing sales and support processes are falling short, pinpoint friction points in customer journeys, and surface key operational issues.

The $30 million Series A arrives at a time when the AI sales agent space is becoming increasingly crowded, with players ranging from voice-focused incumbents to emerging conversational AI platforms. Encore's differentiation lies in its thesis that the best AI agent is one trained on an organization's own proven practices, rather than generic external data.

For enterprises deploying AI in customer-facing roles, the promise of agents that learn from internal success stories rather than black-box training data could be compelling from both a performance and compliance perspective. The company's next challenge will be scaling its platform while maintaining the quality of its interaction mining across diverse industries and languages.