Encore AI secures $30M to power AI agents trained on customer conversations

Encore AI, a platform that builds conversational AI agents by analyzing how companies interact with customers, has closed a $30 million Series A funding round led by Team8. The fresh capital will fuel expansion of its sales footprint in the US and deepen relationships with financial institutions.

The company, which rebranded from Insait IO, operates on a principle CEO Dvir Ginzburg calls "interaction mining"—systematically reviewing customer calls, emails, and texts to identify which employee behaviors and talking points drive positive outcomes. The platform then distills these patterns into AI agents that can either handle customer conversations autonomously or work as real-time assistants feeding recommendations to support and sales staff.

How the technology works#

Encore's system ingests call recordings and written communications, integrating them with a company's CRM data. The platform segments conversations into stages, isolating the moments where friction emerged or momentum built. By reverse-engineering what worked at each juncture, the AI learns to replicate successful playbooks. According to Ginzburg, agents sometimes adopt the exact humor, anecdotes, and phrasing used by top performers. "The agent we build is a package of many different playbooks that have worked throughout the process," he explained to TechCrunch.

The AI can interact with customers via voice or chat, and also surface suggested responses to employees in real time as they handle calls or messages.

Market position and competitive advantage#

Encore has already signed more than 40 enterprise customers, primarily in financial services. The company has grown annual recurring revenue more than fivefold since its seed round closed less than 18 months prior, though specific numbers remain undisclosed.

Larger competitors like Salesforce, SAP, Zoho, and HubSpot could theoretically build similar capabilities around their installed customer bases, but Ginzburg contends they face structural barriers. "For them to start asking for conversational data with their current employees will require changing their entire implementation stack and technological stack," he noted. Incumbent providers have not historically centered historical conversation data as a core data asset, making a pivot resource-intensive.

Planven, Lukatz, and Garage participated in the round alongside several financial institutions, some of which were existing customers before choosing to invest.

The funds will support scaling US operations and deepening penetration in the financial services and insurance sectors.