Encore AI raises $30M for voice agents trained on customer interactions
The startup has secured $30 million to scale its AI voice agents that learn from successful employee interactions, signaling a shift in how global financial institutions manage client relationships.
Encore AI has secured $30 million in a Series A funding round led by Team8. The capital will be used to expand its U.S. sales operations and deploy its platform across more large financial institutions.
Founded in 2022 by chief executive Dvir Ginzburg, the company builds artificial intelligence voice and text agents for customer support and sales teams. Unlike generic models, the platform uses a process the founder calls interaction mining to study call recordings, emails, and text messages.
The system connects this communication data to customer relationship management tools to identify which conversational approaches drive successful outcomes. The resulting agents then replicate the most effective strategies used by human staff.
“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 said. He described the resulting technology as a consolidated package of various strategies that have proven effective over time.
This capability is particularly relevant for the financial sector, which makes up the majority of the startup's more than 40 global enterprise customers. European and international banks are increasingly turning to specialized artificial intelligence to maintain high-touch client relationships while managing operational costs.
The company, formerly known as Insait IO, has seen its annual recurring revenue grow more than fivefold since its seed round less than 18 months ago. Exact revenue figures and the company valuation were not disclosed.
Planven, Lukatz, and Garage participated in the latest funding round alongside several banks and insurance companies. Some of the financial institutions that provided capital were already using the product before deciding to invest.
The startup faces potential competition from established customer relationship management providers like Salesforce, SAP, Zoho, and HubSpot. These larger vendors possess vast amounts of client data and could theoretically build similar capabilities.
However, Ginzburg argues that simply having access to data is insufficient for replicating this specific approach. He noted that incumbent vendors do not currently treat conversational history as a foundational data point.
“For them to start asking for conversational data with their current employees will require changing their entire implementation stack and technological stack,” he said. This structural hurdle gives early movers a window to establish their technology before larger rivals can adapt their underlying architecture.
The ability of these agents to act autonomously or assist employees in real time highlights a broader shift in enterprise software. Companies are moving away from static tools toward dynamic systems that actively learn from and replicate human expertise.
