Encore AI Secures $30 Million to Deploy AI Agents Trained on Real Customer Interactions
Encore AI, a startup specializing in AI-powered voice and text agents for customer support and sales teams, has raised $30 million in a Series A funding round led by Team8. Additional participants include Planven, Lukatz, Garage, as well as a number of banks and insurers — some of which reportedly chose to invest after first using the product. The capital will be used to expand U.S. sales operations and accelerate deployment with large financial institutions.
The company was founded in 2022 under the name Insait IO by CEO Dvir Ginzburg, originally building recommendation software for financial advisers and relationship managers. Now rebranded as Encore AI, the platform has evolved into something considerably more ambitious: a system that mines a company's own customer conversations to identify what works — and then uses those findings to train AI agents that can replicate the most effective approaches at scale.
How "Interaction Mining" Works
The core of Encore AI's technology is what Ginzburg calls interaction mining. The platform ingests call recordings, emails, and text messages, and connects this data with a company's existing CRM system. It then segments customer interactions into stages and analyzes which parts of a conversation drove positive outcomes — and which did not.
The practical result is an AI agent that doesn't just follow a generic script, but draws on the actual playbooks that have proven effective within a specific organization. According to Ginzburg, this level of specificity can go surprisingly deep:
"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."
This approach also allows companies to identify inefficiencies and friction points in their existing sales and support processes — surfacing not just what the AI should do, but where human workflows are already falling short.
Deployment Modes and Customer Base
Encore AI's agents are designed to operate in multiple configurations:
- Autonomous mode: Agents communicate directly with customers via voice or text, without human involvement.
- Assistant mode: Agents support human employees in real time, recommending responses and tactics during live conversations.
The company currently serves more than 40 enterprise customers globally, with the majority being financial institutions. Ginzburg noted that Encore's annual recurring revenue has grown more than 5x since the company's seed round less than 18 months ago, though he declined to disclose specific revenue figures or the company's valuation.
Why This Is Relevant for E-Commerce and Shop Operators
While Encore AI's current customer base is concentrated in financial services, the underlying technology model holds significant implications for the broader e-commerce and retail sector. Online retailers increasingly rely on customer service interactions — whether via chat, phone, or email — as key touchpoints that affect conversion, retention, and satisfaction.
The principle behind interaction mining directly translates to e-commerce scenarios: Which support conversations lead to a completed purchase? Which upsell approaches work in post-purchase follow-ups? Where do customers drop off in a returns or complaints process? A platform that can systematically extract these insights from real interaction data — and then encode them into deployable AI agents — addresses problems that most shop operators currently solve through guesswork or expensive manual analysis.
For Shopware merchants in particular, where CRM integration and customer communication are often managed through a patchwork of tools, a unified platform that connects conversational data to actionable AI represents a meaningful step forward in operational maturity.
The Competitive Landscape
Encore AI enters a market that is far from uncontested. Large CRM vendors — including Salesforce, SAP, Zoho, and HubSpot — are all building AI capabilities and have access to vast amounts of customer data. However, Ginzburg argues that data access alone is not the decisive factor:
"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."
This is a credible, if time-limited, advantage. Established CRM providers are slow to overhaul core architecture, which gives focused startups like Encore AI a window to establish enterprise relationships and deepen integrations. However, that window will not remain open indefinitely as major platforms accelerate their AI roadmaps.
Practical Takeaways for E-Commerce Operators
- Audit your conversational data: Call recordings, chat logs, and email threads are likely underutilized assets in most e-commerce operations. Understanding what patterns exist in successful versus unsuccessful interactions is a prerequisite for AI-assisted improvement.
- Evaluate CRM integration depth: AI agents are only as useful as the data they can access. Ensuring your CRM captures interaction history — not just transaction data — is increasingly important as AI tooling matures.
- Consider hybrid deployment first: For most operators, AI agents working alongside human staff (rather than replacing them) is a lower-risk entry point that still delivers measurable efficiency gains.
- Watch the financial services use case: Regulated industries like banking and insurance often serve as early proving grounds for enterprise AI. Solutions validated there tend to expand into adjacent verticals, including retail and e-commerce.
Outlook
Encore AI's Series A positions it to scale aggressively in the U.S. market, with a clear initial focus on financial institutions. The broader trajectory, however, points toward any industry where customer conversations are both frequent and consequential — a description that fits e-commerce precisely. As AI agent platforms mature and integration with tools like Shopware becomes more standardized, the question for shop operators will shift from whether to adopt such technology to which platform best fits their existing data infrastructure and customer communication patterns.
The funding round and Encore AI's growth trajectory are a further indicator that AI agents trained on proprietary, organization-specific data — rather than generic large language models alone — are becoming a distinct and investable product category. For e-commerce operators building a content and automation strategy today, this development is worth tracking closely.