Artificial intelligence has become part of insurers’ everyday work. In a recent global survey, 81% of industry executives said their companies use AI in some or most business processes. In most cases, however, that means individual tools rather than connected systems capable of handling the insurance sales process autonomously from beginning to end. Superagent AI is one of the first successful efforts to build such a system. The company’s co-founder and CTO, Vadym Shashkov, designed an autonomous multi-agent platform that handles insurance sales from the first customer inquiry to a policy ready to be bound. We spoke with Vadym Shashkov about how he fine-tuned models on calls handled by top-performing agents, and deployed the platform without requiring agencies to integrate it with their existing systems or provide any personally identifiable information (PII) about their customers during onboarding.
— Superagent AI is one of the first systems designed to handle the entire insurance sales process autonomously, from the first call through policy purchase. In a sentence, what does it do? And why has it gained traction so quickly?
— We built the first fully autonomous, multi-agent AI workforce for insurance — nine specialized agents that run an agency’s entire sales cycle, from the first call to a bound policy. The problem is structural: the industry is losing licensed agents to retirement faster than it can train new ones, and every prior attempt to automate insurance sales ran into the same two barriers: regulation and the complexity of carrier systems. We set out to close that gap without asking agencies to compromise on compliance.
— As CTO, you designed the platform’s architecture. What’s the core technical innovation behind it?
— Three things working together. First, nine agents that share context and hand off work like a human team — inbound, outbound, onboarding, live coaching, quoting, binding, and retention. Second, a closed-loop learning design: every call and objection becomes training data, so the gains compound the longer the system operates inside an agency. Third, models fine-tuned on proprietary insurance sales-call data from top-performing human agents — that’s what lets the AI hold a compliant, natural insurance conversation a general-purpose model can’t.
— You chose to fine-tune the models on real calls from top-performing agents rather than rely on a general-purpose model. Why was that necessary in insurance sales?
— Because insurance sales isn’t a generic conversation. It’s regulated state by state; tone and disclosures matter, and a wrong answer has real consequences. General models are fluent but not compliant or domain-accurate. Fine-tuning on real top-agent calls was the decision that most defines our product — it’s the single biggest reason the platform produces outcomes off-the-shelf AI can’t.
— You also built closed-loop learning into the platform from the start. How does that loop work, and what happens to performance over time?
— Every interaction feeds the system: each call the AI joins provides data, each coaching tip to a human agent improves conversion, and each customer objection becomes training data fed back into the models. So the platform doesn’t just work on day one — it keeps getting better the longer it’s deployed. That compounding is the architectural bet.
— Quoting remains one of the most complex and labor-intensive parts of an agency’s work, yet you managed to automate it. What makes autonomous quoting so difficult, and how did you solve it?
— Quoting means logging into different carrier systems, each with its own application, and comparing the resulting quotes — work that has always required a licensed agent for every quote. Our Quoting AI Agent does it autonomously and returns a bind-ready quote in about 30 seconds. Getting there meant building credentialed automation, handling widely varying carrier forms, and achieving the accuracy required for a quote to be ready to bind.
— Insurance is heavily regulated. How did you design the platform so agencies could deploy it safely and remain compliant?
— Compliance-by-design. We deploy with no system integration and no customer PII required at onboarding, so agencies can adopt quickly while remaining compliant. On top of that, we’re SOC 2 compliant, and an independent security assessment found that our security architecture exceeded “industry best practice” — which is rare for an early-stage company in a regulated sector.
— What outcomes are agencies actually seeing?
— Measurable ones. New-agent ramp-up cut in half across our base; at RightSure specifically, ramp fell from seven to nine months to about two and a half weeks, with a 151% lift in quote-to-sale conversion. Policy retention up 35%. A 14-to-1 ROI within 90 days and a 98.7% satisfaction rate.
