Pibit.AI Secures $7M Series A to Bring Trusted AI Underwriting Into the Mainstream

The insurance industry has long been defined by its ability to understand, quantify, and manage risk. Yet the systems underwriters rely on still look and feel like artifacts from another era. While other industries have embraced advanced automation and intelligent tooling, underwriting has remained dependent on spreadsheets, scattered documents, and labor-intensive reviews. That gap is exactly what Pibit.AI aims to close — and with a fresh $7 million Series A, the company is accelerating its plan to modernize one of insurance’s most essential functions.

The funding round, led by Stellaris Venture Partners with participation from Y Combinator and Arali Ventures, will fuel the continued development and adoption of Pibit.AI’s Centralized Underwriting Risk Environment (CURE™). Designed to unify the full underwriting journey — from first submission to final decision — CURE™ positions Pibit.AI at the center of a rapidly evolving market where carriers and MGAs are under increasing pressure to do more with less.

A Mission Rooted in Personal Experience

For founder Akash Agarwal, the company’s origin is deeply personal. As a child, he watched his father work late into the night managing insurance paperwork by hand. Years later, as he witnessed AI revolutionize industries from transportation to logistics, the contrast was stark: if AI could guide vehicles autonomously, why was underwriting still stuck in a world of PDFs, emails, and manual data entry?

This question became the spark behind Pibit.AI. Agarwal envisioned an underwriting environment where AI serves as a reliable, transparent partner — not a black-box replacement. Trust became the central pillar of the company’s philosophy: every output should be explainable, verifiable, and aligned with an underwriter’s expertise.

“AI should empower underwriters, not replace them,” Agarwal noted. “We’re building something that’s transparent and decision-ready — a system that underwriters can trust while helping them move faster than ever before.”

The CURE™ Platform: Transforming the Underwriting Lifecycle

At the heart of Pibit.AI’s offering is CURE™, a tightly integrated platform that consolidates the fragmented underwriting process. Instead of toggling between tools, documents, and data sources, underwriters operate within a single environment enhanced by AI-driven intelligence.

CURE™ includes purpose-built modules such as:

• ClearCURE™ for submission triage
• DocumentCURE™ for accurate parsing of messy, unstructured documents
• ResearchCURE™ for real-time data enrichment
• RiskCURE™ for advanced account evaluation and portfolio-level insights
• WorkflowCURE™ for orchestrating tasks, collaboration, and decision tracking

Together, these modules support a streamlined journey from initial submission to decision-ready output. The platform automates data gathering and classification while maintaining human oversight where it matters most. Pibit.AI’s philosophy blends speed with reliability: automation accelerates the process, while transparent logic preserves trust, consistency, and compliance.

Delivering Measurable Impact for Carriers and MGAs

The need for modernization is growing more urgent. Submission volumes continue to rise, yet underwriting teams are shrinking. Many teams still lose up to a third of their time to manual triage, document handling, and data normalization. The inefficiencies don’t just slow teams down — they impact accuracy, revenue, and risk selection.

Pibit.AI’s customers, which include HDVI, Shepherd Insurance, RMS Insurance Brokerage, Kinetic, and Method Insurance Services, are already seeing dramatic improvements. Reported outcomes include up to 85% faster underwriting cycles, a 32% increase in gross written premium per underwriter, and as much as 700 basis points of improvement in loss ratios.

For insurers, these gains translate into greater capacity, higher throughput, and the ability to scale profitably. As Adam Price, CEO at Kinetic, highlighted: the company can now manage more than a billion dollars in annual submissions without needing to increase overhead. For Method Insurance Services, the platform has provided the structure to scale nationally without compromising control or accuracy.

For Stellaris Venture Partners, that combination of efficiency and transparency was a decisive factor in leading the Series A round. Partner Alok Goyal emphasized that CURE™ addresses the core constraints throttling modern underwriting — outdated workflows, inconsistent data, and rising operational demands.

The Broader Implications: What This Technology Signals for the Future

The rise of platforms that unify data, automate routine analysis, and bring explainability to complex decisions marks a turning point for industries that have historically depended on manual expertise. Underwriting is one of the clearest examples, but the underlying trend extends far beyond insurance. As organizations across sectors generate exponentially more information than teams can reasonably process, the need for systems that can interpret unstructured inputs, surface relevant insights, and present them in a transparent way is becoming universal.

The next decade will likely see a shift in how knowledge work itself is organized. Instead of professionals spending large portions of their day gathering, formatting, or reconciling information, their focus will move toward evaluating AI-generated recommendations, exploring edge cases, and applying judgment to the scenarios that don’t fit the mold. This transition won’t eliminate human expertise — it will reshape where that expertise is applied. People will increasingly act as decision architects rather than data processors.

A parallel transformation will unfold around trust. As AI becomes involved in more high-stakes decisions, the demand for explainability will only intensify. Systems that can show their reasoning, cite the evidence behind their conclusions, and quantify uncertainty will set the standard. Tools that fail to provide that level of transparency will struggle to gain acceptance, especially in regulated industries.

Longer term, the accumulation of structured, decision-ready data will create new possibilities. More accurate risk modeling, cross-industry benchmarking, and real-time monitoring of emerging trends will become feasible in ways that aren’t possible today. Entire workflows — not just isolated tasks — will be redesigned around continuous intelligence rather than static documents and episodic reviews.