Casey

What do they actually do

Casey converts a broker’s raw client and risk data into carrier‑ready submission packages so brokers don’t have to assemble ACORD forms, driver lists, vehicle schedules, and loss runs by hand. A typical flow is: upload client data, Casey auto‑extracts and normalizes it, then generates a complete package for wholesalers/underwriters. After submission, Casey compares underwriter questions to existing data and drafts requests for any missing information (meetcasey.ai, Y Combinator, LinkedIn post).

The product is live by demo (no public pricing visible) and is aimed at commercial broker workflows, focusing on removing manual admin in the submission and follow‑up loop (meetcasey.ai, Y Combinator, Fondo launch post).

Who are their target customer(s)

• Commercial insurance brokers at small-to-medium brokerages: They spend hours assembling ACORDs, driver lists, vehicle schedules, and loss runs for each deal, which slows placements and reduces time selling. Casey promises to turn client risk data into carrier‑ready submission packages so they don’t have to build these files manually (meetcasey.ai, Y Combinator).
• Submission coordinators / producer assistants: Their day‑to‑day is repetitive data extraction, normalization, form‑filling, and chasing missing items from clients/underwriters. Casey auto‑extracts and normalizes uploaded data and prepares requests for missing information, targeting that workload (meetcasey.ai, LinkedIn post).
• Brokerage operations managers: Manual, inconsistent submission processes create bottlenecks, rework, and unpredictable turnaround across a book. Casey’s stated goal is to standardize and automate submissions as an infrastructure layer to reduce operational overhead (Y Combinator).
• Wholesale brokers and underwriters who receive submissions: They often get incomplete or poorly formatted packages that require extra review and back‑and‑forth, delaying quoting and binding. Casey builds carrier‑ready packages and is working to triage or answer underwriter questions so reviewers get cleaner, more complete files (meetcasey.ai, Fondo launch post).

How would they acquire their first 10, 50, and 100 customers

• First 10: Run high‑touch pilots with 8–10 small-to-medium commercial brokerages that still assemble ACORDs by hand; convert warm YC/industry intros into 4–8 week paid pilots with clear time‑saved and fewer follow‑up metrics, hands‑on onboarding, and one‑off templates/connectors to prove ROI (meetcasey.ai, Y Combinator).
• First 50: Use early case studies to drive a focused outbound to submission coordinators and ops managers, plus weekly demo webinars and LinkedIn/broker forums to convert inbound (site already supports demo booking). Package onboarding into a 2–3 week pilot kit with templates, checklists, and success metrics (meetcasey.ai).
• First 100: Layer in channel partnerships with wholesalers, MGAs, and brokertech vendors; productize connectors to common AMS/forms systems to cut implementation time. Introduce tiered plans and self‑serve trials for lighter‑touch accounts while expanding CS/solutions support.

What is the rough total addressable market

Top-down context:

The U.S. Insurance Brokers & Agencies market was about $260.1B in 2024, indicating a large distribution channel spend base (IBISWorld). Independent agencies place the vast majority of commercial lines (about 87.2%), aligning with Casey’s focus on commercial submissions (Insurance Journal).

Bottom-up calculation:

A concrete U.S. target set is ~39,000 independent P&C agencies. At illustrative pricing of $5k–$50k per agency per year and 10%–50% penetration, the ARR opportunity ranges from roughly $19.5M to $975M (Insurance Business Mag).

Assumptions:

• Targetable U.S. customers ≈ 39,000 independent P&C agencies.
• Annual price per agency bands: $5k, $20k, $50k (illustrative; actual pricing not public).
• Penetration scenarios modeled at 10%, 25%, and 50%.

Who are some of their notable competitors

• Indio (Applied Systems): Broker-facing application and submissions platform that digitizes ACORD and supplemental forms, collects client data, maps it to forms, and packages submissions—overlaps directly with Casey’s core form assembly/submission use case (Indio, Applied Systems).
• Vertafore (AMS360 / Commercial Submissions): Incumbent AMS with commercial submissions/rating that pre‑fills ACORD eForms and connects brokers to many carriers—competes by embedding submission workflows where many brokers already work (AMS360, Commercial Submissions).
• Indico Data: AI document ingestion and decision‑automation for extracting structured data from unstructured submissions and powering underwriting workflows; overlaps on intake and underwriter Q&A/triage (Indico Data, Underwriting automation).
• Federato: AI‑native underwriting/RiskOps platform that triages submissions, scores appetite/winnability, and flags missing items—competes on the post‑submission triage Casey plans to automate (Federato).
• Sixfold: Underwriting AI that synthesizes risk vs. appetite and uses agents to automate routine underwriting actions (research, referrals, documentation); overlaps with Casey’s roadmap to automate the underwriter Q&A loop (Sixfold).