Adaptional

What do they actually do

Adaptional builds Underwriter AI, a tool that pre‑processes commercial insurance submissions so underwriters don’t have to do the routine data work themselves. It ingests emails and attachments, extracts key fields, checks them against public and third‑party sources with citations, and compares the file to an insurer’s underwriting guidelines to produce a concise risk summary and recommendation for human review YC profile company site.

The product is used by underwriting teams at carriers and specialty MGAs. The company has publicly indicated deployments with a large carrier and a specialty MGA, with underwriters reviewing the AI’s extraction, sources, and reasoning before making final quote/decline decisions YC profile LinkedIn announcement company site.

Who are their target customer(s)

• Commercial P&C underwriters at large carriers: They spend hours reading broker emails and PDFs to pull policy facts and check exposures, which slows quoting and creates backlogs. They want faster, accurate intake and guideline checks to free time for judgement work.
• Underwriters at specialty MGAs: They receive messy, non‑standard submissions and must validate unusual exposures quickly against guidelines, raising error risk and rework. They need reliable extraction, source‑backed validation, and clear flags on conflicts.
• Underwriting team leads / operations managers: They must raise throughput and meet SLAs without adding headcount, but manual intake and re‑work restrict capacity. They need repeatable automation from submission through quote and renewal to scale teams.
• Submission processors / intake specialists: Their day is dominated by repetitive data entry from applications and attachments, which is low‑value and error‑prone. They need accurate automated extraction to eliminate re‑keying.
• Compliance, risk and audit teams inside carriers: They require auditable trails for underwriting decisions and worry about unsupported automated changes. They need outputs with citations and transparent reasoning for review and audit.

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

• First 10: Run paid pilots with a single underwriting team at large carriers and specialty MGAs where the founders have relationships; integrate intake (email/PDF ingestion, field extraction, citations) and measure queue‑clearance/time saved over 6–12 weeks to secure a named reference YC profile company site.
• First 50: Productize the pilot into a fast‑start package (prebuilt inbox/PDF connectors), add a sales engineer for demos and a salesperson for targeted outbound to carriers/MGAs, and use early case studies and measured intake savings to speed procurement and event‑driven leads company site YC profile.
• First 100: Scale via channel integrations (policy admin, broker portals, MGA platforms), stand up customer success with deployment templates and compliance playbooks for multi‑team rollouts, and add inside sales to convert trials to standard contracts with minimal bespoke work company site YC profile.

What is the rough total addressable market

Top-down context:

Global insurance IT spending is about $232B in 2024; if underwriting is one of ~10–15 core domains of insurer operations, that implies ~$15–23B in annual underwriting‑related software and services spend Gartner McKinsey. The AI‑specific underwriting slice is in the low single‑digit billions today and growing quickly per industry market reports MarketResearchFuture.

Bottom-up calculation:

Practical buyer pool: hundreds of carriers underwriting >$1T in U.S. P&C premiums and a large, growing MGA segment (~$94–102B U.S. written premium) NAIC Conning. If 500 carriers and 1,000 MGAs are viable buyers and average annual contracts range $200k–$600k for intake/guideline automation, that yields ~$300M–$900M initially, with room to expand toward low single‑digit billions as deployments grow in scope and seats (the U.S. alone has ~120–130k underwriters) BLS.

Assumptions:

• 500 carriers and 1,000 MGAs globally are realistic near‑term buyers for AI‑underwriting tools.
• Average ACV for intake/guideline automation is ~$200k–$600k per organization, expanding with lifecycle features.
• Adoption expands within accounts over time (more lines/teams), moving the segment toward low single‑digit billions.

Who are some of their notable competitors

• Planck: AI‑derived underwriting data and risk insights from an applicant’s digital footprint to populate fields and apply knockout criteria; used to enrich/automate risk selection rather than full submission ingestion with citations (Planck).
• Cytora: Risk digitization engine that parses inputs, scores/triages risks, and produces decision‑ready submissions; overlaps on speeding decisions but focuses on risk scoring and triage over citation‑backed guideline validation (Cytora, GenAI overview).
• Hyperscience: Document automation platform for forms/emails/PDFs across industries; competes on extraction/intake but is general RPA/OCR rather than underwriting‑specific reasoning and guideline checks (Hyperscience).
• CAPE Analytics: Property‑level attributes and imagery‑derived signals (e.g., roof condition) for underwriting and pricing; a specialized data source rather than a full submission‑processing assistant (CAPE).
• Zesty.ai: AI property and climate risk analytics to improve underwriting and pricing; competes as a property risk‑data provider carriers plug into workflows, not a submission ingestion + citation tool (Zesty.ai).