Every commercial submission arrives as a mess of ACORDs, loss runs, subcontracts, safety manuals, and broker emails — and underwriters spend 1-2+ hours per submission wrangling it before making a single decision. Speed to Market AI compresses that to 10-15 minutes.
Three numbers. One structural gap. Speed to Market AI is built to close it.
It's the work that happens before any decision — document wrangling, extraction, guideline checks, workbook prep, email drafting. None of it judgment. All of it the bottleneck.
Why it matters at renewal cycles. Broker’s #1 complaint every 1/1, 4/1, 7/1, 10/1 is turnaround. At peak volume the fastest quote wins. At off-peak the saved hours are redeployed into the exceptions that actually need human judgment.
The compounding advantage. AI works twenty-four hours a day, seven days a week. It doesn’t take sick days. It doesn’t burn out, it doesn’t complain no matter how busy the season. It doesn’t need months of ramp-up to learn a new line of business or a new appetite — a prompt edit and it’s ready the same day. Applied consistently across a book, its accuracy holds steadier than a rotating team working across time zones, and every submission it processes sharpens the next one rather than adding to attrition risk. Humans still own the judgment. AI owns the repetition.
Drop the broker email into the queue. The platform reads the packet, organizes thousands of pages into the few dozen that matter, and fills out the underwriting system with exposures, fleet breakdowns, underlying limits, premiums — everything. You make the decisions: price, forms, terms.
All that’s left: price it·pick forms·send the quote.
Every screen below is the working product as deployed at speedtomarketai.com. The pipeline runs in real time. The file manager organizes them by categories. The workbench fills itself out from extracted data — with every field tagged by the module that extracted it.
Each module is a specialist — ACORD reader, loss history extractor, subcontract agreement parser, GL/AL quote analyzer, and many more. They fire in parallel the second the submission lands, each citing the exact source page it pulled from. By the time you take a few sips of your coffee, the entire packet has been read and verified.
12 built-in categories color-code the entire packet — Loss History in red, Applications in green, Underlying in yellow, the rest matching the real-world doc taxonomy you already use. Thumbnails let you scan a 3,000-page submission visually in seconds down to only the pages needed. Manual renames stick across runs — rename anything once and the override persists.
Named insured, addresses, broker contacts, GL/AL/EL/EBL coverages, fleet breakdown, GL exposure rater, underlying limits and premiums, the full excess tower, loss history with paid/reserve/incurred reconciled across LOBs. Every field tagged by the module that extracted it (A3, A8, A9 …) so you can trace any value back to the source page in one click.
Each module owns a slice of the submission. Run them independently as a specialist assistant, or chain them into the full consolidation.
web_search to find the site.GL, Auto, Workers' Comp, Excess. Speed to Market AI was built by an underwriter who has sat in the clearance seat, the assistant seat, and the underwriter seat — across every stage of the commercial intake funnel. The use cases below are just examples but can do way more.
AI agents run overnight against every submission in the queue. By the time the underwriter opens the laptop, a fully structured queue and summary email are waiting — strengths, exposures, class codes, limits, loss trends, guideline flags, open items. No more 7am document triage.
Every submission cross-referenced against your underwriting guide. Prohibited, Restricted, and Referral classifications flagged with verbatim citation. The compliance team gets pre-bind flags in real time — no more post-audit surprises.
Subcontractor agreements, vendor contracts, lease agreements — the contracts that determine whether losses flow down or stay with your insured. The platform extracts every risk-transfer provision across any commercial line and flags gaps before bind.
Loss runs arrive in every format carriers can produce. The platform normalizes GL, Auto, WC, and Umbrella claims into a single structured view — with penetration analysis, severity trending, and fraud/edit detection built in.
Generic AI underwriting tools built by ex-Google ML teams miss the texture of the job. Speed to Market AI was built by a working underwriter solving his own desk problems — 13 years across clearance, assistant, and underwriter roles. What that looks like in practice:
Most AI underwriting tools were built by engineers talking to underwriters. This one was built by the underwriter.
web_search tool for insured websitesEvery question a CISO asks — answered from day one. Every question a regulator asks — logged. Built for private VPC or on-prem deployment, with SOC 2 on the roadmap and NAIC/NYDFS-aligned vendor packages ready.
Speed to Market AI starts at the underwriting prep layer because that’s where the hours are being lost today. The arc is bigger. Three horizons, each anchored to work already in-flight.
Live at speedtomarketai.com. Browser-based, nothing to install. Handles ACORDs, loss runs, subcontracts, vendor agreements, safety manuals, and broker emails — PDF, Excel, or Word in; full Excel pack plus summary email out. Pilot-ready today.
From prep to pricing guidance. Class-by-class severity models. Attachment optimization. Quote automation for low-complexity refers. The same underwriter judgment, executed at 10x the pace.
The infrastructure layer between brokers and carriers for the commercial market. Multi-carrier routing. Portfolio intelligence. Claims feedback that makes every future submission smarter than the last.
Each horizon is a natural extension of the underwriter workflow — not a pivot. The data you capture in the prep layer is the exact data that feeds pricing, portfolio analytics, and eventually the cross-carrier infrastructure layer. Same product, widening surface area.
◆ QUOTES DISTILLED FROM ONGOING ADVISOR AND DESIGN-PARTNER CONVERSATIONS. NAMED ATTRIBUTIONS WILL BE ADDED AS PILOT AGREEMENTS ARE EXECUTED.
Justin Wray, CPCU has spent 13 years in underwriting — ground-up, from a clearance seat to Executive Casualty Underwriter — plus 5 years applying AI to underwriting workflows and guideline automation. He carries the CPCU designation (completed 2024), the industry’s gold-standard technical credential. Every module on this platform automates a task Justin personally performed thousands of times. Nothing ships until it would survive his own audit.
He is already leading AI underwriting work at his carrier: machine-learning models for operational summaries and exposure identification, automated business classification, and contract interpretation for indemnification, hold-harmless, and subcontractor limits. Speed to Market AI is the productization of those patterns — hardened for any carrier.
Every rung of that ladder is now a module on this platform — from intake through senior-level analysis. The person who built it knows exactly how the job is done today, because he still performs the job today. Transitioning to full-time with first pilot conversions — an operator-founder profile for an operator-defined problem.
Onboarding a small group of commercial insurance teams for the closed pilot. Tell us your book and we'll run a walk-through on your actual submissions — see the hours saved on your own accounts.