Fundraising Fox
RAKE ML
Techstars '25Weston, US · 9 employees on LinkedIn · 1 known investors
Find your way into RAKE ML
RAKE ML predicts commercial roof failure timing using imagery, climate data, repair history, and physics-informed simulation to help insurers, lenders, property owners, and brokers make asset-level underwriting and capital decisions. The company serves the commercial property insurance and lending markets by providing time-bound failure probability assessments that replace age-based underwriting proxies.
Also known as RAKE · RAKE ML
Investors · 1
How we know: the Techstars portfolio · open dataset · Top 3000 Accelerator Startups (no YC) 2026-08 · Not right? Tell us
Company profile
researched Sep 2026RAKE ML develops what it calls "physical underwriting" software for commercial property: instead of scoring a roof by age or condition grade, the platform returns a dated failure-probability curve, a confidence interval, and a recommended action for a given address or portfolio. Inputs include street address and roof area, aerial and satellite imagery, inspection history, weather and climate exposure, repair records, and contractor outcome feedback. The company positions the output as audit-ready evidence usable in underwriting files, asset management plans, and credit review.
The product workflow has four stages: ingest of asset and exposure data; simulation of material degradation, thermal cycling, drainage failure, membrane mechanics, hail, UV, freeze-thaw and precipitation; scoring via a causal model that returns a time-bound failure probability curve with confidence interval and audit trail; and a learning loop in which contractor repair events, work orders and replacements recalibrate the model against verified outcomes. RAKE ML states it does not replace physical inspection, but prioritizes and triages inspections. An illustrative sample report on the company site shows a 124,000 sq ft Dallas commercial roof with 87% model confidence, a 71% 12-month failure probability, a predicted failure window of months 7-11, a recommended full replacement in Q3, and a $1.74M cost if deferred 12 months; the company labels this as illustrative rather than a live asset report.
A third-party private-market profile categorizes the company as business/productivity software in vertical SaaS, facility management, ML platforms and synthetic data, with a B2B model and subscription SaaS plus services/consulting customer profile.
Business model
B2B software sold to commercial property risk stakeholders, characterized externally as subscription SaaS combined with services and consulting. Engagement typically begins with a scoped portfolio or single-asset assessment, an underwriting and credit workflow review, and pilot design, after which the buyer can judge signal usefulness before broader deployment.
Described externally as subscription SaaS with services and consulting components; the website's commercial entry point is a scoped portfolio or single-asset assessment leading to pilot deployment.
Traction
Headcount reportedly grew from 3 to 6 employees quarter-over-quarter as of late December 2025, with an employee range of 7 recorded in a private-market profile; momentum on that platform is ranked in the top 50% of tracked companies. Public material otherwise shows pre-deployment activity: scoped portfolio assessments, workflow reviews and pilot design offers.
Latest developments
Accelerator participation dated December 5, 2025 is recorded on a private-market profile, with cumulative funding of $220,000 reported there; a February 2026 firmographic signal notes headcount expansion.
▸Full profile — market position, technology, go-to-market, geography, history
Market position
An early-stage entrant in property risk analytics spanning InsurTech, PropTech and facility management software, described externally as an application-layer AI company with keyword coverage including asset-level intelligence, predictive maintenance, digital twin, synthetic data and AI underwriting. A third-party comparables model lists Moody's, RELX and Autodesk as similar-embedding companies rather than direct competitors.
Positions against roof-age proxies and static condition grades: rather than a condition score, the output is failure timing under an asset's actual material, climate, drainage, repair and exposure conditions. The company argues that timing changes the decision — an expected failure in 8 months is an underwriting action, while a 24-month horizon is a pricing, reserve or CapEx decision. Supporting claims cited from the company's deck reference $31B/yr in U.S. commercial roof claims paid, $1.31B/yr of premium leakage from misclassified roof age, and roughly +/- 18 months of accuracy for age-based actuarial proxies, attributed to CIAB 2024, Verisk roof-age product data and RAKE benchmark work.
Technology
The system fuses observable asset data with physics-informed simulation. Because major roof failures are too rare in most portfolios to train on claims alone, RAKE ML uses synthetic data generation plus field outcome feedback to model roof aging, degradation and failure. Component-level modeling covers material degradation, drainage failure, membrane mechanics and thermal stress; climate calibration layers hail, UV load, freeze-thaw, wind and extreme precipitation against geographic exposure. A causal model produces a time-bound failure probability curve with a confidence interval, and every score is tied to its data inputs, confidence, timing and recommended action.
Go-to-market
Inbound demand generation through the company website, which routes prospects by role (owner, insurer, lender, broker) into a portfolio risk assessment request form or a 30-minute call. The first deliverable is a scoped preliminary condition signal on a submitted address, collateral pool, underwriting book or owner portfolio, positioned as a precursor to a pilot.
Commercial property owners and asset managers (repair-versus-replace and CapEx timing), insurers and underwriters (submission screening, pricing load, non-renewal timing, claims preparation), lenders and servicers (collateral condition monitoring, reserve adequacy, covenant and workout risk), and brokers and risk advisors (placement, renewal and submission quality).
Geography
Headquartered in Weston, Florida, United States, with product framing and market sizing oriented to U.S. commercial property claims and underwriting.
History
Founded in 2024 per a private-market firmographic profile, with accelerator involvement recorded in September and December 2025 alongside Techstars and RevTech Labs.
Compiled by commissioned research from 5 cited public sources — announcements, filings, and press listed under research sources below.
Key figures
latest reportedCompany-reported or press-reported figures, each dated to when it was claimed — not independently audited.
Competitors · 1
by search overlap
Companies competing with RAKE ML for the same Google search keywords, organic and paid, via search-intersection analysis.
Timeline · 2
launches, deals, and filingsA Caplight company profile lists an accelerator round dated December 5, 2025, with Techstars, RevTech Labs Capital and RevTech Labs named among investors across the company's accelerator rounds.
A Caplight company profile lists a second, earlier accelerator round dated September 12, 2025.
Dated company events from announcements, filings, and press; legal rows summarize public dockets and regulator releases.
▸Research sources · 5
primary sources listed
- RAKE MLrakeml.com · web
5 public sources were cited for this profile; the first-party ones are listed here.
Frequently asked questions
- What does RAKE ML do?
- RAKE ML predicts commercial roof failure timing to support insurance underwriting, lending, and CapEx decisions.
- Who are RAKE ML's investors?
- RAKE ML's investors include Techstars.
- Where is RAKE ML headquartered?
- RAKE ML is headquartered in Weston, US.