The New York proptech automates rental applications end to end - verifying income, flagging fraud, and returning a data-grounded yes or no in hours, not days. It now screens more than 400,000 homes.
FINDIGS, INC. - The wordmark's cursive "f" doubles as an arrow, a nod to helping renters find their next place. Est. 2018, 636 Broadway, New York.
For decades, deciding whether to rent someone an apartment came down to a three-digit score and a leasing agent's gut. Findigs, a New York company founded in 2018, argues that the score was always the wrong tool - because it tells a landlord a number, not whether the rent gets paid. Its answer is to run the whole screening process itself and hand operators an actual decision.
Findigs is an AI-native resident screening platform for residential property operators. When a prospective renter applies, the software collects and verifies the application from start to finish - identity, income, documents, credit, eviction and criminal history - and returns a data-grounded recommendation to approve or decline. The company frames its own value plainly: it automates tenant screening "to save time, speed decisions, and cut delinquency."
The distinction Findigs draws is between a screening score and a screening decision. Legacy tools surface data and leave the judgment to a human. Findigs ties its model to what happens after the lease is signed - whether a tenant actually pays and stays - and automates the yes-or-no. As the company puts it: "We don't just support your screening process, we run it from start to finish."
That approach shows up in speed. The platform reports a median application-to-decision time of about 3.4 hours, and roughly 45% of decisions are made outside business hours - when renters actually apply and legacy back offices are closed. Operators say it frees leasing agents about eight hours a week.
Findigs also does something unusual for a software vendor: it stands behind the decision with a contractual fraud guarantee, shifting a slice of application-fraud risk off the operator and onto itself.
Findigs sells to U.S. residential operators and property managers - hundreds of them, covering more than 400,000 units across single-family rentals, multifamily buildings, affordable housing, and student housing.
The pitch is "occupancy that pays" - full buildings that actually collect rent, without trading away fairness or speed.
*Delinquency reduction reflects Findigs' portfolio-wide "up to" figure, not McKinley specifically. Bars scaled for comparison.
Renting in America runs on a trade-off operators know well: push for high occupancy and you risk approving tenants who fall behind; tighten standards and units sit empty. Traditional screening does little to break that tension, because it hands back raw data - a credit score, a background report - and asks a human to guess at the outcome.
Meanwhile, applicants wait. A report that lands on a Friday can leave a renter in limbo until Monday, and a good applicant may sign elsewhere. On the other side, application fraud - doctored pay stubs, borrowed identities - has grown into a real cost for landlords.
Findigs targets all three seams at once: the occupancy-versus-quality trade-off, the speed gap, and fraud. By automating verification and grounding decisions in post-lease performance, it aims to give operators "occupancy that pays" rather than simply filling units.
The Series C sharpens the focus on the hardest corner of the market: affordable housing. New funds are earmarked for LIHTC and Section 8 workflow support - compliance-heavy processes most screening vendors avoid - plus Rent Guarantee products that protect revenue across a full lease term.
The core engine: processes an application end to end and returns an instant, data-grounded approve/decline recommendation.
Behavioral-AI fraud detection backed by a contractual guarantee that moves risk off the operator.
Multi-path identity checks (including international IDs) plus verification of traditional and non-traditional income.
Automated document analysis and validation, with pet and assistance-animal verification built in.
Extends Findigs beyond screening into lease and term management workflows.
New products that protect operator revenue across the full lease term, launched with the Series C.
Competitively, Findigs sits against legacy screening and background-check providers such as TransUnion SmartMove and RentSpree, point tools like Snappt for fraud detection, and screening bundled into property-management platforms including AppFolio, Yardi, and RealPage. Its wager is that operators will prefer a single system that owns the decision - and guarantees it - over a stack of point tools.
Findigs is a B2B SaaS company. It sells subscription and per-application screening and decisioning software to residential operators, then layers revenue-protection products - the fraud guarantee and, now, Rent Guarantee - on top. Third-party estimates put annual revenue in the mid-eight figures, though the company has not disclosed official numbers.
It sits at the intersection of three markets investors like: property technology, financial services, and applied AI. Rental underwriting is, at its core, a credit-and-fraud decision - which is why the platform's stack reads as much fintech as proptech, and why funds like Nyca Partners (a fintech specialist) and RPM Ventures have backed it repeatedly.
Where it fits: Findigs is positioning itself as the decision layer of the rental stack - the system that sits between a renter's application and an operator's lease, and increasingly extends into leasing and revenue protection. Scaling past 400,000 units gives it the performance data it argues competitors lack.
Findigs was cofounded by Steve Carroll (CEO) and Keith Gilvar, who met on their very first day at Colby College. As Carroll tells it, they ended up at the same table in the dining hall "because our mothers were both concerned that we wouldn't make any friends." Nine years later, they started a company.
Before Findigs, Carroll began his career on Wall Street at Nomura Securities and cofounded Seated, a hospitality startup acquired by OpenTable in 2018 - the same year he and Gilvar decided the rental idea they'd sat on had gone unbuilt long enough. "Four years went by, and I said, 'Nobody has really done anything with this idea,'" he recalled.
Colby classmates Carroll and Gilvar launch Findigs in New York to fix the rental application.
Identity, income, and document verification are built out for residential applicants.
Nyca Partners leads a round to reinvent the rental experience and restore operator-renter trust.
Findigs extends into leasing workflows and deepens operator integrations.
RPM Ventures leads as Findigs surpasses 400,000 units and launches Rent Guarantee.
"We met because our mothers were both concerned that we wouldn't make any friends."
Steve Carroll, on meeting cofounder Keith Gilvar"Four years went by, and I said, 'Nobody has really done anything with this idea.'"
Steve Carroll, on deciding to start Findigs"The only product we've seen that rebuilt the decision itself."
Marc Weiser, RPM VenturesFindigs runs residential tenant screening and leasing decisions end to end - verifying identity, income, and documents, detecting fraud, and returning fast, data-grounded approve/decline recommendations for property operators.
It was founded in 2018 by Steve Carroll (Co-Founder & CEO) and Keith Gilvar, who met as students at Colby College.
About $80 million total, including a $32M Series C in June 2026 led by RPM Ventures and a $27M Series B in 2024 led by Nyca Partners.
Operators report up to 80% fewer evictions, up to 90% lower delinquency, a median decision time of about 3.4 hours, and roughly 8 hours saved per leasing agent each week.
Instead of returning a score, Findigs rebuilds the decision itself using AI tied to post-lease performance, runs the process start to finish, and backs it with a contractual fraud guarantee.
Sources: Findigs.com, GlobeNewswire, Commercial Observer, Inman, FinSMEs, BusinessWire, Colby College News, Crunchbase, PitchBook. Figures such as revenue and headcount are third-party estimates and may be approximate. Compiled from public sources.