Checkr Company Profile: Revenue, Business Model & Strategy

What Does Checkr Do?

Checkr provides an integrated trust, safety, and background screening platform that automates candidate verification across the entire employee and contractor lifecycle:

• Automated Criminal Background Checks: Instant querying of national, federal, and local county court records across all 3,000+ US counties, using NLP to disambiguate common names, match charges, and filter out records that are not legally reportable.
• Motor Vehicle Records (MVR) & Continuous Monitoring: Real-time driving record verification across all 50 state DMVs, coupled with continuous monitoring that alerts employers immediately if an active driver incurs a DUI or moving violation.
• Identity Verification & SSN Trace: Automated facial selfie biometric comparison, government ID document validation, and Social Security Number (SSN) address history trace to prevent synthetic identity fraud.
• Candidate Stories & Fair Chance Tools: Candidate portal that allows applicants with prior criminal history to provide context, letters of recommendation, and evidence of rehabilitation directly to hiring managers before adverse decisions are made.
• GoodHire for Small Businesses: Turnkey, self-serve background screening platform specifically optimized for small-and-medium businesses (SMBs) with zero setup fees.
• Checkr Pay: Instant wage settlement rail that allows on-demand platforms and staffing agencies to disburse daily worker earnings directly onto branded Checkr debit cards upon shift completion.

How Does Checkr Make Money?

Checkr operates a highly profitable, high-volume transactional API fee and recurring subscription software business model:

1. Per-Check Transaction Fees ($15 to $50+ per background check): Core revenue stream charged to corporate clients for each completed background screen, scaling with package depth (basic SSN trace, multi-county criminal searches, federal records, motor vehicle records, and drug testing).
2. Checkr Trust Continuous Monitoring SaaS Fees ($1 to $3 per worker/month): Recurring monthly subscription fees charged to rideshare and delivery platforms for continuous, real-time driver license and criminal arrest monitoring.
3. GoodHire SMB Subscriptions: Tiered package pricing charged to over 50,000 small-and-medium businesses for turnkey pre-employment screening.
4. Drug Screening & Health Verification Fees: Additional fees charged for coordinating in-person 5-panel and 10-panel electronic occupational drug testing through partner laboratory networks (Quest Diagnostics, Labcorp).
5. Checkr Pay Interchange Fees: Transaction fees and interchange captured when workers spend funds disbursed onto Checkr corporate debit cards.

Origins: Frustration at Deliv & The Historic Uber Contract

The institutional story of Checkr began in 2013 inside the engineering room of Deliv, an early same-day retail delivery startup in Silicon Valley. Daniel Yanisse and Jonathan Perichon were young French software engineers tasked with scaling Deliv's driver onboarding. The two were stunned by how broken background checks were: every time a driver signed up, Deliv had to manually enter their information into legacy screening portals and wait three weeks for a response. Yanisse and Perichon looked at the legacy screening giants and realized they weren't technology companies; they were low-margin services agencies using manual labor.

Yanisse and Perichon quit Deliv and were accepted into Y Combinator's Winter 2014 batch. Over three intense months, they built a REST API that programmatically scraped and parsed digitized court records. In late 2014, having just launched, they landed the ultimate prize: Uber Technologies was experiencing hyper-growth and was being strangled by legacy background check delays. Uber tested Checkr's API against legacy vendors: Checkr completed checks in hours instead of weeks with higher accuracy. Uber moved its entire US driver screening volume to Checkr. Overnight, Checkr became the trust and safety backbone of the gig economy, powering screening for Lyft, DoorDash, Instacart, and Postmates.

Fair Chance Hiring: Building Software for Second Chances

While Checkr achieved multi-billion-dollar commercial success powering gig economy platforms, its most enduring historical legacy is its pioneering leadership in Fair Chance Hiring and Criminal Justice Reform. In the United States, an estimated 70 million adults, nearly one in three working-age individuals, have an arrest or conviction record, often for minor, non-violent offenses committed decades earlier. Historically, legacy background check companies slapped a giant red 'FAILED' stamp on these applicants, permanently locking rehabilitated individuals out of the formal economy.

Daniel Yanisse believed that software could restore human empathy to criminal justice. Checkr engineered Candidate Stories, an influential feature embedded into the Checkr candidate portal. When an applicant's background check uncovers a past record, the candidate is invited to submit a personal explanation: they can explain their journey, upload letters of recommendation, and provide certificates of rehabilitation. Checkr displays this human context directly alongside the legal record to the hiring manager. Checkr built automated compliance rules that filter out old, non-relevant offenses (such as cannabis possession in states where cannabis is now legal). Checkr's tools are designed to help individuals with past arrest records secure fair employment, proving that technology can be a powerful engine of societal redemption.

Disambiguating 3,000 County Courts: The Machine Learning Record Normalization Engine

To appreciate why automated background screening in the United States was historically considered an impossible engineering problem, one must examine the chaotic fragmentation of the American judiciary. Unlike European nations that maintain a single centralized national criminal justice database, the United States has no unified criminal repository. Instead, criminal records are held across more than 3,000 independent county courthouses, 50 state administrative court offices, and 94 federal district courts.

Each of these thousands of county courthouses uses different software systems, incompatible case docket naming conventions, and arcane local penal codes. court dockets frequently list only a defendant's first name, last name, and date of birth, omitting Social Security Numbers to protect privacy. If a background check system searches for 'Michael Smith' in Cook County, Illinois, it might uncover 500 distinct criminal dockets. Legacy screening companies relied on human data-entry clerks who frequently misattributed criminal records to innocent job applicants with common names. Checkr permanently solved this through its Machine Learning Record Normalization Engine. Checkr's algorithms ingest disparate court dockets, cross-reference partial identifiers (address histories, middle initials, historical driver's license numbers), and evaluate probabilistic matching scores. If a match score meets a 99.9% certainty threshold, it is normalized into plain English; if ambiguity remains, the file is routed to certified compliance specialists for human review. This hybrid AI-human architecture eliminated false positives, delivering sub-second accuracy across millions of records.

Continuous Trust: How Real-Time Arrest Webhooks Replaced Static Annual Checks

In traditional corporate workforce risk management, background screening was treated as an isolated, static event that occurred exactly once: on the day an employee was hired. In high-risk operational environments, such as on-demand rideshare driving, commercial trucking, home healthcare, and school transportation, this static paradigm created large, alarming safety blind spots.

If an active rideshare driver or delivery courier committed a severe violent crime, was arrested for felony driving under the influence (DUI), or had their driver's license suspended on Tuesday, their employer had zero knowledge of the event until their scheduled annual re-screen twelve months later. Checkr eradicated this safety blind spot by engineering Checkr Trust and Continuous Workforce Monitoring. Checkr established real-time API integrations with state departments of motor vehicles (DMVs) and municipal law enforcement booking data feeds across the country. The moment an active driver or field worker commits a major moving violation or is booked on an arrest, Checkr's engine detects the record within minutes and dispatches an automated webhook alert directly to the employer's trust and safety dashboard. Corporate safety teams can instantly suspend the worker's account pending investigation, preventing dangerous incidents before passengers or customers are put in harm's way.

Acquiring GoodHire: Expanding Enterprise-Grade Screening to 50,000 SMBs

While Checkr conquered the technology and on-demand gig economy by winning contracts with leaders like Uber, Lyft, and DoorDash, traditional small-and-medium businesses (SMBs), such as local auto dealerships, regional retail chains, and dental offices, were still trapped using slow, expensive legacy screening agencies. Small businesses did not have software engineering teams to integrate Checkr's developer APIs; they needed a simple, self-serve web portal where an office manager could run a compliant background check with zero technical setup.

In April 2022, Checkr made a transformative strategic acquisition, purchasing GoodHire from Inflection LLC. GoodHire was an acclaimed, highly rated background screening platform built specifically for small businesses, serving over 50,000 small business customers across America. Checkr smoothly migrated GoodHire's customer base onto Checkr's powerful, automated backend infrastructure. Small business owners gained access to Checkr's lightning-fast court record pipelines and Fair Chance compliance tools through GoodHire's intuitive web portal, while Checkr dramatically expanded its high-margin SMB revenue and diversified its customer base beyond the gig economy into mainstream American business.

The French Engineers in SoMa: How Daniel Yanisse and Jonathan Perichon Built Checkr

The institutional story of Checkr is a remarkable sign of the immigrant software engineering culture of Silicon Valley. When Daniel Yanisse and Jonathan Perichon arrived in San Francisco from France, they had no top venture capital connections, no background in human resources, and no family ties to the American legal system. What they possessed was top algorithmic training from top French engineering institutions (École Centrale Paris and Epitech) and a deep, intellectual obsession with eliminating administrative friction.

While working at on-demand delivery startup Deliv in 2013, the two engineers were fascinated by how much money and time was wasted on manual background checks. They realized that the background check industry was ripe for the exact same disruption that Stripe brought to credit card processing and Twilio brought to telecommunications: taking a messy, human-intensive, heavily regulated industry and wrapping it in three lines of beautiful code. When they were accepted into Y Combinator's Winter 2014 batch, they rented a tiny apartment in San Francisco's SoMa district, writing code by night and pitching on-demand founders by day. Their technical precision, intellectual modesty, and focus on software automation transformed two young French engineers into the architects of an essential multi-billion-dollar national security and employment utility.

Checkr International: Navigating Global Privacy and Criminal Records in 200+ Countries

In modern enterprise human resources, multinational corporations hire software engineers, sales directors, and customer support representatives across North America, Europe, Latin America, and Asia-Pacific. However, conducting employment background checks internationally presents a large legal and operational challenge: sovereign data privacy regulations, such as the European Union's General Data Protection Regulation (GDPR), strictly prohibit the transmission and processing of criminal history data without explicit statutory legal grounds.

Checkr solved this cross-border compliance dilemma by creating Checkr International. Headquartered in London, Checkr International established direct data access protocols with official police ministries, national government identity registries, and accredited educational credential verification agencies across more than 200 sovereign nations and territories. In Europe and the UK, Checkr coordinates directly with the UK Disclosure and Barring Service (DBS) and national criminal registers while strictly adhering to GDPR data minimization standards. Multinational enterprises can initiate background screens for international candidates using the exact same REST API endpoints and dashboard used for domestic US screening, providing global corporate recruiters with a unified, legally compliant screening infrastructure across every continent.

Checkr Pay: Merging Background Screening and Instant Worker Earnings

For on-demand gig platforms, delivery services, and contingent staffing agencies, retaining flexible workers is a continuous battle. Independent workers frequently migrate between competing delivery and rideshare apps based on which platform offers faster access to their earnings. Historically, platforms paid workers through traditional bi-weekly direct deposits or third-party payout tools that charged steep instant-cash-out fees.

Checkr transformed flexible worker retention by debuting Checkr Pay. Checkr Pay unites background screening, digital onboarding, and instant earnings disbursement onto a single platform. When a prospective driver or delivery worker passes their Checkr background check, they are instantly provisioned with a virtual or physical Checkr commercial debit card. The moment a worker completes a delivery shift or rideshare trip, their earnings are disbursed directly onto their Checkr card in real time with zero transaction fees. By providing instant liquidity to workers while giving platforms automated tax reporting and compliance management, Checkr Pay transformed background checks from a one-time onboarding expense into an active worker retention engine.

The Denver Engineering Hub: Building Scalable Identity Trust Infrastructure

As Checkr's screening volume scaled past millions of background checks, the company encountered an imperative to scale its systems engineering, data science, and customer compliance operations beyond its founding headquarters in San Francisco. Coastal operational costs and fierce competition for software talent prompted leadership to establish a second major operational center.

In 2018, CEO Daniel Yanisse selected Denver, Colorado, as Checkr's second major headquarters and primary engineering hub. Located in downtown Denver, the campus grew into an engineering and customer advocacy center housing hundreds of data engineers, machine learning scientists, and FCRA-certified compliance specialists. The Denver engineering team took ownership of Checkr's core machine learning record disambiguation models, building high-throughput data processing pipelines capable of querying thousands of municipal court databases simultaneously with sub-second latency. This geographic diversification provided Checkr with exceptional operational redundancy and access to premier engineering talent, powering the platform's multi-billion-dollar scaling trajectory.