Andrew McLeod on how Certn uses AI for background checks

Background checks are a trust problem. Every employer needs them, every candidate dreads them, and the infrastructure behind them is surprisingly brittle. Most background screening still depends on fragmented data sources, manual review steps, and workflows that haven't changed much in decades.

Andrew McLeod, from Certn, is working to change that. At HumanX 2026, WorkOS CEO Michael Grinich sat down with McLeod to talk about how Certn applies AI to background screening — making it faster, more accurate, and better for both employers and candidates.

The problem with traditional background screening

Background checks combine identity verification, compliance requirements, and trust decisions into a single workflow. Employers need to verify that candidates are who they say they are, that their credentials check out, and that there are no disqualifying records — all while staying compliant with a patchwork of local, state, provincial, and federal regulations.

The traditional process is slow. It involves pulling data from court systems, credit bureaus, educational institutions, and employment records — many of which don't have standardized integrations or interoperable formats. Results can take days or weeks, and errors are common. For candidates, the experience is opaque: submit your information and wait.

For companies scaling their hiring, this friction compounds. Every day a background check is delayed is a day an offer hangs in limbo.

How certn applies AI to the problem

Certn's approach is to apply AI across the background screening pipeline — from data collection and identity verification to risk assessment and compliance checks. McLeod explains how Certn uses machine learning to match records more accurately — for example, disambiguating common names across court databases — flag inconsistencies earlier, and reduce the manual review burden that slows down traditional providers.

The result is faster turnaround times. By automating the repetitive, rules-based parts of the process, Certn's team can focus human attention on the edge cases that actually require judgment.

This matters for developers and technical teams building hiring workflows. An API-first background check provider that returns results in hours instead of days changes what's possible in onboarding automation.

Trust infrastructure at scale

McLeod and Grinich also discuss the broader challenge of building trust infrastructure. Background screening is one piece of a larger puzzle: how do you verify identity, manage access, and maintain compliance across an organization?

WorkOS knows this territory well. Whether it's SSO, directory sync, or audit logs, the core challenge is the same — building the connective tissue that lets organizations trust their users and systems. Certn is tackling a parallel problem in the hiring pipeline, with the same underlying philosophy: make the hard stuff invisible so developers can focus on their product.

Where background checks are headed

The conversation closes with McLeod's perspective on where the industry is going. As AI capabilities improve and more data sources become accessible via API, the time between initiating a background check and receiving a result will continue to shrink. The companies that win will be the ones that treat background screening as a developer experience problem, not just a compliance checkbox.

For anyone building hiring, onboarding, or identity workflows, it's worth watching.

This interview was recorded at HumanX 2026 in San Francisco.