Alex, a startup that’s developed an AI recruiter to help companies recruit job candidates ranging from junior positions to the most senior, has picked up $17 million in new funding. The Series A was led by Peak XV Partners, with Y Combinator and Uncorrelated Ventures also investing, along with a coterie of Fortune 500 chief human resources officers as angel investors. It comes after a $3 million seed round from 1984 Ventures.
The company says its system already holds structured conversations to confirm basic qualifications, compensation expectations and availability, and summarizes results for human recruiters.
The pitch is straightforward: use software to do repetitive screening so talent teams can concentrate on high-signal interactions.
Why Screening Is Ready To Be Automated at Scale
Volume is the pain point. Glassdoor estimates that for an average corporate job posting, you can expect hundreds of applicants, but only a few will make it to an actual interview. By contrast, SHRM has cited average time-to-hire in weeks and average cost-per-hire in thousands of dollars—numbers that all balloon for high-volume positions, where recruiters are forced to ask the same first-pass questions over and over again.
Automating the initial conversation offers measurable time savings: standard questions, 24/7 availability, quicker scheduling and standardized summaries sent to an applicant tracking system. For employers who onboard across shifts or locations — such as financial services, national restaurant chains and large professional services firms — those kinds of gains can mean shorter time-to-fill and a reduction in candidate drop-off.
What Alex Says It Does for Recruiters and Candidates
Alex’s leadership says its customers are unnamed Fortune 100 companies, financial institutions, nationwide restaurant groups and Big Four–adjacent employers. The company did not share logos, but described usage as growing more and more enterprise-grade. The CEO, Wang — who used to work at Facebook and also as a hedge fund quant — framed the product as a conversation engine that elicits a richer signal than a static resume.
Behind the scenes, these types of systems typically execute a structured script, dynamically tailor follow-up queries and determine if entities have been mentioned — number of years of experience, certifications and shift preferences. The result is a universally applicable rubric — think pass/fail on must-haves plus a ranked slate — that’s sent back out to tools like Greenhouse, Lever or Workday through integrations. It supports both voice and video, Alex says, so that employers can be flexible across hourly and professional roles.
Data Ambitions Beyond First-Round Screens
Alex is also making a longer-term play for a data-powered moat: interviewing millions of applicants to build richer professional profiles than its users could assemble on a resume or social profile. Wang contends that a quick, tight conversation surfaces nuance — scope of work, recent achievements, constraints — missed in static fields. If pulled off well, such a data set could power more accurate matching and internal mobility recommendations.
The approach also raises governance concerns. The establishment of background profiles from interviews requires full consent, specific retention duration, and purpose definitions. U.S. regulators have indicated an increased focus on black-box AI for employment decisions; the EEOC has published guidance on algorithmic fairness in hiring, and the FTC consistently highlights transparency and truth-in-advertising for AI claims. Any “data network effect” will rely as much on trust as model quality.
Compliance And Fairness Are Make-Or-Break
Local and international regulations are becoming more restrictive. Local Law 144, enacted in New York City, mandates bias audits and candidate notices for automated tools that make employment decisions. In the EU, new AI legislation identifies employment-related AI as high-risk and requires risk management, documentation and human oversight. Large employers are going to want vendors to lend assistance with adverse impact testing, provide audit artifacts and deliver human-in-the-loop controls.
Best practice is to co-design structured interviews with hiring teams, calibrate questions to bona fide job requirements and monitor outcomes by demographic. The vendors that bake in bias-testing coupled with explainable scoring, as well as an accessible candidate experience that provides accommodations and a clear opt-out, will be more prepared to win enterprise deals and not face surprises on compliance.
A Crowded Field and Divergent Tactics in Screening
Alex enters a busy arena. Early-stage competitors HeyMilo, ConverzAI and Ribbon are hunting the same automation, while entrenched recruitment platforms and conversational AI tools are expanding into screening too. Some vendors favor text chat for scale, while others emphasize asynchronous video to assess communication skills. The lessons of history are also instructive: video interview providers from a previous generation faced pushback when features spilled over into invasive territory, leading this wave to place job relevance and fairness front and center.
The wager of investors is that screening will standardize around AI much as tracking applicants did a decade ago. Over the last several years, LinkedIn’s talent research has consistently uncovered that most recruiting leaders predict artificial intelligence to have a significant impact on their workflow. Combining measurable time savings with compliant, candidate-friendly design — and converting interview data back into demonstrably better hiring decisions — will give Alex a credible path to scale.
What To Watch Next as AI Screening Scales
Procurement lists are starting to look very similar: documented bias audits, the depth of integration with major ATS vendors, multilingual support, and clear data retention policies. Model benchmarks will matter less than referenceable enterprise customers. The outcome in the battle for talent may also come down to how reports summarizing an interview are crafted — ones that are cost-effective, evidence-based and evenhanded enough not to be re-run by hiring managers.
For now, Alex has fresh capital, an articulable wedge and a story that resonates with time-taxed talent teams. The question is execution: Can an AI recruiter ever feel fair, reliable and helpful to candidates — and companies — at scale? If the answer is yes, a bot may start you off on your first interview — and conclude with a faster human yes.
