Recruiters are drowning. By some estimates applicant volumes have skyrocketed by 500%, meanwhile recruiting teams have actually shrunk, creating a perfect storm of too many applications and too few hands to process them. The result? Critical positions languish for weeks while qualified candidates are ignored by overstretched screening processes.
I recently spoke with Claire McTaggart from SquarePeg, whose team has built what they call an “AI-native” screening platform. Rather than simply layering AI onto existing Boolean search methods, they’ve reimagined candidate evaluation from the ground up.
The Resume Flood Is Real
Mid-market companies — those with 200-plus employees — face a particularly acute challenge. Unlike enterprise organizations with dedicated recruiting armies, these growing companies typically have lean talent acquisition teams handling multiple roles simultaneously. They’re managing “a mass influx of resumes, manually screening thousands of applicants, and trying to identify the best fit for their open roles” while dealing with increased hiring precision demands.
Companies need systems that can process applications rapidly without sacrificing quality — or worse, introducing bias into their screening decisions when recruiters are asked to do more with less.
Current Market Landscape
Here’s how the competitive landscape currently breaks down:
- Covey Scout: Uses LLMs and natural language processing for candidate evaluation, allowing recruiters to train custom bots based on their specific criteria. The platform focuses on both inbound screening and outbound sourcing, with recruiters describing evaluation criteria in their own words to generate custom bots that can process candidate profiles.
- HireEZ: Offers an all-in-one platform combining ATS, CRM, and sourcing capabilities with their “EZ Agent” for automated screening. The platform covers multiple recruiting functions including sourcing, resume screening, scheduling, and analytics in a single integrated solution.
- Findem: Built around what they call “3D data” from 1.6 trillion data points across 100,000+ sources. The platform specializes in sourcing and data enrichment, creating comprehensive candidate profiles with attributes ranging from technical skills to company experience patterns.
- Workable: Includes AI screening features within a broader ATS platform at $299 per month. The platform combines applicant tracking with AI-powered resume screening, job description generation, and interview management tools for in-house recruiting teams.
- Recooty: Provides AI scoring and candidate filtering capabilities with job description generation and multi-board posting features. The platform offers basic AI matching that compares resumes against job descriptions and generates candidate summaries.
The market includes platforms with various approaches: some focus on comprehensive workflow management, others specialize in data-driven sourcing, and still others emphasize customizable AI training for specific recruiting needs.
Three Technologies That Set SquarePeg Apart
SquarePeg’s approach differs in several key ways that deliver practical value for overwhelmed recruiting teams:
Data Enrichment Beyond the Resume
Instead of relying solely on what candidates include in their applications, SquarePeg automatically enriches every applicant with company intelligence that isn’t visible on resumes. This includes whether previous employers were venture-backed, high-growth, or went through IPOs, plus industry classifications, funding histories, and competitive landscapes. For roles requiring specific company experience—like candidates from regulated healthcare environments or fast-scaling startups—this enrichment provides signal that traditional resume parsing misses entirely.
Explainable AI Scoring
Unlike “black box” AI systems, SquarePeg shows exactly how each candidate score is calculated. When a candidate receives an 89% match, recruiters can drill down to see why: Python experience might score 80% due to skill decay (candidate hasn’t used it recently), while their PostgreSQL background scores 65% against a MongoDB requirement because both are database technologies with moderate transferability. This transparency helps recruiters understand unfamiliar skills and make informed decisions about candidate potential.
Real-Time ATS Integration with Fraud Detection
The platform syncs with applicant tracking systems every few minutes, automatically flagging fraudulent applications while stack-ranking legitimate candidates. Teams report clearing their applicant pools daily rather than letting candidates sit unreviewed for weeks. Built-in bias auditing by third-party firm WardenAI ensures compliance with legal requirements while maintaining screening effectiveness.
Who Actually Benefits From This Approach
SquarePeg’s customer base reveals patterns about which organizations gain the most value from specialized AI screening:
- Fast-Growing Tech Companies: Organizations experiencing rapid scaling need to process high application volumes while maintaining quality standards. AI companies particularly appreciate SquarePeg’s sophisticated approach—they understand the difference between genuine AI capabilities and basic automation.
- Mid-Market Companies (200-3,000 employees): These organizations face the “worst of both worlds”—enterprise-level hiring volumes without enterprise-level recruiting infrastructure. They need screening efficiency but can’t justify massive recruiting teams or complex procurement processes.
- Lean Recruiting Teams: Solo recruiters or small teams managing multiple roles simultaneously. The platform’s ability to provide hiring intelligence to non-technical recruiters—showing why TensorFlow differs from Keras or explaining DCF modeling skills—extends their effective expertise across diverse roles.
- Knowledge Worker-Focused Organizations: Companies hiring for roles where work history, skills progression, and company experience matter significantly. The data enrichment provides little value for high-turnover hourly positions but substantial intelligence for professional roles requiring specific backgrounds.
- Compliance-Conscious Organizations: Teams that need explainable hiring decisions and bias auditing. The platform’s transparency features and third-party bias monitoring address regulatory concerns while maintaining screening efficiency.
Market Position and Strategic Outlook
SquarePeg occupies an interesting position in the AI recruiting landscape. While competitors chase comprehensive platform strategies—trying to be everything from sourcing to onboarding—SquarePeg has focused intensively on one critical bottleneck: intelligently processing inbound applications.
This specialization strategy makes sense in a market where 98% of hiring managers believe AI has made tasks like screening, scheduling, and assessments smoother, yet many still struggle with AI tools that promise everything but excel at nothing.
The company’s partnership approach with customers—where each new client gets dedicated engineering resources and input into the product roadmap—reflects a mature understanding that AI recruiting tools need continuous refinement rather than one-size-fits-all solutions.
Their expansion into enterprise accounts represents the next logical step. Larger organizations have the application volumes that make sophisticated screening most valuable, but they also require the compliance capabilities and integration flexibility that SquarePeg has built from the beginning.
The real test will be whether their focused approach can compete with well-funded platforms offering broader functionality. However, for recruiting teams overwhelmed by application volume and seeking immediate relief rather than comprehensive workflow overhauls, specialized tools that solve specific problems exceptionally well often win over Swiss Army knife platforms that handle everything adequately.
As AI becomes table stakes in recruiting, the differentiation increasingly comes down to execution quality and practical utility rather than feature breadth. SquarePeg’s bet on depth over breadth positions them well for recruiting teams ready to move beyond basic automation toward genuine intelligence.
