SquarePeg connects to your ATS and scores every applicant against your criteria, with the resume evidence behind each score. It also flags candidates who may not be authentic, adds company context to every resume, resurfaces past applicants who fit new roles, finds new candidates outside your ATS, and lets you ask an agent about your pipeline in plain language.
Most teams connect to their ATS and review a ranked shortlist in minutes. Initial ingestion times depend on the volume of applicants in your ATS.
Create a free SquarePeg accountAPI keys for your ATS (Admin recommended for a smooth integration)
Enrichment adds context a resume leaves out. Each company in a candidate's work history comes with a description, industries, year founded, employee count, public or private status, revenue and latest funding. Your criteria can use it, so you can look for things like "experience at a venture-backed cybersecurity startup".
New accounts get 2,000 free credits to use within 2 months, with no credit card. One credit scores one applicant, so you can try scoring, authenticity checks and enrichment on your real jobs.
Credits work across any number of jobs. When you need more, buy credits one-time, monthly or yearly, with volume discounts.
No. You pay for credits and nothing else: no platform fee and no seat fees. Rescoring and the agent are free, and applicants who fail your eligibility rules are never charged.
As many as you like. Invite recruiters and hiring managers as Admins, Users or Viewers, and give Users and Viewers access to specific jobs. Pricing is based on credits, not seats.
The new SquarePeg is rebuilt around your ATS: it scores and enriches every applicant, checks authenticity and explains each score, instead of being a separate system you hire in.
It cuts manual resume review, surfaces qualified applicants faster, flags fake or misrepresented candidates, adds company context to resumes, brings back strong past applicants, and applies the same criteria to every candidate.
A hybrid stack: frontier LLMs for language reasoning, proprietary scoring algorithms, embeddings for semantic match, enrichment from a 30M-company dataset, and fraud heuristics.
It excludes PII (name, contact info, school names, photos, demographics) and focuses on job-relevant signals like skills, titles, tenure, industries, company attributes, certifications, and outcomes.
SquarePeg doesn't penalize resumes for being written with AI. It flags resumes that mirror your job description too closely in structure and wording, which can mean a resume was tailored to game the posting. Like every authenticity signal, it's there to inform your review, not to reject anyone.
SquarePeg works across industries and company sizes, but is recommended for companies hiring knowledge workers (white collar) where candidates apply with a resume. Most of our customers are high-growth companies in tech, finance, and healthcare., SquarePeg works best when applicant volume is high, fraud is a concern, or teams are lean.
Many employers choose to disclose that they are using AI to screen resumes. Using SquarePeg actually means every profile will be thoroughly reviewed and matching takes all the context of a resume and candidate’s work history into account, vs a keyword search. provides a transparent rationale and audit logs so you can disclose AI use clearly in your own candidate communications.
Yes. In addition to our free tier, SquarePeg offers a 90 day paid pilot so teams can assess ROI across all of their roles. SquarePeg will provide you with an ROI dashboard during the pilot so you can see key metrics needed for assessing pilot success.
You can disconnect your ATS from SquarePeg at any time, and your data will be deleted within 90 days.
SquarePeg combines signals from four sources into an authenticity confidence rating (High, Mixed, Limited, Low or Unknown). LinkedIn: whether the profile exists in a 400M+ profile database, its connections and photo, and whether the name and work history match the resume. Phone: invalid, VoIP, toll-free or voicemail-only numbers, low activity, and (in the US) the registered name. Email: disposable addresses, no public history, or a different registered name. Resume: excessive alignment with the job description. It also flags a phone, email or resume shared with a differently named applicant. No single signal rejects anyone; you decide.
Eligibility rules run first, so applicants outside your location, time zone or screening requirements are set aside without being scored. Everyone else is scored against each of your criteria from 0 to 100, with a written explanation and the resume lines it's based on. Criteria are weighted Required (3x), Preferred (2x) or Bonus (1x), recent experience counts more by default, and company data such as industry, size and funding is part of the context. The overall score is the weighted average, and you can see exactly why every candidate ranked where they did.
SquarePeg is designed for high precision and transparency rather than black-box scores. Our screening combines enriched resume data, structured skills and title ontologies, deep-context analysis, and fraud detection to produce rankings that closely reflect the hiring manager or recruiter’s actual requirements. Across customers, recruiters consistently report that our rankings surface the most qualified candidates faster, reduce time spent on low-quality applicants, and identify issues that traditional ATS filters miss.We also provide explainable scoring—every recommendation includes a clear rationale—so teams can verify signal quality themselves. The result is screening that is consistent, auditable, and aligned to your specific role requirements—not just keyword matching, and never a black box.
Yes. SquarePeg is built as a glass-box system with full rationale, audit logs, and strict SOC 2–aligned controls. We conduct regular penetration testing and maintain comprehensive logging across our screening workflows. In addition, SquarePeg undergoes monthly third-party fairness and compliance audits through Warden AI, ensuring alignment with emerging AI-hiring requirements in jurisdictions such as New York City and California.Our approach avoids black-box models, excludes protected attributes and proxies, and provides transparent explanations for every score—making it easier for employers to meet their own compliance, audit, and documentation obligations.
Each company in a candidate's work history comes with a description, industries, year founded, employee count, public or private status, revenue and latest funding, plus a short summary of the role. Recruiters see it while reviewing, and your criteria can use it, so SquarePeg can check things the resume doesn't spell out, like "worked at a venture-backed startup" or "has worked at a public company".
SquarePeg flags when the same phone number, email or resume file shows up under a different name within 120 days, a common sign of fraud. When the same person applies to several of your jobs, you'll see their application history on their profile.
Data is stored only for screening, never used to train models; customers own their data and can request deletion at any time, with SOC 2-aligned security practices.
The extension opens SquarePeg in a side panel next to your ATS and follows what you're looking at: open a job and you see its ranked applicants, open a candidate and you see their score, the reasoning, authenticity signals and company data. You can ask the agent questions from the panel too. It works with Greenhouse, Lever, Ashby, Workable, Pinpoint, Comeet, Deel, Loxo, Odoo and Recruitee.
Yes. Invite hiring managers as Users or Viewers on the jobs they own. Viewers can see scores, explanations, authenticity signals and company data but can't edit criteria or move candidates. There's no extra cost per person.
When new candidates apply to live role(s), they are enriched and scored as they arrive so your ranked list stays current.
SquarePeg retains data only as long as needed for screening and account usage. Admins can request deletion of data and revocation of ATS access at any time. SquarePeg deletes data within 90 days once your ATS is disconnected.
SquarePeg integrates with your ATS to extend its functionality, not replace it. It fills the gaps in your ATS by offering advanced features like screening, enrichment, and rediscovery. All ATS data is securely stored and handled with the utmost confidentiality, ensuring your data remains protected.
SquarePeg works with most major ATSs, including Greenhouse, Lever, Ashby, Workable, iCIMS, Recruitee, Pinpoint, Comeet, Loxo, Odoo, Deel, Workday, SmartRecruiters and BambooHR. If you don't see yours, contact us.
Admin permissions make setup faster. You can still create an account, but an admin may need to approve access.
Rejections (with a reason and optional note) and stage changes you make in SquarePeg are made in your ATS exactly as if you had made them there, so any emails and automations you've set up still run. SquarePeg doesn't write to ATS fields; you see scores and reasoning in SquarePeg or in the Chrome extension next to your ATS.
After connecting your ATS, SquarePeg begins importing your candidates and job data. If scores are not appearing yet: Confirm that the initial sync has completed. You can view the last sync time from your main dashboard or your settings. If needed, run a manual sync.Make sure the job has requirements set. Scores cannot be generated until you add and save requirements for the role.If candidates recently applied, allow up to a few minutes after a manual sync for scores to process.If you have confirmed all of the above and still do not see scores, contact Support and we will review your ATS connection.
Open the candidate's authenticity card to see exactly which signals were raised and why. Signals inform your review and never reject anyone, so if you're comfortable with the candidate, simply move them forward.
Open the candidate's profile to see which criteria drove the score and the resume lines behind each one. If the emphasis is off, edit your criteria or change a criterion between Required, Preferred and Bonus, then click Rescore (it's free). You can also send feedback on a score to our team from the app.
SquarePeg automatically syncs with your ATS every three hours. If you see candidates in your ATS that are not yet visible in SquarePeg, check the timestamp of your last sync. You can run a manual sync at any time from the main dashboard or from your settings page. Once the sync is complete, SquarePeg will update candidate visibility and stage alignment based on the information in your ATS.
Scores are determined by the requirements you set. If you are not seeing much variance between scores, that is a great indicator to add more requirements!
SquarePeg will honor any automations or workflow rules you have set in your ATS. When you update a candidate stage in SquarePeg, we push that update back into your ATS instantly and trigger any connected actions associated with that stage. This includes notifications, movement to later pipeline steps, and any ATS level automations your team has configured.
AI can be used in hiring in many different ways. It supports everything from sourcing and scheduling to assessments, interview assistance, and onboarding automation. For recruiting teams specifically, AI is especially valuable in screening, where it can read and structure resumes, understand skills in context, detect fraud or risk signals, and compare each applicant to the requirements of a role.Tools like SquarePeg use enriched resume data, structured skills and title ontologies, fraud detection, and transparent scoring to help recruiters and hiring managers identify aligned candidates more quickly and with clearer rationale.
Glass-box AI shows the underlying signals, data, and reasoning behind each recommendation so humans can review, understand, and override decisions. Black-box AI provides outputs without explaining why, making it harder to audit or trust. SquarePeg uses a fully glass-box approach: every score includes clear rationale, evidence, and audit logs so teams can see exactly how an applicant was evaluated.
Yes. AI systems can reflect or amplify bias if the underlying data or rules contain biased patterns. That’s why independent audits, strong guardrails, and human oversight are critical in hiring. Companies like SquarePeg mitigate these risks by excluding protected attributes and proxies from screening, operating within strict SOC-2–aligned controls, and undergoing monthly third-party fairness and compliance audits through Warden AI to help ensure responsible, compliant use.
AI systems analyze structured signals—such as skills, experience, job titles, tenure, and relevant outcomes—and use rules or models to assess how closely a candidate aligns with the criteria defined for a role. While many tools rely on simple keyword matching or generic LLM outputs, SquarePeg reads deeper context, enriches each resume, and evaluates how skills and experience connect so strong candidates aren’t overlooked.Importantly, AI does not make hiring decisions. In tools like SquarePeg, recruiters or hiring managers set the requirements and always remain the final decision-makers. The AI provides matching insights, rankings, and rationale to support screening, but humans review, interpret, and decide which candidates move forward.
While AI is great at repetitive tasks like reading large volumes of resumes, it takes a human like you to build trust, understand human context, and close candidates. Recruiters provide judgment, relationship-building, and strategy that AI can’t replicate.Think of AI as a copilot, not a replacement. Tools like SquarePeg handle the heavy lifting so recruiters can focus on the human parts of hiring that matter most.
AI can make hiring fairer by applying the same criteria to every applicant, using only job-relevant signals, standardizing evaluation, and removing off-limits or biased data. Unlike manual review—which often involves quick scans or keyword searches—AI can read and analyze every candidate in the pipeline, using the full context of their skills, experience, and work history.When paired with transparent explanations and clear audit trails, this consistency helps reduce subjective variation and makes it easier to detect and correct bias. Tools like SquarePeg strengthen this further by excluding protected attributes and proxies, reviewing all candidates with full contextual analysis, and providing explainable scoring to support more objective and equitable hiring decisions.
You should look for a tool that is accurate, transparent, and fits seamlessly into your existing tech stack without requiring heavy implementation. It should be fast, affordable, and clearly best-in-breed at what it does—not an unfocused “all-in-one” platform that spreads itself thin. A strong AI hiring tool should demonstrate real ROI in the first week, by reducing time spent on low-quality applicants and surfacing top talent quickly.It should also provide clear explanations of how it works, give you control over criteria and weights, maintain strong security and privacy protections, and undergo ongoing bias and fairness checks. And importantly, it should allow you to override any recommendation while evaluating every applicant with full context, not relying on keyword scans.Tools like SquarePeg meet these standards by offering deep contextual screening, transparent scoring, SOC-2–aligned controls, monthly third-party audits, and instant value the moment it’s deployed.
AI can analyze every resume, surface patterns no human could catch at scale, detect fraud, and objectively match candidates to job requirements. It brings consistency, speed, and data-driven insights that dramatically improve the early stages of hiring.But hiring is more than screening. Humans set the strategy, define what “great” looks like, and adjust criteria as business needs evolve. They interpret nuance, assess culture and team fit, build relationships, persuade top candidates, and ultimately make the final decision.AI tools like SquarePeg handle the heavy analytical lift so recruiters and hiring managers can focus on the human work of hiring: judgment, communication, trust-building, and long-term talent decisions.
The best AI tools for resumes are purpose-built for top-of-funnel evaluation, not generic LLM wrappers or basic keyword filters. They should enrich and structure resume data, interpret skills and experience in context, let you control the criteria, provide clear and explainable rationale, and keep an audit trail for compliance and fairness reviews. Many ATS platforms offer lightweight AI features, but they typically don't include company enrichment, fraud detection or fully transparent scoring. For teams with high applicant volume, it's usually more effective to add a dedicated tool that integrates with your ATS.
Keyword filters only search for matching words or phrases—they don’t understand context, seniority, relevance, or whether a candidate actually meets your requirements. A dedicated AI screening tool goes much further: it enriches and structures resume data, interprets skills and experience in context, scores candidates against your defined criteria, flags fraud or duplicate profiles, and provides transparent explanations for why each applicant is ranked the way they are.Tools like SquarePeg help ensure you’re evaluating every candidate fairly and accurately instead of relying on keyword hits that can miss strong applicants or surface unqualified ones.
The AI built into ATSs tends to stop short: Ashby's filters applicants by criteria but never ranks them, and Greenhouse's sorts them into five broad bands using only the resume. SquarePeg ranks every applicant, past candidate and sourced profile 0–100, with a subscore, a written explanation and the resume evidence for each of your criteria. It adds company data the resume leaves out, runs eligibility rules before anything is scored, and checks every candidate for authenticity, all inside the ATS you already use. See side-by-side comparisons at squarepeg.ai/compare.
ChatGPT can summarize or rewrite resumes, but it is not built for compliant or auditable resume screening. It does not enrich company data, apply consistent scoring rules, detect fraud or duplicates, or integrate with an ATS. It also lacks the governance, repeatability, and fairness controls required in a real hiring workflow.Dedicated tools like SquarePeg provide the structure ChatGPT cannot: standardized enrichment, transparent scoring, recruiter-defined criteria and weights, fraud checks, audit logs, and seamless ATS integration—so applicant evaluation is accurate, consistent, and compliant.
SquarePeg is a strong fit if you use a supported ATS, get a high volume of applicants, worry about fake candidates, and want rankings you can defend to hiring managers and compliance teams. To see how it does on your actual pipeline, start with 2,000 free credits on a live role.
