X‑Ray Search for Recruiters (Boolean + AI sourcing)

Primer: what X‑ray search is vs. standard Boolean on platforms

X‑ray search is using a general search engine (usually Google) with operators like site:, quotes, minus, intitle:, and inurl: to surface public profile pages on sites such as LinkedIn, GitHub, Stack Overflow, or Dribbble. According to Google’s documentation, you can restrict results with operators such as quotes, the minus sign, and site:, and narrow results to titles or URLs with advanced filters, which is the backbone of X‑ray search. ¹

Standard Boolean inside a platform (e.g., LinkedIn’s search bar or Recruiter) uses AND, OR, NOT, quotes, and parentheses to match keywords within that site’s data model. LinkedIn’s own help says you can “combine keywords with operators like AND, NOT, and OR” to refine results, and Recruiter/Lite add field‑level filters and suggestions. ²

Why X‑ray still matters in 2026:

• Time‑to‑fill has risen from 43.64 days (2022) to 59.67 days (2025) in a large cross‑company dataset, a 37% increase, so compressing sourcing cycles matters. ³
• Non‑executive cost‑per‑hire now averages $5,475, while executive hires average $35,879, raising the ROI bar for every sourcing hour. ⁴
• LinkedIn enforces a commercial use limit on free accounts, which can throttle in‑app people search; X‑ray runs through Google’s index instead. ⁵

Quick operator examples you’ll actually use:

• LinkedIn: site:linkedin.com/in "customer success manager" "SaaS" -job -hiring (profile pages; exclude jobs) ¹
• GitHub: site:github.com "location * New York" (React OR "Next.js") inurl:users (public developer profiles) ⁶
• Stack Overflow: site:stackoverflow.com/users "python" "pandas" "New York" (public user pages) ¹
• Dribbble: site:dribbble.com "product designer" "Figma" inurl:shots (work samples, not job posts) ¹

Two realities to keep in mind:

• Google operators are powerful but not uniformly documented; rely on site:, quotes, minus, and intitle:/inurl: from reputable guides, and expect occasional variance in results. ¹
• LinkedIn’s own Boolean works, but behavior and limits differ between free search and Recruiter/Lite, and LinkedIn can cap usage on free tiers. ⁷

“Recruiters vastly overperformed, hiring at more than twice the rate since 2022,” notes Greenhouse’s 2026 benchmark preview—evidence that tight processes and tooling can offset headwinds. ³

Copy‑paste string library (ItemList)

This section gives you 14 role‑specific starter strings with US/non‑US variants, seniority toggles, and common exclusions. Paste into Google for X‑ray or adapt to LinkedIn/Sales Navigator by swapping NOT for a minus and removing site:. Use our JD generator to sharpen required titles/skills before searching.

Note: Replace things in [brackets] with your target city/country or industry; keep quotes around multi‑word titles. According to LinkedIn, Boolean operators must be capitalized (AND/OR/NOT) for consistent matching. ²

1) Account Executive (SaaS), mid‑market

• US: site:linkedin.com/in ("Account Executive" OR AE) ("SaaS" OR "software") ("mid‑market" OR "MM") -job -hiring
• Non‑US: site:linkedin.com/in ("Account Executive" OR AE) ("SaaS" OR "software") (EMEA OR APAC OR LATAM) -job -hiring
• Seniority toggle: add ("Senior" OR "Sr.") or NOT ("Senior" OR "Sr.")

2) Paid Media Manager (Meta/Google)

• US: site:linkedin.com/in ("Paid Media" OR "Performance Marketing") ("Google Ads" OR "Meta Ads") "[City]" -job -hiring
• Non‑US: site:linkedin.com/in ("Paid Media" OR "Performance Marketing") ("Google Ads" OR "Meta Ads") (UK OR "United Kingdom" OR "EU") -job

3) Shopify Developer

• US: site:linkedin.com/in (Shopify OR "Liquid") ("e‑commerce" OR DTC) "[State]" -job -hiring
• Non‑US: site:linkedin.com/in (Shopify OR "Liquid") ("e‑commerce" OR DTC) (Canada OR AUS OR NZ) -job

4) Front‑end Engineer (React)

• US: site:github.com (React OR "Next.js") "location * [City]" inurl:users -jobs -hiring
• Non‑US: site:github.com (React OR "Next.js") ("location * [Country]" OR "from [Country]") inurl:users

5) RevOps Manager (HubSpot/SFDC)

• US: site:linkedin.com/in ("Revenue Operations" OR RevOps) (HubSpot OR Salesforce OR SFDC) -job -hiring
• Non‑US: site:linkedin.com/in ("Revenue Operations" OR RevOps) (HubSpot OR Salesforce) (EMEA OR APAC) -job

6) Customer Success Lead (B2B)

• US: site:linkedin.com/in ("Customer Success" OR "CS Lead") (SaaS OR B2B) "[City]" -job -hiring
• Non‑US: site:linkedin.com/in ("Customer Success" OR "CS Lead") (SaaS OR B2B) (Germany OR France OR Spain) -job

7) Data Analyst (SQL + BI)

• US: site:linkedin.com/in ("Data Analyst") (SQL) (Tableau OR Power BI OR Looker) -job -hiring
• Non‑US: site:linkedin.com/in ("Data Analyst") (SQL) (Tableau OR Power BI OR Looker) (Remote OR EU) -job

8) Backend Engineer (Python)

• US: site:stackoverflow.com/users "python" ("django" OR "fastapi") "[State]" -jobs -recruiter
• Non‑US: site:stackoverflow.com/users "python" ("django" OR "fastapi") (UK OR Germany OR Netherlands)

9) Product Designer (Figma)

• US: site:dribbble.com ("product designer" OR "ux/ui") Figma inurl:shots -jobs
• Non‑US: site:dribbble.com ("product designer" OR "ux/ui") Figma (Remote OR Europe) inurl:shots

10) Finance Manager (SaaS metrics)

• US: site:linkedin.com/in ("Finance Manager" OR FP&A) (SaaS OR "ARR" OR "COGS") "[City]" -job
• Non‑US: site:linkedin.com/in ("Finance Manager" OR FP&A) (SaaS OR "ARR") (Ireland OR "United Kingdom") -job

11) Growth Marketer (Lifecycle/CRM)

• US: site:linkedin.com/in ("Lifecycle" OR "CRM") (Braze OR Iterable OR Klaviyo) -job -hiring
• Non‑US: site:linkedin.com/in ("Lifecycle" OR "CRM") (Braze OR Iterable OR Klaviyo) (Canada OR "United Kingdom") -job

12) Sales Development Rep (SDR)

• US: site:linkedin.com/in (SDR OR "Sales Development") (SaaS OR B2B) NOT ("Senior" OR "Manager") -job
• Non‑US: site:linkedin.com/in (SDR OR "Sales Development") (SaaS OR B2B) (EMEA OR APAC) NOT ("Senior" OR "Manager")

13) DevOps Engineer (Cloud)

• US: site:github.com ("DevOps" OR SRE) (AWS OR GCP OR Azure) "location * [City]" inurl:users
• Non‑US: site:github.com ("DevOps" OR SRE) (AWS OR GCP OR Azure) "location * [Country]" inurl:users

14) QA Automation (Cypress/Playwright)

• US: site:linkedin.com/in ("QA" OR "Test Automation") (Cypress OR Playwright) -job
• Non‑US: site:linkedin.com/in ("QA" OR "Test Automation") (Cypress OR Playwright) (Poland OR Romania OR Portugal) -job

If you need more platform‑specific syntax examples or a generator, the current SERP includes tools and guides from RecruitCatch, Talentprise, xraysearch.in, LeanEntrepreneur‑style generators, Pin.com and ConnectSafely’s X‑ray tutorial and tool. ⁸

Inline CTA: Want this done for you? Raffi delivers a 48‑hour shortlist, 80% cheaper than fee‑based agencies, with zero placement fees. Plans start at $199 per job.

Field‑tested workflow: Boolean + AI sourcing stack (HowTo)

A Boolean + AI sourcing stack is a step‑by‑step workflow that pairs focused X‑ray queries with an assistant to enrich, dedupe, and pre‑screen candidates before you ever book a call. “You can still do Boolean searches in Recruiter,” says LinkedIn’s AI‑assisted search FAQ—so think of this as layering simple automation around what you already know. ⁹

• Step 1 — Define must‑haves and no‑gos. Pull 5‑7 must‑haves from your JD (title, tech/tools, industry) and 3‑5 exclusions (e.g., internships only). Our JD generator helps you phrase titles and skills for string‑friendly matching.
• Step 2 — Build 2–3 strings per role. Use one LinkedIn X‑ray (site:linkedin.com/in), one GitHub/Stack Overflow (inurl:users), and one “shots” search for designer roles (inurl:shots). Keep each under a few clauses; long, unwieldy strings get noisy. Google’s own help emphasizes operators like quotes, minus, and site:, which keep queries precise. ¹
• Step 3 — Run, capture, and segment. Open each SERP in a new tab; capture top 50 profiles per string into a spreadsheet or CRM. Expect variable operator behavior; rely on site:, quotes, minus, intitle:/inurl:. ⁶
• Step 4 — Enrich and dedupe with AI. Ask an assistant to standardize names, titles, locations, and dedupe by URL/email domain. Tag by must‑have coverage (e.g., Python+Snowflake+DBT).
• Step 5 — Light pre‑screen. Draft 5 structured knockout prompts (availability, work authorization, comp range, core tool depth). Then hand off to structured interviews using our interview questions tool.
• Step 6 — Prioritize outreach. Start with 20–30 best‑fit profiles, personalize by one portfolio artifact or project link (GitHub repo, Dribbble shot).
• Step 7 — Hand off to your ATS. Keep this upstream of ATS—export a clean, labeled shortlist only. If you’re clarifying ATS vs. upstream screening, see our ATS comparison.

Benchmarks to aim for:

• Reduce sourcing cycles so your overall time‑to‑fill tracks toward sub‑45 days rather than the 59.67‑day 2025 benchmark. ³
• Keep per‑role cash outlay below SHRM’s non‑exec cost‑per‑hire benchmark of $5,475 by minimizing job board and agency spend. Use our cost‑per‑hire calculator to model savings. ⁴

Inline CTA: Or, hand the entire workflow to Raffi—“Raffi is the world's first AI recruitment agency — our agents screen, interview, and rank candidates in 48 hours, 80% cheaper than traditional agencies, with zero placement fees. Plans start at $199 per job.”

Comparison grid: X‑ray/Boolean vs. LinkedIn filters vs. AI agent + human review

Caption: When to use each sourcing approach, common pitfalls, and ideal fit.

LinkedIn confirms Boolean support in Recruiter, and free‑tier commercial use limits are documented in Help. Google’s operator guidance (quotes, minus, site:) is the anchor for X‑ray precision. ⁹

Quality, fairness, and global reach

Multilingual queries widen your pool: try localizing titles and skills into the target market’s language (Ingeniero de Datos; Développeur React; مدير نجاح العملاء). For roles that span markets, write two strings—one English, one localized—and include both Roman and native scripts where relevant. Google’s operators apply across languages and locales, and LinkedIn reports hundreds of millions of skills updates—use skill keywords alongside titles. In 2022 alone, members added nearly 400 million skills to profiles, a 40% YoY jump, which improves skill‑based matching. ¹⁰

On AI fairness: MIT Sloan reminds leaders that “AI won’t fix the problem of bias and inefficiency in hiring, because the problem isn’t technological,” underscoring the need for audited prompts, structured scoring, and human review. ¹¹

Practical guardrails:

• Use structured, job‑related prompts for screens and keep explanations auditable; Greenhouse’s benchmarks show rising application volumes and longer time‑to‑fill—structure reduces noise. ³
• Avoid scraping or automation that breaches platform terms; LinkedIn prohibits crawlers and bots that scrape or automate activity, and enforces commercial use limits and anti‑scraping actions. ¹²
• Separate discovery from selection: use X‑ray/Boolean to find candidates, then structured interviews and work samples to decide. Our interview questions tool and salary calculator keep those decisions consistent and aligned to market.

How Raffi handles this

Raffi runs the Boolean + AI playbook end‑to‑end: we localize strings across 100+ languages, X‑ray/source globally, and then our agents run structured AI screens and interviews before a human recruiter reviews every candidate. Raffi is the world's first AI recruitment agency — our agents screen, interview, and rank candidates in 48 hours, 80% cheaper than traditional agencies, with zero placement fees. Plans start at $199 per job. You get a clean, ranked shortlist with transcripts, skill evidence, and notes you can import upstream of your ATS. If you want to skip three‑week recruiter cycles and see a 48‑hour shortlist, start free at https://client.getraffi.ai/raffi/start.