AI sourcing stack for passive-candidate hiring at scale

This is the stack for the sourcing function, not the recruiting function. It assumes you already have an ATS and recruiters, and that your constraint is upstream of both: the people you need do not apply. Passive-candidate sourcing at scale is a recall problem — every profile index has holes, and the roles that stay open longest are the ones whose candidates sit in somebody else’s hole. The stack is built to close that gap with two search indexes, one CRM in front of the ATS, and an attribution loop that tells you which index paid.

If you want the full hiring loop — screening, scheduling, interview intelligence, debriefs — that is the AI-augmented recruiting stack. This one goes deeper on one job.

How the pieces fit

• SeekOut is the depth index. It searches a large public-profile pool enriched with GitHub activity, academic papers, patents, and clearance and work-authorization data, which is what surfaces senior engineers, researchers, and cleared candidates that title-keyword search misses. Its agents (Rubric Creator, Candidate Finder, Profile Analyst, Outreach Author) turn a req into a scored slate rather than a result list. SeekOut also exposes an MCP server, so Claude can query candidate data directly instead of a sourcer re-typing the search.
• hireEZ is the breadth index. It aggregates 800M+ profiles from 45+ sources across the open web — GitHub, Stack Overflow, papers, patents, niche communities — and is the strongest of the two for Mandarin, Japanese, and Korean sourcing, where LinkedIn coverage thins out. It is also the second opinion on contact data: when SeekOut returns a profile with no verified email, hireEZ resolves a usable one on a meaningful share of them.
• Gem is the CRM and the single front door to the ATS. Every profile from either index lands in a Gem project, gets deduped against Greenhouse before anyone spends an outreach touch, and enters a sequence. Gem’s AI Rediscovery, shipped in its December 2025 agent release, searches your CRM and ATS together and surfaces silver medalists and past applicants who match a new req — the cheapest candidates in the building, because you already paid to find them.
• LinkedIn Recruiter is the verification and InMail channel. It is the profile source of truth the aggregated indexes are copies of, and the only place to reach someone whose email you cannot resolve. Recruiter Lite includes 30 InMails per license per month against a 1st-3rd-degree network with 20+ filters; Recruiter Corporate opens the full network and adds the Hiring Assistant agent as a paid add-on. Treat it as a channel and a checker, not as your search layer — that is the seat you are buying two indexes to stop over-paying for.
• Greenhouse is the ATS and the terminal record. Sourced prospects convert into Greenhouse candidates with the source field set, stage transitions record against them, and source-of-hire reporting is what closes the loop back to the two indexes. Greenhouse also has the largest integration surface of the mainstream ATS options, which matters when four upstream tools all need to write to it.

The handoffs that make it a stack

The order is fixed and each arrow is an event, not a habit.

A new req opens in Greenhouse. Before any search runs, Gem AI Rediscovery scans the CRM and ATS for prior applicants and past prospects who match, and returns a rediscovery list — talent rediscovery first, because it costs nothing per profile. What rediscovery does not fill goes to search: SeekOut for the technical or credential-heavy slice of the req, hireEZ for geographic and open-web breadth. Both push their matches into the same Gem project, where dedupe against Greenhouse drops anyone already in process. Gem enriches contact data, and profiles that come back without a verified email route to LinkedIn Recruiter for an InMail instead of being dropped. Sequences run from Gem. A reply flips the prospect to a Greenhouse candidate with the source attributed to the index that found them. When the req closes, Greenhouse source-of-hire reporting and Gem’s funnel analytics answer the only question that matters at renewal: which index produced hires, not which produced profiles.

Why two indexes instead of one

Because sourcing at this level fails on recall, not on interface. Each vendor’s index is assembled from different crawls — SeekOut leans on technical and credential signals, hireEZ on open-web breadth and non-English coverage — and the overlap is nowhere near total. On a hard req, the second index is the difference between a slate of 12 and a slate of 30.

The rule that keeps that from being expensive duplication: two indexes only pay if there is exactly one CRM between them and the ATS. Run SeekOut and hireEZ each with its own sequencing, and you get the same person messaged twice in a week by two sourcers, which is the fastest way to burn a passive candidate. Gem exists in this stack to be the choke point — one dedupe, one sequence, one reply inbox, one attribution model. That is why the ATS-versus-recruiting-CRM split is load-bearing here rather than a nice-to-have.

What it costs

Budget $75K-$155K a year for a 4-seat sourcing pod, before the ATS. The line items:

• SeekOut publishes Recruit Core at $149/mo billed annually ($1,788/yr) including 3 seats, 500 contact credits and 1,000 exports a month. Everything above Core is quoted, and the tier that adds ATS integration and rediscovery is two steps up from Core — check which tier your integration sits in before signing. Third-party contract trackers put real deals in a $10-30K/year band with a roughly $20K median; treat that as a triangulated estimate, not a rate card.
• hireEZ publishes one figure, $494/mo for the solo and growth entry with a 7-day trial, and states plainly that enterprise is priced against the stack it replaces rather than a flat per-seat rate. Buyer-reported medians land near $13K/year with a $6.6-25K range (estimate).
• Gem prices on your company’s FTE count, not on sourcer seats — a detail that decides the quote before you negotiate it. Its startup program runs $130/mo billed yearly for 1-10 FTE, free for six months under 30 employees, with reduced rates from 30-100. Above that it is quoted; third-party purchase data puts the median near $25K/year, with a $7-71K spread (estimate).
• LinkedIn Recruiter is usually the largest single line and the only one LinkedIn does not publish. Buyer-reported figures put Lite near $1,680/year per seat and Corporate at $9-15K per seat per year, with 2026 renewals landing at the upper end after this year’s increase (estimate). Hiring Assistant is quoted separately on top of Corporate.
• Greenhouse is quoted and sits outside this budget — you have it already or this is the wrong stack.

There is a real floor version: SeekOut Core plus Recruiter Lite plus the Gem startup rate runs under $6K a year for one or two sourcers. And there is a break-even test. At $75-155K, the stack has to produce three to five hires a year that would otherwise go to a contingency agency at 20-25% of first-year salary — $30,000-$37,500 on a $150K hire. Below three, buy the agency.

Common variations

• One index instead of two. If fewer than 30% of your reqs are deep-technical, cleared, or non-English, the second index is duplicated spend. Keep hireEZ if the pain is breadth and contact data; keep SeekOut if the pain is technical depth and credential filters.
• Drop Gem, sequence from hireEZ. hireEZ has sequencing built in. This works at one or two sourcers with no CRM analytics requirement, and it saves the largest quoted line after LinkedIn. Swap back the moment a second sourcer double-touches a candidate or a hiring manager asks for source-of-hire by channel.
• Ashby instead of Greenhouse. Ashby’s native analytics cover much of what you would otherwise buy Gem’s reporting tier for, so the Gem quote should come down. Choose it when you are picking an ATS now rather than replacing one.
• Enterprise ATS. On Workday or iCIMS, the CRM layer stops being optional — their native prospect handling will not carry a sourcing pod, so Gem or Beamery becomes mandatory rather than a choice.
• Buy the outcome instead of the seats. When the constraint is sourcer hours rather than search quality, SeekOut Spot and similar managed services sell interview-ready slates per role instead of licenses. That is a different purchase and a different budget line — compare it against the agency fee above, not against this stack.

When this stack is the right pick — and when it isn’t

Pick it when at least 40% of your hires come from outbound, the roles are senior, technical, or specialized, you have 2-8 people whose actual job is sourcing, and you make 30 or more passive-sourced hires a year. That combination is what amortizes two search contracts.

Skip it in four cases. If hiring is hourly or frontline, this is the wrong category — go to Fountain-class tooling. Under 15 hires a year, the contingency-fee math beats the stack outright. If nobody’s job title is sourcer, the tools go unused: recruiters running full loops will not operate two search indexes, and you should buy one plus a managed service. And if your ATS data is unmaintained — duplicate records, empty source fields, stale dispositions — rediscovery returns noise and attribution lies, so fix the data before you buy the stack that depends on it.

What this stack does NOT replace

• A recruiter. This stack produces engaged, replying candidates. Screening, selling the role, and closing stay human and stay downstream.
• Inbound. Recruitment marketing and employer brand drive the applications this stack never has to source. Outbound-only pipelines cost more per hire, permanently.
• The interview loop. No scheduling, no structured interviewing, no assessment, no interview intelligence here — all of that sits downstream in the ATS or in separate tooling.
• Your data-protection obligations. Sourcing profiles of people who never applied puts you in scope for GDPR lawful-basis and notification duties in the EU and UK, and for CCPA/CPRA rights in California. Buying the tools creates the obligation; it does not discharge it.
• A bias audit. Agent-scored slates rank people. Where AI-assisted ranking feeds hiring decisions, disclosure and audit rules — NYC LL 144 and the EU AI Act among them — attach to the employer, not the vendor.