The Citation Source Taxonomy: Twelve Surface Categories AI Draws From When Recommending Employers

Why a Taxonomy

When AI answers a candidate's question about working at a named company, it synthesizes from a specific set of public surfaces. That set is smaller than most employer brand teams expect, and it is structured differently from the surfaces most teams actively manage.

The gap matters. Investment in surfaces AI is not reading produces no AI visibility lift. Investment in surfaces AI cites repeatedly produces compounding visibility lift, sometimes faster than the team responsible for the work realizes. Knowing which surfaces are which is the prerequisite to deciding where the next unit of employer brand investment goes.

There is no industry-standard category list for the surfaces AI reads when recommending employers. Brand-side audit categories were built for an earlier search environment. Recruitment marketing taxonomies were built around paid placement and source-of-hire attribution. Neither maps cleanly onto how AI assembles a candidate-intent answer about a named company in 2026.

This post publishes a category list built for that map. Twelve categories, grouped into five families based on who controls the surface:

• Employer-owned (4): Careers Site, Company Newsroom/Blog, Functional Team Content, Leadership Social Content
• Peer-reviewed (3): Glassdoor, Generalist Peer Review, Sector-Specific Peer Review
• Specialist (3): Compensation Specialist Platforms, Persona-Specific Community Platforms, Reddit and General Professional Forums
• Independent (1): Independent Voice Content
• Authoritative (1): Trade and Business Press

The categories are descriptive. They name what the surfaces are, where they show up in AI synthesis, and why each one earns a distinct line. They do not score any individual company. The scoring question -- whether a given company is anchored, present, thin, or absent on a given surface -- is the subject of follow-on work; this post lands the noun before the measurement vocabulary that sits on top of it.

How This Taxonomy Was Built

The twelve categories were extracted from the AI-cited surfaces observed across the scored corpus of Diagnostics and pre-sprint research scans Antellion ran between April and May 2026. A surface earned category status when it appeared repeatedly across candidate-intent queries, across multiple models, and across multiple sectors. Surfaces that cited inconsistently or only inside a single sector were noted in the inventory layer but not promoted to category status.

The discipline that runs through the taxonomy is scoping. Every claim in this post is scoped to authority across the AI-cited candidate-intent surfaces in the scored corpus -- not to the public web at large, not to all employer signals, not to what any individual AI model knows about employers. The scoped frame is what makes the taxonomy defensible and what allows downstream measurement to be inter-rater reliable.

Reference surfaces -- Wikipedia, Crunchbase, ZoomInfo -- are cited by AI constantly in candidate-intent answers and are captured separately as part of the broader citation analysis. They are excluded from the Authority Surface Map because a company cannot meaningfully move a Wikipedia article through its own work. The Map is intentionally scoped to surfaces where deliberate investment shifts the AI synthesis; reference surfaces are real, cited, and tracked, but they are not where the deliberate-investment conversation lives.

The line between a category and an example surface inside a category is drawn at the level of editorial control and audience tailoring. Glassdoor earns its own line because it is the dominant single citation source across sectors in the scored corpus, and rolling it into a broader "peer review" bucket would erase signal. Built In, Vault, and Wall Street Oasis sit inside Sector-Specific Peer Review because the editorial pattern is the same across sector-tailored platforms even though the audience and sector vary. Methodological honesty, not vendor endorsement.

Worked examples throughout the next five sections are illustrative composites drawn from sector-typical citation patterns observed in the scored corpus. The patterns recur; the named-company identifiers are not.

Employer-Owned Surfaces

The four surfaces a company directly publishes on its own domain or under its own named authorship. Together they are the surfaces where editorial control is highest -- the lift from deliberate investment shows up fastest here when AI is reading the surface.

1. Careers Site. The company-owned recruiting surface -- careers.[company].com, jobs.[company].com, /careers, employee profile pages on the company domain. AI cites careers-site content when candidate-intent queries ask about role-specific culture, hiring philosophy, leader profiles, or the public articulation of an employee value proposition. A regional health system that publishes nurse-leader profiles on /careers, with each leader's clinical specialty and operating philosophy named, surfaces those profiles when candidates ask AI about clinical leadership culture at the system. The careers site is the surface most employer brand teams have the most control over and frequently the surface AI under-cites because the published material is not sufficiently citable in the form AI is trained to read.

2. Company Newsroom and Blog. Press releases, /newsroom, /blog, /insights, leadership announcements, values content, M&A announcements, awards announcements. AI cites newsroom content when candidate-intent queries ask about company stability, growth trajectory, leadership changes, and the public-facing direction of the business. A multi-state credit union whose newsroom carries a recent acquisition announcement surfaces the announcement when candidates ask AI whether the credit union is stable and growing. The newsroom is the surface that most consistently produces date-stamped, attributable content -- which AI weights heavily when assembling answers to questions about company direction.

3. Functional Team Content. Team-specific public content on the company-owned domain -- the engineering blog, the sales playbook, the design blog, the customer success learning library, the operations or safety operating content. AI cites functional team content when candidate-intent queries ask about the substantive work pattern of the function: not "is the engineering team good" but "what does engineering at [company] actually build on." A national logistics company that publishes a fleet-safety operating blog surfaces the blog when operations candidates ask AI about safety culture and operating discipline at the company. Functional team content is the line in the taxonomy that does the most work across sectors -- engineering blogs are not the only thing that fits. Any function with substantive public output earns the surface.

4. Leadership Social Content. Public content authored by named company leaders on platforms outside the company domain -- LinkedIn executive posts, X posts, leadership-authored Medium or Substack content, named-leader YouTube appearances, founder or executive podcast appearances. AI cites leadership social content when candidate-intent queries ask about the operating philosophy, public voice, or substantive views of named leaders at the company. A mid-market industrial distributor whose CFO publishes regular LinkedIn essays on inventory operating discipline surfaces those essays when finance candidates ask AI about the company's operating philosophy. The leader being named on the surface -- not a generic "company posted" attribution -- is what makes the content cite-able in the AI synthesis.

Peer-Reviewed Surfaces

The three surfaces where employees and former employees write the citations. Together they are the surfaces where the company's editorial control is lowest and AI's confidence in the cited material is often highest, because AI weights employee-authored content as proximate to the actual workplace experience.

5. Glassdoor. Singleton category. When AI surfaces peer-review content for a named company in a candidate-intent answer, Glassdoor is the citation source AI returns to far more often than any other across the scored corpus. The concentration is structural -- observable across sectors and across the four major AI models. Scoring Glassdoor inside a broader "peer review" category would erase signal that the depth of Glassdoor presence is the load-bearing variable. Glassdoor earns its own line because the citation pattern says it earns its own line, not because of any editorial preference.

6. Generalist Peer Review. Cross-sector employee review platforms -- Comparably, Indeed reviews, Kununu (European markets). AI cites generalist peer review when candidate-intent queries ask about management style, leadership ratings, work-life balance signals, and culture metrics that the platforms surface as scored or rated content. A specialty retailer surfaces Comparably's leadership rating page when candidates ask AI about management style for a customer experience role at the retailer. Generalist peer review platforms vary widely in how AI weights them by sector and persona, but the editorial pattern -- aggregated employee-authored review content presented at the company level -- is shared.

7. Sector-Specific Peer Review. Sector- or persona-tailored review platforms -- Built In (tech), Vault (finance, consulting, law), Wall Street Oasis (finance), sector-tailored employee review platforms. AI cites sector-specific peer review when candidate-intent queries are sector-coded and the candidate is asking questions the sector-specific platform is built to answer. A regional investment bank surfaces Vault's compensation and culture review when finance candidates ask AI about deal team experience at the bank. Sector-specific platforms are the surfaces most often invisible to brand-side audits that scan generic peer-review sources but not sector-specific ones.

Specialist Surfaces

The three surfaces tailored to specific candidate communities. Highest-leverage section in the taxonomy. These are the surfaces most employer brand teams have the thinnest visibility into and where AI most often surfaces material the company has not authored and may not realize is being read.

8. Compensation Specialist Platforms. Role-specific compensation surfaces with active community contribution -- Levels.fyi, RepVue, salary.com community boards, role-specific compensation transparency platforms. AI cites compensation specialist platforms when candidate-intent queries ask about base, variable, equity, ramp guarantees, on-target earnings, and compensation-band transparency. A mid-market enterprise SaaS company surfaces RepVue's sales-org compensation transparency rating when revenue candidates ask AI about commission structure at the company. Compensation specialist surfaces are where AI most often produces concrete number ranges in candidate-intent answers -- the depth of presence on these surfaces is often a stronger predictor of confident AI compensation synthesis than anything published on the careers site.

9. Persona-Specific Community Platforms. Named professional communities and forums tied to specific candidate personas -- Blind, Gainsight Community, Customer Success Network, Pavilion, SHRM communities, role- or function-coded professional communities. AI cites persona-specific community platforms when candidate-intent queries ask about role-specific operating experience, day-in-the-life patterns, scope-of-role at named companies, and community-sourced sentiment that the platform's contributors have surfaced. A late-stage SaaS company surfaces a Gainsight Community thread on team structure when senior customer success candidates ask AI about scope of role at the company. The community platforms are where the candidate-experience narrative for a role at a named company is most often shaped by content the company itself did not author.

10. Reddit and General Professional Forums. Public professional discussion communities not tied to a single persona platform -- Reddit (r/sales, r/customersuccess, r/managers, sector-specific subreddits like r/financialcareers, r/HealthIT, r/manufacturing), Hacker News, Lobsters, sector-specific public forums. AI cites Reddit and general forum content when candidate-intent queries ask about interview process, hiring speed, named-company sentiment from a candidate-experience perspective, and unfiltered employee or candidate commentary. A regional bank surfaces a r/financialcareers thread on hiring process speed when finance candidates ask AI about the bank's interview experience. The Reddit family of surfaces is the surface family AI most often cites that companies have the lowest awareness of as a cited source.

Independent Voice

11. Independent Voice Content. Named-author independent content surfaces not on company-owned domains -- Substack newsletters from non-leadership authors, podcast transcripts where the podcast is independent (not produced by an employer or vendor), conference talk transcripts, named-author Medium content, named-author YouTube content, named industry-analyst essays. AI cites independent voice content when candidate-intent queries ask about industry-level patterns, named-expert assessments, or candidate-experience narratives where the cited author has stake or credibility independent of the company. A health insurance carrier surfaces a named industry-analyst Substack essay when actuarial candidates ask AI about the carrier's product strategy and what it implies for actuarial roles.

Independent Voice is one category, not five, because the editorial pattern is the same across the platforms inside it -- a named human author publishes on a surface they personally control, AI synthesizes the named-author content. The platform varies; the citation behavior does not.

Authoritative Press

12. Trade and Business Press. Editorial press surfaces -- the major business press (Bloomberg, Reuters, Wall Street Journal, Financial Times, the major sector trade publications), HR trade publications (TLNT, HR Brew, Recruiting Daily, SHRM articles), sector trade press (Modern Healthcare, American Banker, Supply Chain Dive, IndustryWeek), and regional business journals. AI cites trade and business press when candidate-intent queries ask about company-level news, growth trajectory, sector-level competitive positioning, leadership changes, and any company narrative the press has covered at scale. A specialty chemicals manufacturer surfaces a regional business journal acquisition story when operations candidates ask AI about the company's growth trajectory and what the acquisition means for operational stability.

Trade press and general business press behave differently in source weighting and recency, but the editorial-authority signal is the same -- editorial publication, named reporter or analyst attribution, dated content, citable through the publisher's archive. They share a category because the citation pattern shares a structure.

What Is Not in the Taxonomy and Why

Four surface families are deliberately excluded from the Authority Surface Map even though all four are real, cited, and tracked elsewhere.

Reference surfaces (Wikipedia, Crunchbase, ZoomInfo, similar) are cited by AI constantly in candidate-intent answers. They are captured separately as part of the broader citation analysis. They are excluded from the Map because a company cannot meaningfully move a Wikipedia article through its own employer brand work. Including reference surfaces in the Map would dilute the signal of surfaces where deliberate investment matters.

Paid placement -- sponsored LinkedIn job posts, sponsored Indeed listings, paid Reddit promotions, sponsored content on trade publications -- is placement, not synthesis surface. AI rarely cites paid placement in candidate-intent answers about working at a named company, and where it does, the citation typically reads as an editorial rather than as a paid signal. Paid surfaces affect candidate awareness; they do not meaningfully affect AI synthesis. They are tracked in recruitment marketing reporting, not in the Map.

Private communities -- internal Slack channels, private Discord servers, internal forums, gated alumni networks -- are not synthesizable surfaces. AI cannot read what it cannot access. Some of the highest-trust candidate-experience signal lives in private communities; none of it routes into AI synthesis until and unless a member of the community publishes a derivative artifact on a public surface.

Podcasts and video without transcripts are not synthesizable. If the surface produces no machine-readable text and is not transcribed by a third party that AI does index, the surface is not in the corpus. Podcasts with transcripts and conference talks with transcripts route to Independent Voice Content; podcasts and video without transcripts route nowhere AI can read.

The Map is intentionally scoped to actionable authority depth -- surfaces where a company can move the synthesis through deliberate work. Reference and paid surfaces are real, cited, and tracked in inventory; they are simply not where the deliberate-investment conversation lives.

What This Taxonomy Is For

This is not "the surfaces an employer brand team should publish on." It is "the surfaces AI is actually reading when a candidate asks about a named company." Knowing the difference is the first move.

The taxonomy is the structural backbone of one section of the H1 2026 AI employer visibility benchmark publishing June 30. That section will look at surface-distribution patterns in aggregate across the companies in the scored corpus -- not authority scoring of any individual company. The benchmark will name patterns that show up consistently across companies of similar size and sector, and patterns that diverge -- and will use this category list as the unit of analysis.

For an employer brand leader reading this post, the first useful exercise is to take the twelve categories, list the surfaces inside each one that your team actively works, and circle the categories where the active surfaces are zero or near-zero. The circled categories are where AI is reading and the company has the least deliberate input. That gap, by itself, is a strategic input -- not a verdict on any team's prior work, and not a critique of the categories the team has historically prioritized, but a structural read of where the next unit of employer brand work has the most leverage given how AI now assembles candidate-intent answers.

Twelve categories. Five families. Sector-agnostic. The map is the deliverable.