AEO for Job Boards: How to Get Your Job Board Cited by ChatGPT, AI Overviews, and Perplexity

AEO flips that. As Smith explained on Lenny's Podcast: "You can get mentioned by a citation tomorrow and start showing up immediately. You can have a Reddit thread, you can have a YouTube video. You can be mentioned on a blog. So early-stage companies can win, they can win quickly."

This is the core reason job board operators should care about AEO right now. Every era of search has had a window where incumbents were vulnerable. AEO is that window for job boards, and niche operators have structural advantages that Indeed and LinkedIn cannot replicate.

When someone asks an AI engine "What's the best job board for nonprofit professionals?", the answer isn't going to be Indeed. Indeed has millions of listings across every industry but has no depth in any single vertical. A niche nonprofit job board with structured data on every listing, editorial content about nonprofit career paths, salary benchmarks specific to the sector, and mentions in nonprofit industry publications, that board has every signal AI engines use to determine authority on that topic.

We saw this firsthand building Himalayas, a niche remote job board competing against every mega-aggregator, to 5 million clicks and 365 million impressions. Topical authority in a specific vertical compounds in ways that horizontal scale can't match.

The economics reinforce the opportunity. AI referral traffic converts at 3x or more the rate of Google organic. For a niche board doing 10,000 monthly visits, even 200 high-intent AI referrals at those conversion rates meaningfully move revenue. NerdWallet reported 37% revenue growth in Q4 2024 despite a 20% decline in monthly unique users. Their growth was primarily driven by insurance revenue expansion, so it's not a pure AEO success story, but the directional signal is clear: the relationship between raw traffic volume and revenue is decoupling. Fewer, higher-intent visits from AI-endorsed sources can drive more value than undifferentiated organic volume.

If you've been thinking about creating a job board, the AEO landscape means you don't need years of domain authority to earn visibility. If you're still exploring, we've written about job board ideas worth pursuing and how to validate your niche before building.

Why are job boards built for answer engine optimization?

Most job board operators hear "AI search" and feel a threat. The assumption is that ChatGPT, Perplexity, and Google AI Overviews will steal traffic, summarizing your listings without sending visitors. That assumption is wrong.

Job boards have structural advantages for AEO that few other website categories match.

Large language models don't hallucinate answers about job markets from memory. They use a pattern called RAG (Retrieval-Augmented Generation): search the web, retrieve relevant sources, synthesize an answer, then cite those sources. What they need from those sources is structured, trustworthy, fresh data. Job boards deliver exactly this, by design, every single day.

Why structured data gives job boards an AEO advantage

Every job listing on your board contains machine-readable fields: job title, company name, location, salary range, employment type, experience level, skills required. This isn't content you have to retrofit. It's the core of your product.

If you've implemented JobPosting schema for Google for Jobs, you've already formatted this data for machine consumption. AI engines parse the same structured markup. Your job board SEO investment translates directly: the structured data that earns rich results in Google is the same structured data that makes your listings easy for LLMs to retrieve and cite.

And if you're using the Google Indexing API or IndexNow to push new listings into search indexes within minutes instead of waiting for passive crawls, you're already solving the freshness problem that most publishers struggle with. Your newest listings get into AI search indexes faster than nearly any other content type on the web.

Why freshness matters for AI citations

AI engines reward freshness signals, and most websites struggle with content staleness. They publish a blog post, it ages, they update it six months later. Job boards don't have this problem. Freshness is baked into the business model. Listings expire and refresh constantly. New jobs appear daily. Salary data updates with every posting. This constant churn is exactly the signal answer engines use to determine whether a source reflects current reality.

When a candidate asks ChatGPT "What's the average salary for a senior product designer in Austin?", the AI needs a source with recent, real data. A job board with hundreds of current listings and dynamic salary pages is a better source than a static salary guide blog post published eighteen months ago.

Why job board queries match how people use AI search

ChatGPT queries average 25+ words, compared to just 6 on Google. Smith found that the AEO long tail is roughly 4x bigger than SEO's long tail. People talk to AI the way they talk to a recruiter, in full sentences with multiple constraints. And as Smith noted on Lenny's Podcast, there are "questions that have never been asked before and questions that have never been searched before, because search can't support lots of really specific stuff. Whereas chat is specifically made to ask a bunch of follow-up questions."

"Remote marketing manager jobs in Austin that pay under $120K and don't require a degree."

That query is long-tail, specific, and deeply personal. It also maps perfectly to the filtering and categorization systems job boards already have. The programmatic SEO pages you've built, category by location by salary by remote status, are pre-built answers to exactly these queries.

How programmatic pages power AI search visibility

The category x location x salary page matrix that powers job board SEO creates something AI engines value enormously: deep topical authority at scale. When your board has thousands of pages covering specific job markets, each with fresh listings and structured data, you're demonstrating the kind of deep, narrow expertise that LLMs are trained to prefer as citation sources.

This isn't theoretical. Job board operators are already seeing it. Visitors arriving from AI search have a specific, well-formed intent that your board is already built to serve.

While most publishers are scrambling to adapt their content for AI search, job boards were accidentally built for it. The structured data, the freshness, the long-tail query alignment, the programmatic depth, these aren't features you need to add. They're features you need to amplify.

How does AEO differ from SEO?

Before jumping into tactics, you need to understand two things about how AEO works differently from SEO. These differences shape every decision in the playbook that follows.

Why citation volume matters more than ranking #1

In Google, if your blue link shows up first, you win. In an LLM, that's not the case. As Smith puts it: "The LLM is summarizing many citations, and so you need to get mentioned as many times as possible."

ChatGPT now has 800 million weekly active users processing roughly 2 billion queries per day. That's approximately 12% of Google's search query volume. Despite that volume, ChatGPT sends 190x less referral traffic than Google. The answers are self-contained. Users read the response, maybe click one link, and move on.

So the game isn't about getting clicks from ChatGPT. It's about being named in the answer. Brand mention is the new click. And the way to get named is not by ranking your URL #1 in citations, but by being mentioned across as many citation sources as possible, across Reddit threads, YouTube videos, blog posts, and industry publications.

Seer Interactive's research across 10,000 queries shows a ~0.65 correlation between ranking on page 1 of Google and being mentioned by LLMs. That correlation is strong but far from perfect, meaning Google rankings are necessary but not sufficient.

How ChatGPT, AI Overviews, and Perplexity cite differently

ChatGPT, AI Overviews, and Perplexity each retrieve, synthesize, and cite information differently.

ChatGPT uses a technique called query fan-out: when a user asks a complex question, it breaks it into multiple sub-queries and searches the web for each one simultaneously. A single question like "What are the best niche job boards for healthcare with good salary data?" might trigger 5-10 separate searches across different angles. This means more chances for your content to be retrieved, but also means you need to cover topics from multiple angles, not just one keyword. ChatGPT search is powered by OAI-SearchBot (separate from GPTBot, the training crawler) and uses Bing's index as a starting point.

Perplexity runs a real-time Bing search, feeds results into an LLM, and synthesizes an answer with numbered footnote citations. With 22-45 million monthly active users, it's the most likely to send actual referral traffic.

Google AI Overviews appear on an estimated 7-25% of searches depending on the month, pulling from Google's own index. When they appear, organic CTR drops significantly. Seer Interactive measured organic CTR at 0.61% on queries with AI Overviews versus 1.62% without them. But being cited inside an AI Overview partially offsets that: cited pages capture more clicks than uncited pages in the same results, making citation the difference between losing traffic and holding your ground.

Citation slots are scarce. LLMs typically cite only 2-7 domains per response. And 89% of citations differ between ChatGPT and Perplexity. You can't optimize for one and assume the other follows.

Does AEO replace traditional SEO?

One important calibration: AEO is an addition to SEO, not a replacement. As Smith puts it: "Google's slice of the pie stays the same. The pie gets bigger." Every few years the industry declares Google dead (TikTok search, Instagram, Facebook before that). Each time, these turn out to be new surfaces that add on top of Google, not replacements.

Google still drives the vast majority of job board traffic. Everything that works in SEO works in AEO. But there are additional things beyond SEO that also work in AEO, specifically citation optimization and long-tail question coverage. Your job board marketing strategy should treat AEO as a new channel to add, not a reason to abandon what's working. Strong E-E-A-T signals increase your odds across all platforms.

Will AI bypass job boards entirely?

AI could route job seekers straight to employer career pages, cutting job boards out entirely. This isn't hypothetical. Perplexity's Comet browser can already auto-apply to jobs on behalf of users, an early signal of the agentic AI future where AI agents interact with employer ATS systems directly, no job board intermediary required.

The pattern is clear: specificity wins. A job board that owns a vertical with thorough, structured, authoritative content is harder for AI to bypass than one that competes on breadth. The real risk isn't that AI eliminates job boards. It's that AI eliminates undifferentiated job boards.

Test how your board appears by running a few queries in ChatGPT and Perplexity now. The next section covers how to close the gaps.

How to optimize your job board for AI search

Smith breaks AEO into two equal pillars: onsite (your own pages) and offsite (getting mentioned on other people's pages). Both matter. For discovery queries like "best job boards for healthcare," offsite may matter more.

Step 1: find the right questions

In SEO, you target keywords. In AEO, you target questions. Start by identifying which questions you want your board to appear in.

Take your competitors' paid search data (or your own) and transform those keywords into questions. Smith's tactic: feed the keyword list to ChatGPT and ask it to generate the questions people would ask. "Best healthcare job boards" becomes "What are the best job boards for healthcare professionals?" and "Where should I post nursing jobs?"

Then go beyond search data. Mine the questions candidates ask in your niche: Reddit threads, Quora, customer support tickets, sales calls. These map directly to the long-tail questions being asked in AI chat, many of which have never been searched on Google.

Put those questions into an answer tracker (Graphite, Profound, or any of the 50+ tools now available). Track your Share of Answers: how frequently your brand appears across AI surfaces for those queries, measured across question variants and repeated runs. Smith's advice on tooling: "Pick the cheapest one that does what you need."

Step 2: optimize your onsite pages

Everything that ranks well in Google tends to perform well in AEO. But AI engines reward additional signals that traditional SEO doesn't prioritize.

Audit your structured data. Most job boards already output JobPosting schema for Google for Jobs. The problem is that most implementations are minimal. AI systems reward completeness. Audit your schema against three tiers:

• Required: title, description, datePosted, validThrough, hiringOrganization, jobLocation
• Recommended for AI: baseSalary with min/max range, employmentType, educationRequirements, experienceRequirements, jobLocationType set to TELECOMMUTE for remote roles, applicantLocationRequirements
• Advanced: experienceInPlaceOfEducation, occupationalCategory using O*NET-SOC codes, jobBenefits, qualifications

A fully optimized listing in JSON-LD:

Beyond individual listings, stack multiple schema types on content pages. Company profile pages should combine Organization + BreadcrumbList. Career guide pages should add FAQPage schema alongside BreadcrumbList. This multi-schema stacking gives AI engines more structured context to pull from, and more entry points to cite your board.

Cavuno generates compliant JobPosting schema automatically for every listing, including salary extraction and remote work classification. If you're on another platform, run your pages through Google's Rich Results Test and compare property coverage against the full list above.

Build programmatic pages that answer AI queries. Programmatic SEO pages (the category x location x salary matrices) serve double duty in AEO. They rank in Google AND become citation-ready for AI engines answering queries like "What remote data science jobs pay over $150K?"

Salary pages are particularly valuable for AEO. When someone asks ChatGPT "What does a product designer make in Austin?", the AI needs a structured, current data source. Programmatic salary pages that pull from your live listing data, showing median, 25th, and 75th percentile salaries with sample sizes, are exactly what AI engines prefer to cite. They combine the freshness of real listings with the structure of a data resource.

Programmatic pages that get cited by AI need more than good rankings. They require:

• An AEO-optimized intro paragraph of roughly 150 words that directly answers the conversational query. If the page targets "marketing jobs in Austin," the first paragraph should read like a complete answer to "What marketing jobs are available in Austin?", including the number of open roles, salary range, top hiring companies, and a time-specific reference. AI engines extract this passage wholesale.
• Salary range data with sources. Aggregate and display the salary distribution for that category + location combination. A line like "The median marketing manager salary in Austin is $98,400 based on 247 active listings" is exactly the kind of data-rich snippet that gets pulled into AI responses.
• FAQ schema specific to each combination. Generic FAQs are useless. Each programmatic page should have 3-5 questions unique to that job type and location: "What's the average marketing salary in Austin?" / "Which Austin companies are hiring marketers?" / "Are there remote marketing jobs based in Austin?"
• Internal links to career guides and company profiles. Every programmatic page should link to relevant career content and to company profiles for the top employers in that segment. This builds the entity web that signals topical authority to AI systems.

Cavuno auto-generates programmatic SEO pages by category, location, and salary range, complete with optimized meta tags, structured data, and internal linking. If you're building these manually, prioritize your highest-traffic category + location combinations first.

Create career content that builds topical authority. A job board with only listings is a database. AI systems don't cite databases. They cite authoritative sources that demonstrate deep domain expertise. Graphite's research found that pure AI-generated content accounts for only 3% of organic search results and ranks on page 2 on average, while human-written content dominates the top 5 positions. Your career content needs real expertise and original data, not AI-generated filler.

The key concept here is information gain: does your content contain data or analysis that doesn't exist anywhere else? Google's information gain patent explicitly rewards pages that add new information beyond what other results provide. Job boards have a natural advantage here because you're sitting on first-party data that no one else has. Your listing counts, salary distributions, hiring trends, application volumes: this data is unique to you. The winning approach is to converge on what's already known (cover the basics every other guide covers) then diverge with your own data (add the insights only you can provide). A salary guide that starts with Bureau of Labor Statistics benchmarks then layers on "based on 1,247 active listings on our platform, the median is actually $12K higher" is far more citable than either source alone.

Build content clusters around your core verticals:

• Salary guides by role and location. AI engines cite salary data constantly. When someone asks ChatGPT "What does a product manager make in New York?", the answer gets pulled from whichever source has the most specific, well-structured, and recent data. Publish salary guides that combine your listing data with Bureau of Labor Statistics benchmarks. Update them quarterly.
• "How to become a [job title]" career path guides. These are high-intent informational queries that AI assistants field daily. An in-depth guide covering education requirements, typical career progression, required skills, and expected salary at each level positions your board as the authority on that role.
• Industry hiring trend reports with original data. This is where job boards have an advantage. You have real-time data on which roles are growing, which locations are hiring, and how salaries are shifting. Original data is the single strongest signal for AI citation because no other source can replicate it.
• Interview preparation guides for specific roles. Structure them around specific questions like "What are the most common product manager interview questions?", which maps directly to how candidates query AI.

Apply what Graphite calls the "AEO Topics" framework: identify clusters of related questions around each role or industry you serve, then create one complete page per cluster rather than thin pages for each question. Within each cluster, think in terms of a question hierarchy: head questions ("What is a product manager?"), mid-tail ("What does a product manager do at a startup?"), and long-tail ("What's the difference between a product manager and a program manager at a Series B company?"). A single well-structured page can target all three tiers. Depth beats breadth for AI citation.

One important filter: focus on product questions where AI answers suggest solutions. "What are the best job boards for healthcare?" triggers product citations. "What is a registered nurse?" does not. Prioritize content that creates opportunities for your board to be named.

If your blog or career content lives on a subdomain (blog.yourboard.com), move it to a subdirectory (yourboard.com/blog). Subdirectories consolidate topical authority under a single domain, which strengthens both SEO and AEO signals. Cross-link aggressively between career content and job listings.

Cavuno includes a built-in blog for content marketing on the same domain as your listings. The internal linking between your listings, programmatic pages, and career content creates exactly the topical authority web that AI systems reward.

Optimize every listing for extractability. Structured data tells AI engines what your content is. Extractability determines whether they actually use it. AI systems scan for discrete, self-contained passages that answer specific questions.

The core unit is what researchers call an "answer nugget": a 40-80 word passage that completely answers a single question without requiring surrounding context. When ChatGPT or Perplexity cites a source, they're pulling these nuggets. If your content doesn't contain them, it gets skipped in favor of competitors that do.

• Front-load the answer. The first 100 words after every heading should contain a complete, self-contained answer to the question implied by that heading. State the point immediately.
• Use question-formatted headings that match how candidates query AI. Instead of "Responsibilities," use "What does a [job title] do?" Instead of "Compensation," use "What's the average [job title] salary in [city]?"
• Structure requirements and benefits as bullet lists with 3-10 items each. AI engines parse lists more reliably than prose paragraphs. Each bullet should be a complete thought.
• Add statistics and citations within the text. The Princeton GEO study, led by Pranjal Aggarwal et al. and published at KDD 2024, found that adding quantitative data, named sources, and inline citations boosts content visibility by up to 40% in AI-generated responses. A passage like "Austin has 1,247 open marketing roles with a median salary of $94,000 according to our February 2026 data" is far more citable than "Austin has many marketing opportunities."
• Use semantic URLs with 5-7 descriptive words. /jobs/remote-senior-data-engineer-jobs outperforms /jobs?category=42&type=3. Clean URLs give AI engines a content signal before they even parse the page.
• Build entity density into your pages. Named companies, specific technologies, certifications, locations, salary figures: these give AI engines more hooks to match against user queries.

Step 3: get mentioned everywhere AI looks

For queries where your board is the direct answer ("What Python jobs are available in Denver?"), onsite optimization wins. But for discovery queries like "best job boards for healthcare" or "where to find remote design jobs," AI engines synthesize answers from multiple third-party sources. You can't optimize your way into these responses from your own site alone.

For many job boards, this is the highest-leverage AEO tactic. LLMs build brand associations from co-occurrence across the web. When your board is mentioned alongside "healthcare jobs" in Reddit threads, blog posts, and industry publications, the model learns to associate your brand with that topic. The more sources that mention you in the right context, the more likely you are to be cited when a user asks a related question. This is fundamentally different from traditional SEO, where you optimize your own pages. In AEO, you need other people's pages to mention you.

Smith calls this the "Earned Strategy." The principle: volume of mentions across trusted sources matters more than any single #1 ranking. Appearing in 5 separate citations across Reddit, YouTube, and career blogs beats holding the top organic position in one.

The critical detail: you need to appear on the specific pages AI engines are already citing, not just anywhere on the same domain. A mention on a random blog post helps. A mention on the exact listicle that ChatGPT already pulls from when answering "best healthcare job boards" is 10x more valuable. This is why the reverse-engineering step later in this section matters so much.

Reddit: the kingmaker. Reddit has become a primary source for AI-generated answers, particularly for recommendation queries. When someone asks ChatGPT "What's the best niche job board for [industry]?", the answer frequently pulls from Reddit threads. The strategy is simple. As Smith described on Webflow University: "Make a Reddit account, say who you are, say where you work, and give a useful answer." Authentic participation in r/jobs, r/careerguidance, and your niche-specific subreddits builds the mention footprint AI engines rely on. Smith puts it bluntly: "You don't actually need 10,000 comments, even five could be great."

The obvious growth-brain approach, hundreds of fake accounts auto-posting comments, doesn't work. Reddit's community catches it, accounts get banned, and comments get deleted. The winning strategy is the obvious one: be a real person, share real expertise, disclose your affiliation.

YouTube: wide open territory. Create "best job boards for [niche]" videos. As Smith noted on Lenny's Podcast, for B2B and niche topics "there's not that many videos about AI-powered payment processing APIs, as interesting as that is, but it's a great money term." The same applies to job boards. For most vertical categories, there is effectively zero competition on YouTube. A single well-made video can become the dominant source AI engines cite for that query.

Career advice blogs and listicles. Get your board mentioned in "best job boards for [industry]" roundups and career resource guides. These listicle pages are exactly what AI engines compile answers from. Reach out to career bloggers, offer guest contributions, and provide unique data they can reference.

Industry and association publications. For operators running association job boards, this channel is particularly powerful. Contributing thought leadership to association newsletters, trade publications, and industry conference content positions your board as the authoritative source for that niche. When an AI engine encounters your board mentioned across the association's own publications, member forums, and industry press, the authority signal compounds.

Reverse-engineer the citation sources. Run your target queries in ChatGPT and Perplexity and document which sources get cited in the answers. Those are the pages you need to appear on. If ChatGPT cites a particular "best healthcare job boards" listicle when answering that query, getting your board added to that specific page is higher-leverage than publishing ten new posts on your own site.

Build a systematic outreach cadence: 2-3 Reddit contributions per week, one YouTube video per month, and ongoing pitches to career bloggers and industry publications.

Step 4: set up your technical foundation

AI search engines can only cite what they can crawl. You need a deliberate strategy for which bots get access to your job board and how you keep indexes fresh.

Which AI crawlers matter. Job boards sit on a massive, constantly-changing dataset that AI companies want for two different reasons: training their models and powering their search products. Understanding the difference helps you make informed decisions about crawler access.

The landscape as of early 2026:

• GPTBot: OpenAI's training crawler, roughly 30% of AI crawler traffic (Cloudflare). Blocking it does not affect whether ChatGPT search can cite your pages.
• OAI-SearchBot: Powers ChatGPT search results. Block this and you disappear from ChatGPT search entirely.
• ChatGPT-User: Fires when a user asks ChatGPT to browse a specific URL. As of December 2025, no longer respects robots.txt for user-initiated browsing.
• PerplexityBot: Powers Perplexity's search. The most attribution-friendly AI search product. Allow it.
• Google-Extended: Controls Gemini AI training and AI Overviews. Separate from Googlebot. Blocking it won't affect Google Search or Google for Jobs.
• ClaudeBot: Anthropic's training crawler (Cloudflare).

Our recommendation: allow all AI crawlers, both search and training. You want your content baked into the models themselves, not just retrievable at query time. When your job board's data is part of what a model learned during training, your brand shows up even in responses where the AI doesn't run a live search. Blocking training crawlers means you only appear when the AI actively searches for you. Allowing them means you become part of the AI's background knowledge.

Review your crawler strategy quarterly as the landscape shifts.

Dual-protocol indexing for job freshness. Job boards have a freshness problem that most websites don't. A job listing might expire in 14 days. If an AI crawler indexes a stale listing and serves it in a search result, the user clicks through to a dead page, and the AI engine learns your board isn't trustworthy. Freshness isn't optional for job boards. It's critical.

Use both indexing protocols simultaneously:

• Google Indexing API for Google for Jobs and AI Overviews. Lets you notify Google the instant a job is posted, updated, or removed. A 2025 change restricted access to "authorized partners," so smaller boards may need to fall back to high-frequency XML sitemaps with <lastmod> timestamps.
• IndexNow for Bing, Yandex, and Microsoft Copilot. Submit a URL and those engines know about the change within minutes. Bing's AI Performance dashboard specifically recommends IndexNow for better AI citation coverage.

When a job expires, your system should automatically remove it, set up a redirect, and notify both protocols.

Cavuno implements Google Indexing API + IndexNow dual-protocol from day one. In an AI search world where freshness directly affects citation trust, passive crawling leaves visibility on the table.

Step 5: measure and experiment

AEO measurement tooling is still catching up to the opportunity. But it's getting usable, and operators who set up tracking now will have months of baseline data when AI traffic accelerates.

Start with GA4 custom channel groups. Create a custom channel group that identifies traffic from chatgpt.com, perplexity.ai, copilot.microsoft.com, gemini.google.com, and claude.ai as a distinct "AI Search" channel. This takes ten minutes and immediately gives you visibility into a traffic source that's been invisible in your analytics.

The Bing Webmaster Tools AI Performance dashboard, launched in February 2026, is the first major platform offering native AI citation tracking: citation counts, which pages are being cited, and the "grounding queries" that trigger Copilot to reference your content.

Beyond free tools: Ahrefs Brand Radar (free in beta) monitors brand mentions. HubSpot's AI Search Grader evaluates visibility across AI surfaces. Profound ($499+/month) offers full AI citation monitoring. Smith's advice: pick the cheapest tracker that does what you need. Answer tracking is a commodity, just like keyword tracking.

Test what actually works. Most AEO advice is recycled from blog to blog without anyone verifying it. Smith is blunt: "Most best practices, most blog posts are not correct. Somebody will say something and then it will get repeated, and then it becomes best practice and no one ever did an analysis."

Pick a few target queries, try a tactic (Reddit comment, YouTube video, landing page upgrade), and check whether your visibility changes over the following weeks. If it works, do more of it. If it doesn't, try something else. The tooling makes this easy to track now.

For a deeper dive on measurement, see our guide to job board analytics.

30-day AEO action plan for job boards

You don't need to rebuild your job board to win in AI search. You need to be systematic. This four-week plan distills everything above into steps any job board operator can execute, whether you're running a niche board on WordPress or a custom-built platform with thousands of listings.

Week 1: audit and baseline

Start by understanding where you stand. Run your highest-traffic job listing through Google's Rich Results Test and check whether your JobPosting schema includes baseSalary, employmentType, and occupationalCategory with O*NET-SOC codes. Most boards implement the bare minimum.

Open ChatGPT and Perplexity in a private/incognito window or logged out and test 20 job-related queries in your niche. (Prior conversations influence AI responses, so you need a clean session to see what a typical user would get.) Search "best remote data engineering jobs," "average UX designer salary in Chicago," "top healthcare job boards." Document which boards get cited, how they're described, and what content the AI pulls from. This competitive audit tells you exactly what the bar looks like.

Set up AI referral tracking in GA4 by creating custom channel groups for chatgpt.com, perplexity.ai, copilot.microsoft.com, and gemini.google.com. Check your robots.txt to ensure OAI-SearchBot and PerplexityBot aren't blocked. Set up the Bing Webmaster Tools AI Performance dashboard.

Week 2: foundation fixes

Upgrade your JobPosting schema to include baseSalary, employmentType, and occupationalCategory with proper O*NET-SOC codes. This is the single highest-leverage technical change: it gives AI engines structured, parseable data instead of forcing extraction from unstructured HTML.

Implement dual-protocol indexing with both the Google Indexing API and IndexNow. Add FAQ schema to your top 10 highest-traffic job category pages. Create or optimize your "About" page with Organization schema and clear E-E-A-T signals: founding date, team expertise, data methodology, and industry affiliations.

Week 3: content creation

Write 3-5 salary guides for your most popular job categories. AI engines cite salary data constantly, and if your guide is well-structured with clear data points, you become the source. Cavuno auto-generates programmatic salary pages from your live listing data, giving you a head start here.

Optimize your programmatic page templates with AEO intro paragraphs of roughly 150 words that directly answer the core query each page targets. Publish "How to become a [role]" guides for your top three searched roles. Ensure each content marketing page has strong entity density: named companies, technologies, certifications, and locations throughout.

Week 4: earned visibility and experimentation

Identify five Reddit communities relevant to your niche and contribute genuine value: answer questions, share data from your platform, disclose your affiliation. Reddit threads are among the most-cited sources in AI search results.

Pitch guest posts or contributor quotes to three industry publications. Create one YouTube video: "Best [niche] job boards in 2026", where there's likely zero competition for this query in video format.

Pick 5-10 target queries where you don't currently appear and try different tactics: a Reddit comment, a YouTube video, a guest post mention. Track whether your visibility changes over the following weeks with your answer tracker. Set up monthly AI search monitoring with Ahrefs Brand Radar or HubSpot AI Search Grader to track progress over time.

Building a new board? Cavuno handles Weeks 1-2 automatically with built-in structured data, AI crawler management, and dual-protocol indexing. Start at Week 3 ->