Job Feeds for Job Boards: How to Backfill, Manage, and Monetize in 2026

Every job board starts with the same problem: it's empty. No employer wants to pay $299 to post on a board with zero traffic, and no candidate will bookmark a site with twelve listings. You're stuck in the classic two-sided marketplace trap where supply needs demand and demand needs supply, but neither side will move first. The credibility threshold is lower than most people think. Fifty to 200 relevant listings is enough to make a niche board feel alive, but zero is fatal.

This guide draws from our experience building Himalayas, which grew to over 365 million Google Search impressions, and now Cavuno, combined with insights from industry veterans Steven Rothberg and Jeff Dickey-Chasins. You'll learn how to source job feeds, manage quality, avoid the SEO traps that sink most aggregated boards, and build a revenue model around feeds that actually scales. Beyond the basics of what feeds are and where to get them, this guide covers the quality controls, deduplication logic, and revenue math that determine whether feeds become a growth engine or a liability.

What is a job feed?

A job feed is a structured data file containing job listings that flows between systems. When an employer publishes a role in their ATS, that listing can be packaged into a feed and distributed to job boards, aggregators, and search engines. When an aggregator like Indeed collects millions of those listings, it can redistribute subsets of them via feeds to niche boards. The entire job board ecosystem runs on these feeds. They're the pipes that move listings from where they're created to where candidates actually search.

Understanding the formats matters because each serves a different purpose.

Feed formats

• XML feeds: the industry backbone. Indeed, ZipRecruiter, and most legacy aggregators exchange data via XML. The format is verbose but well-understood, and virtually every job board platform can ingest it. If a provider offers only one format, it's almost always XML.
• RSS feeds: a simpler XML variant originally designed for blog syndication. Smaller publishers and niche boards sometimes use RSS because it's easy to generate and consume. The downside is limited metadata: you'll get title, description, link, and date, but rarely structured fields like salary range, job type, or location coordinates.
• JSON feeds: increasingly the default for modern APIs. JSON is lighter than XML, easier to parse in JavaScript-based stacks, and better suited for real-time integrations. Providers like Adzuna, Jooble, and newer programmatic platforms tend to favor JSON.
• REST APIs: programmatic access with filtering, pagination, and real-time data. Rather than downloading a static file, you query an endpoint for jobs matching specific criteria (location, category, salary range, posting date). APIs give you the most control over what you pull in and when.

Feeds flow in two directions. Inbound feeds pull jobs into your board from external providers. This is backfilling and aggregation. Outbound feeds push your listings out to aggregators, Google for Jobs, and other distribution channels. This is traffic acquisition. The best boards optimize both simultaneously.

How to backfill a job board

The cold-start problem kills more job boards than bad design, weak marketing, or underfunding combined. Employers evaluate a board by its traffic and candidate quality. Candidates evaluate a board by its listings. Neither side will invest in a platform that hasn't proven the other side shows up. Phil McParlane built 4dayweek.io, got a viral Hacker News launch, and still it took six months before a single company paid to post a job. Six months of building content and traffic before revenue. That's with a strong brand hook and organic buzz. Most boards won't get that kind of head start, which is one of many reasons to carefully evaluate your approach before launching.

Backfilling breaks this deadlock. It means supplementing your board's organic listings (the jobs employers post directly) with aggregated jobs from external feed providers. These providers supply listings through revenue-share arrangements, typically on a CPC (cost-per-click) or CPA (cost-per-apply) basis. You display the jobs, candidates click or apply, and you earn a small amount: $0.05 to $0.70 per click for CPC models, $1 to $15 per application for CPA models. The jobs feel native to your board. Candidates get relevant results. You get content and revenue from day one.

The credibility threshold for launch is lower than most people assume, but it's not zero. A critical mass of 50 to 60 jobs is the bare minimum for a board to feel functional. In practice, 200 to 500 relevant listings create enough density for job seekers to find genuine value. When someone searches "marketing manager jobs in Denver" on your niche board, they should get real results instead of an empty page. If you're validating a niche, the question isn't just whether demand exists; it's whether feed providers can supply enough relevant listings to clear that threshold. Jeff Dickey-Chasins has pointed out that most aggregators can't curate well for very narrow niches, which means the narrower your focus, the more you'll need to supplement feeds with manual sourcing or direct ATS scraping. This is where AI-powered aggregation changes the equation: Cavuno's backfill uses AI to filter and validate jobs against your niche definition rather than relying on keyword matching alone, so even boards targeting narrow verticals can backfill with relevant listings.

Backfilling is a bridge strategy, not a destination. The progression looks like this: you launch your board with aggregated listings that establish content density. That content generates long-tail search traffic across hundreds or thousands of indexed job pages targeting specific role-location combinations. Traffic attracts candidates. Candidates sign up for job alerts and email lists. With a growing audience, you now have something to sell, and you shift to outbound sales, convincing employers to post directly at premium prices. Over time, backfilled jobs should decrease as a percentage of total listings. As we covered in the monetization guide, capping backfill at 30 to 40 percent of total listings preserves your value proposition. Once aggregated jobs dominate your board, you're just another feed-powered directory with no differentiation.

The two-sided marketplace dynamics are what make this work. Backfilled jobs generate long-tail organic traffic you couldn't earn with an empty board. That traffic builds your domain authority and candidate base. A growing candidate base gives you leverage in sales conversations with employers: "we have 15,000 monthly visitors searching for fintech jobs in London" is a pitch that converts. And every direct posting displaces a backfilled listing, improving your margin and deepening the moat between your board and pure aggregators that can't offer the same curation or audience.

Best job feed providers for job boards

The feed provider market is fragmented. Providers range from major job boards sharing their inventory to aggregators pulling from thousands of sources to data companies selling raw job feeds. No single provider covers every niche, geography, or job type, and the landscape shifts as companies pause programs, change pricing, or restrict access.

Aggregators and publisher networks

Aggregators collect job listings from employer career pages, ATS platforms, and other job boards, then redistribute them through XML feeds. When you integrate an aggregator feed, you display their listings on your board and earn revenue on a CPC basis. This is the most accessible entry point for new boards. Most aggregators accept publishers with minimal traffic requirements, and integration typically takes an afternoon. The trade-off is lower CPCs, typically $0.05 to $0.25 per click, because you are one of many distribution partners. For a deeper look at how the aggregator model works, see our dedicated guide.

Programmatic job advertising platforms

Programmatic platforms operate like ad exchanges for job listings. Instead of a fixed feed, they use algorithms to match job ads with the right audience on the right publisher site, optimizing for conversions in real time. CPCs are higher, often $0.25 to $0.70, because the traffic is more targeted. The barrier to entry is also higher. Most programmatic platforms require established traffic before they will onboard you as a publisher.

Data providers and feed services

Data providers sell raw job data (scraped, crawled, or compiled from ATS career pages and public sources) as a product. You pay a flat fee or per-listing rate for access to their dataset, then use it however you want. No revenue-share arrangement, no CPC model, no click tracking. This gives you maximum flexibility but requires more work: you handle deduplication, normalization, expiration tracking, and quality control yourself.

Provider comparison

Aggregators pay you per click. Typical CPCs range from $0.05 to $0.25 for aggregator feeds and $0.25 to $0.70 for programmatic platforms. A niche board generating 1,500 outbound clicks/month earns $75–$375/month from aggregators or $375–$1,050/month from programmatic partners.

Important caveats before you sign up

Indeed's Publisher Program has been paused since October 2022. Indeed stopped accepting new publishers and has given no public indication of reopening. Do not build your backfill strategy around Indeed access.

ZipRecruiter deprecated its publisher program (ZipSearch) on March 31, 2025. No replacement has been announced. Another reminder that feed access is never permanent.

Single-provider dependency is dangerous. Steven Rothberg of College Recruiter has repeatedly warned about this risk. When Indeed paused its program, boards that relied on it for 80%+ of their listings were left scrambling overnight. Run two to three providers minimum.

Most aggregator feeds are general-purpose. As Jeff Dickey-Chasins of JobBoardDoctor puts it: "If you only want jobs about basketball, most of them are going to say sorry you're out of luck." Filtering feeds down to your niche is your responsibility, unless your platform handles it for you. Cavuno's backfill lets you define your niche in plain English and uses AI to filter automatically, so even ultra-specific verticals get relevant listings without manual cleanup.

Application processes vary widely. Some aggregators (Jooble, Adzuna, CareerJet) accept most publishers with a straightforward application. Programmatic platforms like Appcast require minimum traffic thresholds and may involve a sales conversation. Data providers sell subscriptions regardless of traffic but at price points that assume commercial use.

The alternative: proprietary backfill

The provider landscape above represents the traditional approach to backfilling: sign up with one or more third-party providers, manage API keys and feed integrations, handle filtering and deduplication yourself, and earn modest CPC revenue on outbound clicks.

Cavuno takes a different approach. Instead of connecting you to third-party feeds, Cavuno includes its own proprietary job sourcing on every plan at no extra cost. You configure your niche (job titles, locations, seniority, workplace type) and Cavuno populates your board automatically. No provider applications, no API keys, no feed management.

The trade-off is straightforward: Cavuno's backfill doesn't generate CPC revenue because the jobs aren't sourced through a revenue-share arrangement. But as the revenue math later in this guide shows, backfill CPC income is modest for most boards (under $375/month for a typical niche board, and less than 5% of revenue for established ones). What you get instead is zero provider dependency, built-in quality controls (deduplication, expiry checking, location validation, AI filtering), and no risk of a provider pausing their program and leaving your board empty overnight.

How to distribute job listings to other job boards

Job feeds flow in both directions. Most operators focus on inbound feeds (importing listings to backfill their board), but outbound feeds (distributing your listings to other platforms) are equally important for driving candidate traffic back to your site.

Outbound feeds: distributing your jobs for traffic

Every job listing on your board, whether posted directly by an employer or sourced through aggregation, can be distributed outward to drive candidate traffic back to your site.

Google for Jobs does not accept a traditional XML feed submission. It requires JSON-LD structured data on each job page, a sitemap that includes those pages, and ideally integration with the Google Indexing API for near-real-time discovery. This is the single most important outbound distribution channel for any job board.

Jooble, CareerJet, Talent.com, Jobsora, Jora, and WhatJobs all accept XML feeds from publishers. You generate an XML feed of your listings, submit it through their publisher application, and their crawlers pull your feed on a regular schedule. Each click from their platform back to your board is a free candidate visit.

Indeed accepts XML feed submissions from publishers, though acceptance for new publishers has been limited since the 2022 program pause. LinkedIn is restricted to ATS integrations and official partner feeds, with no open publisher submission process.

List job distribution as a feature on your pricing page. Employers value knowing their listing will appear on Google for Jobs, Jooble, and other platforms.

How to integrate a job feed into your job board

You don't need a team of engineers to integrate job feeds. The formats are standardized, the tooling is mature, and most modern job board platforms handle the heavy lifting. But understanding what happens under the hood helps you make better decisions about providers and troubleshoot issues.

XML feed structure

XML remains the standard format for feed exchange. A typical job feed looks like this:

1234567891011121314
<?xml version="1.0" encoding="UTF-8"?>
<jobs>
  <job>
    <title>Senior Software Engineer</title>
    <company>Acme Corp</company>
    <location>San Francisco, CA</location>
    <description><![CDATA[Full job description here...]]></description>
    <url>https://acme.com/careers/senior-swe-123</url>
    <date>2026-02-15</date>
    <salary_min>150000</salary_min>
    <salary_max>200000</salary_max>
    <job_type>Full-time</job_type>
  </job>
</jobs>

The structure is simple, but the devil is in field mapping. Every provider uses slightly different field names and conventions. One provider's <location> might be a single string ("San Francisco, CA"), while another splits it into <city>, <state>, and <country>. Your feed integration layer needs to normalize these variations into a consistent internal schema: standardizing job titles, parsing locations into structured geographic data, and classifying roles into your board's taxonomy.

JSON-LD for Google for Jobs

Google for Jobs reads JSON-LD structured data embedded on each job page, following the Schema.org JobPosting specification:

123456789101112131415161718192021
{
  "@context": "https://schema.org/",
  "@type": "JobPosting",
  "title": "Senior Software Engineer",
  "datePosted": "2026-02-15",
  "validThrough": "2026-03-15",
  "hiringOrganization": {
    "@type": "Organization",
    "name": "Acme Corp"
  },
  "jobLocation": {
    "@type": "Place",
    "address": {
      "@type": "PostalAddress",
      "addressLocality": "San Francisco",
      "addressRegion": "CA",
      "addressCountry": "US"
    }
  },
  "description": "Full job description..."
}

The validThrough field deserves special attention. This is the date Google uses to determine when a listing should expire from Google for Jobs results. Missing or incorrect validThrough dates are among the most common causes of Google for Jobs removal and policy violations. For the complete property-by-property breakdown, see our JobPosting schema guide.

Feed scheduling and freshness

How often you pull and process feeds determines whether your board feels alive or abandoned.

Match your pull frequency to the provider's update schedule. A feed with 50,000 listings that updates every six hours needs to be pulled at least every six hours. A smaller niche feed that updates daily can be polled daily. Pulling more often than the feed updates wastes resources; pulling less often means you miss new listings and display expired ones.

Auto-expire logic is non-negotiable. Jobs should automatically expire when they disappear from the source feed or when their validThrough date passes. The simplest approach: on each feed pull, compare the incoming listings against your existing database. Any listing in your database that no longer appears in the feed gets marked as expired. Any listing past its validThrough date gets removed regardless.

Webhooks beat polling. A webhook is like giving a provider your phone number so they can call you when something changes, instead of you calling them every hour to ask "anything new?" Modern feed providers increasingly support webhook notifications that push alerts to your system when new jobs are posted or existing ones expire. Webhooks eliminate the lag between a job being posted and appearing on your board, and between a job being filled and disappearing. If your provider supports them, use them.

How to manage job feed quality and deduplication

Here is the uncomfortable truth about backfilling: unmanaged feed imports destroy user trust faster than an empty board does. When a candidate clicks a listing and lands on an expired page, encounters the same Senior Engineer role three times from three different sources, or gets redirected to a job that has nothing to do with your board's niche, they don't come back.

The volume problem is easy to solve. The quality problem is where most boards fail. There are five specific failure modes that account for the majority of backfill quality issues, and each requires a different technical solution.

1. Duplicate listings

The same job appears on 50+ boards simultaneously. When you pull from multiple feed providers (an aggregator, a programmatic network, and a direct ATS integration), the same Senior Software Engineer role at Stripe shows up three times with three different source URLs.

How to solve it: Build a multi-stage deduplication pipeline. Start with exact URL matching, the fastest check that catches the majority of duplicates. Then run title + company + location matching for the same job posted through different channels. Finally, use fuzzy description similarity for edge cases.

The critical detail most implementations miss is URL normalization. ATS platforms embed meaningful identifiers in query parameters. Greenhouse uses ?gh_jid=, Lever uses path-based IDs, Workday embeds requisition numbers. A naive normalization that strips all query parameters will fail to deduplicate correctly. The right approach strips schemes, www prefixes, trailing slashes, and fragments while preserving the query parameters that ATS systems use as unique identifiers. A unique database index on the normalized URL then prevents duplicates at the storage level.

2. Expired and stale jobs

One of the biggest causes of Google for Jobs removal and user frustration. Feed providers often include jobs that closed days or weeks ago. The candidate clicks through, hits a "this position has been filled" page, and your board takes the credibility hit.

Google's JobPosting schema requires the validThrough property for exactly this reason. If your structured data advertises a job as active but the source has closed it, Google will reduce your visibility in job search results.

How to solve it: Implement automated expiry checking on a regular cadence. Poll source feeds or APIs to verify each backfilled listing is still active. For any listing without an explicit expiry date, enforce a maximum TTL of 30 days to prevent the slow accumulation of stale content.

Cavuno runs a dedicated expiry service on a six-hour cycle. Every active backfilled job is checked against its source. Jobs confirmed expired or missing from the source feed are immediately marked expired and removed from search results. No manual review required, no stale listings lingering for days.

3. Location data errors

Feed data is notoriously inconsistent with location formats. The same city might appear as "SF", "San Francisco", "San Francisco, California", or "San Francisco Bay Area." When your location filters depend on consistent data, these inconsistencies break the user experience. A candidate searching for jobs in San Francisco misses listings tagged as "SF." Your programmatic SEO pages show different results than they should because half the listings are filed under variant spellings.

How to solve it: Run every imported listing through geocoding validation against a standardized location hierarchy. Map locations to ISO 3166-2 codes (country + subdivision) and validate that coordinates fall within the expected region.

Cavuno normalizes locations on import using geocoding, converting every raw location string into a consistent city, region, and country structure with ISO codes. A feed listing tagged "SF" gets normalized to "San Francisco, California, US" before it ever appears on your board, enabling accurate filters, reliable faceted search, and programmatic SEO pages that contain every relevant listing.

4. Niche bleed

General-purpose feeds include jobs that don't match your board's focus. A remote-work board gets office-only positions. A healthcare board receives software engineering roles. This is the most common quality complaint from niche job board operators, and it stems from the fundamental mismatch between broad feed sources and narrow board scopes.

How to solve it: Keyword matching alone is not sufficient. You need boolean logic (must include X, must not include Y) combined with structured filters for employment type, workplace type, seniority level, and geography. The rule "title contains 'nurse' OR 'RN' AND country = 'US' AND workplace_type = 'remote'" is far more precise than a keyword search for "remote nurse."

Cavuno's backfill configuration uses AI-generated rules built on boolean query logic for job titles, combined with structured filters for countries, seniority levels, employment types, and workplace types. A plain-English validation description tells the AI which jobs belong on the board and which to reject. Typing "Remote software engineering roles, reject jobs requiring on-site work or security clearance" generates the appropriate filters automatically, including edge cases like hybrid roles requiring two days per week in-office.

5. Thin content and SEO damage

Backfilled listings with minimal descriptions (a title, a company name, and a "click to apply" link) are thin content by Google's standards. At the scale backfilling enables, thin pages waste crawl budget and can trigger quality assessments that suppress your entire domain's visibility.

Google allocates crawl budget based on site size and authority. A board with 500,000 listings might receive only 50,000 to 100,000 crawl requests per day. If thousands of those requests hit expired listings, thin pages, or duplicate URLs, you're burning crawl budget that should go to your high-value pages.

How to solve it: Set a minimum content threshold for imported listings. Require a meaningful description, structured location data, and complete metadata before accepting a listing. Reject the thin ones. The listings you reject are the ones that would have damaged your SEO anyway.

Cavuno only syncs jobs that have passed through AI enhancement, meaning every listing arrives with structured location details, extracted skills, normalized seniority, employment type, education requirements, and salary data where available. Listings that are just titles with apply links never make it to the board.

Beyond thin content, there's the duplicate content problem: the same job appears on 50 boards with identical description text, diluting ranking signals across all of them. Canonical tags help, but they aren't sufficient when every board uses the same feed data. The real solution is adding editorial value that makes your version different: salary insights parsed from the description, AI-extracted skill requirements, enriched company context, structured location data that the original listing lacked. This transforms a commodity feed listing into content worth indexing. Boards that do this well rank above the job wrapping aggregators that repost the same data in a different template.

How much revenue do backfilled jobs generate?

Backfilled listings generate revenue through two models, and the math is more modest than you might expect.

CPC vs. CPA

• CPC (cost per click): you earn $0.05 to $0.70 every time a candidate clicks an outbound link on a backfilled listing. The rate varies by industry (healthcare and finance pay more), geography (US and UK traffic pays more), and provider. Most boards start with CPC because it generates revenue from the first click with no conversion tracking required.
• CPA (cost per application): you earn $1 to $15+ per completed application. Higher per-event revenue but lower volume, since not every click converts. CPA requires deeper integration and works best for boards with high-intent traffic.

The actual revenue math

Here is what backfill revenue looks like for a typical niche board, not a theoretical maximum but a realistic scenario:

$375 per month won't make you rich. But it covers hosting costs, proves the revenue model works, and generates income while you build the direct employer relationships that drive real revenue.

Industry data suggests backfill typically generates less than 5% of overall revenue for established boards. Direct job postings, bulk packages, and featured listings are the primary revenue drivers. Backfill revenue is a bonus line item, useful but never the foundation of a sustainable business.

Revenue progression

Based on real operator trajectories:

Boards like RemoteOK ($35,000/month) reached the upper range through premium direct posting products and employer branding, not through backfill CPC revenue.

The backfill ratio trap

When 90% of your listings are identical to every other board running the same feed, employers stop paying for featured slots because the organic listings already give them visibility, and job seekers stop trusting your curation.

The rule from successful operators: keep backfill at 30 to 40% of total listings, maximum. The rest should be direct postings, curated listings, and content you've added value to. For detailed math on what it costs to launch and run a job board, the numbers above provide the revenue side of the equation.

AI-powered job feed management

For years, managing job feeds meant writing keyword rules by hand, reviewing imported listings one by one, running deduplication scripts on a cron job, and manually spot-checking for expired content. This works when you're importing a few hundred listings. It breaks completely at thousands.

AI-powered relevance scoring

Rigid keyword rules fail on edge cases. A board about "climate tech" that filters on the keyword "climate" will miss a Solar Panel Installation Technician role at a renewable energy company. The role is relevant to the board's audience, but there's no mention of "climate" anywhere in the listing.

AI relevance scoring evaluates whether a job fits your niche based on the full context: title, description, required skills, company industry, and seniority level. It understands that a "Sustainability Analyst" role at a carbon accounting startup belongs on a climate tech board even if the description never uses that phrase. This catches the long tail of relevant jobs that keyword rules systematically miss, which means more listings your audience actually clicks on and fewer irrelevant results driving them away.

Automated job title normalization

Employers use wildly inconsistent naming conventions. "SWE III", "Senior Software Engineer", "Sr. Software Dev", and "Software Engineering IC3" are all the same seniority level. AI normalization maps these variations into consistent categories (entry-level, mid-level, senior, director, executive) based on signals from the full listing, not just the title string. This enables seniority filters and faceted search that actually work across hundreds of different titling conventions, so candidates find the right level of roles and your board feels curated rather than chaotic.

Skills extraction and taxonomy mapping

Job descriptions contain implicit skill requirements buried in paragraphs of prose. "You'll work with our data pipeline built on Kafka and Spark" implies Apache Kafka and Apache Spark skills, but a keyword search for "skills: Kafka" would never find this listing. AI skills extraction pulls structured skill lists from unstructured descriptions, enabling skill-based search that keyword matching can't provide. For operators, this means higher engagement: candidates who can filter by the exact skills they have spend more time on your board and are more likely to apply.

AI-generated backfill rules

Instead of manually writing boolean queries and structured filters, describe your board's niche in plain English: "Senior and mid-level data science roles at US and European companies, excluding defense contractors." AI generates the appropriate inclusion and exclusion rules, and identifies edge cases you might not think of, like data engineering roles that are closely adjacent to your niche but not quite on target.

Predictive quality gating

AI can flag listings that are likely to expire soon, contain suspicious patterns (ghost jobs, spam), or fall outside your niche before they're published to your board. Catching these proactively prevents quality issues rather than cleaning them up after the fact.

Job board backfilling mistakes to avoid

1. Single-provider dependency: Indeed famously restricted feed access, leaving boards that depended on it scrambling overnight. Diversify across at least two to three providers from different categories: one aggregator, one programmatic network, one direct ATS source. If any single provider disappearing would remove more than 40% of your listings, you're overexposed.

2. Failing to filter feeds to match your niche: Most aggregator feeds are general-purpose. Without tight filtering, your remote work board gets flooded with office-only positions. Two hundred genuinely relevant jobs beat two thousand where half are mismatches. Candidates learn fast which boards waste their time.

3. Treating backfill as a permanent strategy: Backfill is a bridge to organic employer-posted content, not the destination. Boards that never transition remain commodity aggregators with no pricing power. Plan the handoff from the start: as direct postings grow, reduce backfill proportionally.

4. Ignoring expired job handling: Stale listings are the fastest way to lose both user trust and Google for Jobs visibility. If you're not checking expiry at least daily, you're accumulating dead links that candidates will click and bounce from. The validThrough property exists for exactly this reason. Use it, and keep it accurate.

5. Not differentiating native vs. backfilled jobs in the UX: When a candidate clicks a backfilled listing and gets redirected to an external site, the experience is jarring if unexpected. Some boards mark external listings with a subtle indicator. Others show a brief interstitial. The specific approach matters less than the principle: honesty about where a click leads builds more trust than pretending everything is native.

6. Backfilling from direct competitors: If you import feeds from boards competing for the same niche, you're driving traffic to your competitors with every click. Evaluate every feed source: are you the destination, or the intermediary?

Job feeds are infrastructure, not strategy. Every job board platform can connect to the same providers and import the same listings. That's table stakes. The strategy lives in the quality controls you apply: how you deduplicate, how you handle expiry, how you filter for niche relevance, and how you plan the transition from backfilled content to a board employers actively choose to post on.

Cavuno includes proprietary job sourcing with AI-powered quality controls on every plan at no extra cost, so you can skip the provider signup process and focus on building the board employers want to post on directly. Start building your job board for free with Cavuno.