Diversity Recruiting Software 2026: 6 Tools Compared

Most hiring bias does not come from one bad decision. It builds up across the funnel: a job ad that quietly puts some people off, a sourcing search that only looks where the team already knows, CV screening that rewards familiar names and schools, and interviews where each interviewer asks different questions and trusts their gut. Each step removes a few strong candidates for reasons that have nothing to do with the job.

"Diversity recruiting software" is not one category. It is a set of tools that each target one of those steps. Some rewrite job ads, some widen sourcing, some structure the evaluation so everyone is judged against the same criteria, and some measure where candidates drop out. This guide sorts six tools by the stage they fix and explains how to use them within the law in the U.S. and the UK.

We compare tools on what their own websites say and what they publish about price. We have not run paid trials, and we have left out vendors' outcome claims where the method is not explained. Prices are as published in November 2026 and exclude tax.

Where bias enters the hiring funnel

Before buying any tool, find the stage where your funnel leaks. If your applicant pool is varied but your hires are not, a sourcing tool will not help; your evaluation is the problem. If too few people apply in the first place, start with the ad.

A tool will not fix this on its own. If you have not yet agreed what you are trying to change and why, start with our guide to building a DEI strategy from scratch, then choose software to support it.

The six tools at a glance

Job descriptions and ads

Textio

Textio's website describes AI for recruiting and performance management, with its recruiting product aimed at helping teams "eliminate bias in recruiting communications" while keeping messages in line with the employer brand. It says its AI has been trained for ten years to identify bias in HR communications and that 25% of Fortune 500 companies have used it to improve job posts and feedback.

• Strength: a long track record in inclusive language, applied where you write: job posts and candidate messages.
• Also useful for: performance feedback, which suffers from the same biased language as job ads.
• Watch for: no published pricing; sales design a package for each business. There is a free trial account.
• Best for: organisations that write a lot of job content and want every recruiter to apply the same language standard.
• Not for: small teams posting a few roles a year, who can get most of the benefit from a careful manual checklist (see below).

Datapeople

Datapeople's website describes a job content platform for talent teams, now part of Payscale. It offers a Smart Editor to "manage and improve job content", an AI first-draft writer, AI guidance and compliance review, and insights on job post performance. It claims compliance support for "pay transparency laws, DEI mandates, and EVP guidelines", and native integrations with ATSs including Workday, Greenhouse and Lever.

• Strength: treating job descriptions as a managed library, so every ad follows the same structure and requirements are not quietly inflated.
• Also useful for: pay transparency, as more U.S. states require salary ranges in ads.
• Watch for: no published pricing; the site does not detail its inclusive language features, so ask for a demo using your own job ads.
• Best for: larger teams with many open roles and recurring job templates.
• Not for: teams that need only occasional language checks.

Sourcing

SeekOut

SeekOut's website describes AI sourcing across more than a billion profiles and your own ATS, applicant evaluation with AI screening against custom rubrics, and outreach campaigns. Its compliance section lists a "Diversity Analytics" view of pipeline diversity by department and "regular third-party bias audits (latest: Sept 2025)". A customer quote credits its "diversity filters and talent pool insights", though the home page does not list what those filters are.

SeekOut publishes a starting price on its pricing page: Sourcing Core costs $149 per month billed annually ($1,788 a year), or $179 billed monthly, for three seats, with a 14-day free trial. Larger sourcing and full-funnel plans are priced on request.

• Strength: combining wider sourcing with pipeline diversity reporting and published bias-audit dates.
• Watch for: ask exactly how diversity filters work and where the underlying data comes from. Filters that infer someone's race or gender from their name or photo are inaccurate and, used to select candidates, can create legal risk (see the legal section below).
• Best for: in-house sourcing teams trying to reach beyond referrals and the usual target employers.
• Not for: teams whose problem is evaluation rather than reach.

Gem

Gem's website describes an AI sourcing agent that searches more than 800 million profiles and resurfaces your best past candidates, a recruiting CRM with automated nurture sequences, an ATS, and analytics to "surface pipeline bottlenecks". The site links to OFCCP compliance and diversity, equity and inclusion pages, but its home page does not describe demographic reporting features. Pricing for startups starts "as low as $135/month for the full platform", with discounts for the first year; enterprise pricing is custom.

• Strength: rediscovery. Past silver-medal candidates, already assessed and interested, are often a more varied pool than a fresh search of familiar networks.
• Watch for: confirm what diversity and funnel reporting exists in a demo, as it is not described on the home page.
• Best for: growing companies that want sourcing, CRM and ATS from one vendor.
• Not for: enterprises set on keeping Workday or another ATS as the system of record, though Gem also sells agents that run on top of Greenhouse and Workday.

For more sourcing options, see our candidate sourcing software comparison.

Evaluation and the ATS

The biggest single lever on fairness is how candidates are evaluated. Structured hiring means deciding what the job needs before seeing anyone, asking every candidate the same job-related questions, scoring answers against a written rubric, and having each interviewer submit scores before the group discusses. Our structured interview guide explains how to set it up. Your applicant tracking system is where that discipline lives or dies.

Greenhouse

Greenhouse's website says it pioneered structured hiring. Its AI voice interviews are "structured, using your team's questions and rubric", and its notetaker maps AI-generated notes to scorecard questions so feedback stays tied to the same criteria. For its AI Talent Matching feature, which sorts applications into tiers, Greenhouse publishes a bias audit statement: independent auditor Warden AI tests for disparate impact across demographic groups and checks whether signals such as names, gendered words or hobbies change results, and each new version must pass an audit before release. Results are published on Warden AI's dashboard rather than on the page itself.

• Strength: mature structured hiring workflows plus transparent, ongoing bias testing of its AI ranking.
• Watch for: no published pricing.
• Best for: mid-sized and large employers that want structure built into every interview loop.

Ashby

Ashby's website says it is built to "enable structured hiring end-to-end" and offers advanced recruiting analytics with custom reporting. It does not describe diversity or EEO reporting on the home page.

• Strength: analytics depth for teams that want to build their own funnel reports, including drop-off by stage.
• Watch for: no published pricing; confirm how demographic survey data is collected, stored separately and reported.
• Best for: fast-growing companies with an analytically minded recruiting team.

For a wider comparison of applicant tracking systems, see our applicant tracking system guide.

Doing it within the law

Diversity recruiting tools are about removing barriers, not about preferring one group over another. Getting that distinction wrong is the main legal risk.

United States

The EEOC's page on prohibited employment practices says it is illegal to publish a job advertisement that shows a preference for, or discourages, people because of a protected characteristic. Its example is an ad seeking "recent college graduates", which may discourage older applicants. Recruitment methods matter too: relying on word-of-mouth in a workforce that is mostly one group "may violate the law" if it produces a similarly uniform pool of new hires. That is a legal reason, not only an ethical one, to widen sourcing beyond referrals.

The same page warns against pre-employment questions that reveal race or other protected characteristics, because they can be used as evidence of intent to discriminate. Voluntary demographic surveys for reporting should be kept separate from hiring decisions, and hiring managers should not see the answers.

If you use AI to screen or rank candidates for jobs in New York City, Local Law 144 applies. The city has enforced it since 5 July 2023. An automated employment decision tool must have had a bias audit within the past year, a summary of the results must be public, and candidates must be told 10 business days before it is used. Ask any vendor using AI ranking for its latest audit; Greenhouse and SeekOut both publish information about theirs.

United Kingdom

UK guidance on positive action in the workplace is clear: "Positive discrimination is unlawful in Great Britain." Under the Equality Act 2010 you can take positive action, such as targeted outreach, adverts encouraging applications from under-represented groups, or mentoring (section 158). Under section 159 you can use a protected characteristic as a tie-break between candidates of equal merit, where that group is under-represented or disadvantaged. You cannot appoint a less suitable candidate, set quotas, or lower the bar so that more people count as "equal".

For software, that means sourcing filters and targeted campaigns can widen the pool, but the selection itself must rest on job-related evidence, applied the same way to everyone.

AI raises the stakes

AI is now part of most recruiting platforms. SHRM's 2026 research found that 39% of organisations have implemented AI in their HR functions, with another 7% planning to. An AI model trained on your past hiring decisions can learn the patterns you are trying to remove. Our article on AI's impact on diversity, equity and inclusion covers both the risks and the opportunities in more depth.

A no-cost bias checklist for job ads

Before you pay for a language tool, apply these checks to your next five job ads:

1. Cut the requirements list. Separate genuine must-haves from nice-to-haves. Long lists of "requirements" discourage capable people who meet most but not all of them.
2. Drop proxies. Replace degree requirements and years of experience with the skills they stand for, where the job allows. Our skills-based hiring guide shows how.
3. Remove age and gender signals. Phrases such as "digital native", "young and dynamic team" or "rockstar" put people off for reasons unrelated to the work.
4. Publish the pay range. It is required in a growing number of U.S. states and helps every candidate judge whether to apply.
5. State flexibility and adjustments. Say whether the role can be part-time or flexible, and how to ask for adjustments during the process.
6. Read it aloud. If it sounds like it describes your current team rather than the job, rewrite it.

Measuring whether it works

Diversity recruiting only improves what you measure. Track these by funnel stage:

• Applicant pool: the mix of applicants by source, so you know which channels widen it.
• Pass-through rates: the share of each group moving from application to screen, screen to interview, and interview to offer. A sharp drop at one stage tells you where to look.
• Offer acceptance: low acceptance among one group can point to the interview experience or pay.
• Quality of hire: SHRM's 2025 benchmarking found that only 20% of organisations track quality of hire. Without it, you cannot show that fairer processes also hire well.

Collect demographic data voluntarily, store it separately from hiring decisions, and report in aggregate. In the UK, employers with 250 or more staff already publish gender pay gap data. The ONS puts the national gap for full-time employees at 6.8% in April 2026 (provisional). Hiring and pay data together show whether progress at the door continues inside the organisation.

What to ask vendors in a demo

• Which funnel stage does your product address, and what evidence do you have that it reduces bias there?
• If you use AI to rank or screen candidates, when was it last audited for bias, by whom, and where are the results published?
• How are diversity filters built? Do you infer protected characteristics, and if so, how?
• How is demographic data collected, stored and kept away from decision-makers?
• Which ATSs do you integrate with, and what writes back to the candidate record?
• How is pricing calculated: per seat, per hire or per open role?

The last question matters. SHRM research found that 30% of HR leaders regret their most recent HR technology purchase. With SHRM also finding that 69% of U.S. organisations still struggle to fill full-time roles, a tool that widens your pool without adding friction is worth more than one that adds another dashboard. Run a pilot on one team first.

Frequently asked questions

What is diversity recruiting software?

Tools that reduce bias or widen access at one or more stages of hiring: inclusive job ad writing, broader sourcing, structured evaluation, and funnel analytics. Few products cover every stage, so most employers combine an ATS with one or two specialist tools.

Is diversity recruiting legal?

Removing barriers and widening outreach is legal and encouraged. Selecting candidates because of a protected characteristic is not. In the UK, positive discrimination is unlawful, although a tie-break between candidates of equal merit is allowed in limited circumstances.

Can software remove bias from hiring?

No tool removes bias on its own. Software helps by making good practice consistent: the same job-related criteria, the same questions and the same scoring for every candidate, with data showing where people drop out.

Do AI recruiting tools reduce or increase bias?

Either, depending on how they are built and checked. Ask for independent bias audit results; in New York City, employers using automated employment decision tools must have an audit within the past year and publish a summary.

What is the cheapest way to start?

Rewrite your job ads using the checklist above, introduce structured interviews with shared scorecards, and track pass-through rates by stage in your existing ATS. Add specialist software once you know which stage needs it.