Textio vs Applied vs Pin: The Best AI Tools for Diverse, Inclusive Sourcing in Recruitment Agencies (2026)

TL;DR

Diverse, inclusive sourcing is now its own tool category, distinct from generic AI sourcing platforms like SeekOut or hireEZ, and it splits into three real jobs: writing job ads that do not repel candidates before they apply (Textio, Datapeople), building candidate pools that are not just your recruiters' existing networks (Pin, Untapped, Handshake, Mathison, now part of Changeforce), and screening candidates without a name, photo or school biasing the first cut (Applied, GapJumpers) [1]. None of these do the same job twice, and none of them is boilr's job either: boilr has no debiasing, blind-screening or demographic-filtering feature, and this article says so plainly rather than pretending otherwise [9]. The reason this category matters now: the EU Pay Transparency Directive requires gender-neutral job ads and a non-discriminatory recruitment process from mid-2026 [6], the EU AI Act classifies candidate-screening AI as high-risk with deployer obligations landing 2 December 2027 [7], and more enterprise clients are writing "diverse slate" clauses - at least two candidates from an underrepresented group per shortlist - directly into staffing vendor contracts [13].

Why "Inclusive Sourcing" Is a Different Tool Category, Not a Feature

Search "best AI sourcing tools" and you get SeekOut, hireEZ, Gem, Pin's own general sourcing lists, and a dozen others competing on database size and outreach automation [8]. That is a real and useful category, but it answers a different question: can you find more candidates, faster? The tools in this article answer a narrower one: does your current process for finding and screening candidates quietly favour the same kind of candidate every time, and can software catch that before a client notices?

• The bias usually enters before anyone opens a resume - a job ad written with masculine-coded language ("dominant," "competitive," "rockstar") measurably suppresses applications from women, before any screening happens at all.
• Recruiter networks are not neutral by default - "who do I already know" sourcing tends to reproduce the demographics of the recruiter's own network, not the labour market.
• Unstructured screening lets unconscious bias in early - a name, a photo, a university logo on a CV all influence a human reviewer before a single skill is assessed, and general-purpose ATS software does nothing to stop it.
• "AI-powered" does not mean bias-aware - a generic AI sourcing or matching tool trained on historical hiring data can encode the same bias it is meant to remove, unless it is specifically built to strip demographic signal out of the loop [3].
• Regulators are now treating this as a compliance question, not just an ethics one - New York City's Local Law 144 already requires an independent annual bias audit for automated employment decision tools, with enforcement tightening through 2026 [12].

The Three Layers of Inclusive Sourcing Software

Every tool in this category does some mix of three jobs. Knowing which one a vendor is actually strong at matters more than any star rating.

Layer 1: Inclusive job-ad language

• What it fixes: gendered, aged, ableist or otherwise coded wording in the job posting itself, before a single application arrives.
• Manual approach: a style guide and a hopeful "does this read okay?" from whoever wrote the ad.
• Tool examples: Textio, Datapeople.

Layer 2: Diverse-pool sourcing

• What it fixes: a candidate pipeline that mirrors the recruiter's existing network rather than the actual talent market.
• Manual approach: manually posting to a handful of diversity-focused job boards and hoping for volume.
• Tool examples: Pin, Untapped, Handshake, Mathison (Changeforce).

Layer 3: Blind, structured screening

• What it fixes: unconscious bias entering at the CV-review or interview stage via a name, photo, age or school.
• Manual approach: asking a hiring manager to "try to be objective," with no structural enforcement.
• Tool examples: Applied, GapJumpers.

The Shortlist at a Glance

Seven tools recruitment agencies and in-house TA teams reach for most often in 2026, judged on the layer they actually solve and what an agency should realistically expect to pay.

Tool	Layer	Best for	Pricing signal	Main watch-out
Textio	Inclusive job-ad language	High-volume job posters who want a real-time bias score before publishing	~$209/mo small teams; $15K-$50K/yr for enterprise [4]	English-only; strongest ROI at volume, less so for a handful of ads a month
Datapeople (by Payscale)	Inclusive job-ad language	Teams that want bias flags tied to internal policy and compensation data together	Custom quote only [10]	No public pricing; heavier setup than a lightweight writing plugin
Applied	Blind, structured screening	Agencies running structured, skills-based hiring for a client with a defined process	Custom enterprise, reportedly from ~$5,000/yr [11]	Works best when the client controls the full process end to end
GapJumpers	Blind, structured screening	Skills-first, audition-style screening for technical or creative roles	Estimated from ~$500+/mo, per-assessment options exist [5]	Assessment design takes real setup time per role
Pin	Diverse-pool sourcing	Agencies wanting a general AI sourcing tool that also does not feed demographic data into matching	Free tier; $99-$249/mo paid tiers [1]	Diversity is a design principle, not a dedicated DEI product - fewer specialist filters than Untapped
Untapped	Diverse-pool sourcing	Early-career and campus hiring where underrepresented-talent depth matters most	Custom, per-seat contract [1]	Skews early-career; less useful for senior or niche technical mandates
Handshake	Diverse-pool sourcing	Campus and graduate pipelines, including HBCU and HSI partnerships	Free basic job posting; premium tier custom-priced [1]	Built for early-career hiring, not a general agency sourcing tool

Mathison is deliberately not in that table's active rows. It was acquired by Changeforce in May 2024 and now operates inside Changeforce's DEIB consulting group rather than as a standalone self-serve product [2]. Its Equal Hiring Index and inclusive-language Chrome extension are real and still referenced in market roundups, but a buyer evaluating it today should confirm current product availability directly with Changeforce rather than assuming the pre-acquisition offering is unchanged.

Textio and Datapeople - Fixing the Job Ad Before Anyone Applies

Both tools attack the same failure point: a job ad that quietly filters out qualified candidates through word choice, before an application ever lands. Textio scans postings and outreach for gendered, aged or exclusionary phrasing and produces a single predictive "Textio Score" for how the language is likely to perform [4]. Datapeople takes a more granular approach, flagging eight specific bias categories - including racism, ageism, ableism, sexism and elitism - in a colour-coded editor, alongside compensation-policy and legal-compliance checks [10].

Where they earn their cost

• Volume changes the maths - an agency writing dozens of ads a month gets compounding value; one writing three a month may not clear the enterprise price tag.
• They catch what a style guide misses - real-time flagging beats a PDF nobody re-reads.
• Datapeople ties bias checks to pay-range accuracy - useful with EU Pay Transparency obligations landing in the same job-ad workflow [6].

Where they fall short

• They fix the ad, not the screening after it - an inclusive ad followed by biased CV review still produces a narrow shortlist.
• Both gate pricing behind a sales conversation for anything beyond an entry tier [4][10].
• Textio is English-only, which limits it for multi-market DACH/EU agency desks writing ads in German or French.

Applied and GapJumpers - Screening Without a Name Attached

Applied structures the entire hiring workflow around anonymised, competency-based applications: it strips names, photos, age and education details before a reviewer sees them, and scores candidates on job-specific questions instead [11]. GapJumpers takes a narrower "blind audition" approach: candidates complete an anonymised skills challenge or coding test, and only the top-scoring anonymous submissions get de-anonymised for interview [5].

Where they earn their cost

• Structure is enforced, not requested - a hiring manager cannot "just glance at" a name that has been removed from the system.
• Skills-based scoring travels well across roles where a CV format varies wildly by candidate background.
• Both produce an auditable record of how a decision was reached, which is increasingly relevant under bias-audit regimes like NYC's Local Law 144 [12].

Where they fall short

• They need the client to buy in fully - a recruitment agency running a search for a client that still wants a named CV up front cannot force blind screening on them.
• Setup cost is real - writing good, fair, job-specific assessment questions per role takes time that a generic ATS never asked for.
• Applied is priced for enterprise adoption, custom-quoted, reportedly from around $5,000 a year [11], which is a different budget conversation than a per-seat sourcing tool.

Pin, Untapped, Handshake and Mathison - Building a Wider Pool

These four solve the "we always end up interviewing the same kind of person" problem by changing where the candidates come from, not how they are screened once found.

• Pin is a general AI sourcing platform (850M+ profiles from LinkedIn-adjacent networks, GitHub, Stack Overflow, patents and academic databases) that deliberately does not feed demographic data into its matching model, and reports pipelines that are up to 6x more diverse as a result [1]. It competes on the same axis as SeekOut or hireEZ, with diversity-by-design as a differentiator rather than a bolt-on feature.
• Untapped (formerly Jopwell) is purpose-built for underrepresented and early-career talent, with a searchable pool of roughly a million profiles, around 70% from underrepresented backgrounds, and filtering across 75+ data points including demographic and experiential diversity [1].
• Handshake connects to 18 million students across 1,500+ institutions, including 120+ partnerships with HBCUs and Hispanic-serving institutions, making it the strongest option specifically for campus and graduate diversity pipelines [1].
• Mathison, now part of Changeforce, combined a bias-assessment tool (the Equal Hiring Index), an inclusive-language Chrome extension for LinkedIn, and a partner network of diversity-focused sourcing channels [2]. Treat it as a name to verify current availability on, not a settled self-serve buy.

None of these four screen candidates once sourced - that is Layer 3's job, done by Applied or GapJumpers, not by a sourcing platform.

Manual Diverse Sourcing vs an AI-Assisted Approach: What Actually Changes

Task	Manual approach	With inclusive-sourcing AI	What does not change
Writing a job ad	Style guide, gut feel, one editor's judgement	Real-time bias score before it goes live (Textio, Datapeople)	Someone still has to act on the flags
Finding a diverse candidate pool	Posting to a handful of diversity job boards manually	Search across purpose-built diverse-talent databases (Pin, Untapped, Handshake)	Pool depth still varies a lot by function and seniority
First-round screening	A human reviewer sees a name, photo and school on every CV	Anonymised, competency-scored screening (Applied, GapJumpers)	The client still has to agree to the process
Proving the process was fair	No record beyond a hiring manager's memory	An auditable scoring trail, relevant under bias-audit laws	Legal interpretation still needs a compliance review, not just software

What Recruitment Agencies Get Wrong Buying Into This Category

Mistake #1: Buying a screening tool to fix a sourcing problem

Applied and GapJumpers make the screening of candidates you already have fairer. They do nothing to widen a pipeline that was narrow to begin with. If the actual gap is candidate diversity, not decision fairness, that budget belongs in Pin, Untapped or Handshake instead.

Mistake #2: Assuming a general AI sourcing tool is automatically bias-aware

"AI-powered matching" trained on historical placement data can quietly reproduce whatever pattern won in the past. Ask any general sourcing vendor directly whether demographic proxies (name, university, address, photo) ever enter the matching model, and get the answer in writing [3].

Mistake #3: Treating this as a one-off compliance box to tick

A single inclusive job ad or one blind-screened role does not satisfy a diverse-slate clause or an ongoing bias audit requirement. These tools work as a standing process, not a one-time fix before an audit.

Mistake #4: Not checking whether the client will actually accept the process

Blind screening only works if the hiring manager on the other end agrees not to ask for names or photos back before shortlisting. Confirm this with the client before selling them on Applied or GapJumpers as part of the search.

Mistake #5: Assuming acquisition means the product is unchanged

Mathison's move into Changeforce is a reminder that this is still a young, consolidating market. Confirm a tool's current roadmap and support model before signing a multi-year contract, not just its feature list from a 2023 review.

Where boilr Fits - and Where It Honestly Does Not

boilr has no debiasing feature, no blind-screening mode, and no demographic filter, and it should not be reviewed as if it competes with Textio, Applied or Pin on any of those axes. It is a BD tool: Signals monitors funding, leadership moves, expansions and job-posting velocity to flag which clients are about to hire, Companies scores those signals against an agency's ICP, and the Candidates feature sources a shortlist from LinkedIn, GitHub, referral networks and passive pools once a role is briefed, matched against the role and the agency's Company Brain rather than a keyword search [9].

That Candidates feature does not anonymise applicants, does not run a bias audit, and does not deliberately widen a pool toward underrepresented groups the way Untapped or Handshake do by design. If an agency's actual problem is a narrow candidate pipeline or a client contract with a diverse-slate clause, the honest answer is to add one of the Layer 2 or Layer 3 tools above, not to expect boilr to solve it. Where boilr genuinely helps is upstream and adjacent: by scoring which companies to approach on objective ICP-fit and hiring-signal data rather than a recruiter's personal network, it reduces one specific, often-overlooked source of narrowness - the client-side "who do I already know" habit that shapes which roles a desk even works, before sourcing for them begins at all.

Question	Honest answer
Does boilr replace Textio, Applied or Pin?	No. It has no debiasing, blind-screening or demographic-filtering feature.
Should an agency run boilr alongside a dedicated inclusive-sourcing tool?	Yes, if a client requires diverse slates or bias-audited screening. boilr covers the BD and general candidate-sourcing layer; the tools above cover the fairness layer specifically.
Does boilr's Candidates feature widen a candidate pool toward underrepresented talent?	Not by design. It matches against a role brief and the Company Brain, not against diversity criteria.
What does boilr actually contribute to this problem?	It reduces reliance on recruiter-network warmth for deciding which client and role to work in the first place, which is a different, earlier source of narrowness than candidate screening.

See how a signal-led BD layer sits alongside a dedicated inclusive-sourcing stack: try boilr free or book a 20-minute demo to walk through your own desk.

A 30-Day Plan to Add Inclusive Sourcing to an Existing Stack

1. Week 1 - Diagnose the actual layer: audit whether the gap is job-ad language, pool depth, or screening bias. Pull the last 20 shortlists and check where diversity actually drops off.
2. Week 1 - Check client contracts for diverse-slate clauses: know which mandates already require this before choosing a tool, not after.
3. Week 2 - Trial one tool per layer, not five: a job-ad tool (Textio or Datapeople) if the ad is the problem, a pool tool (Pin, Untapped or Handshake) if the pipeline is narrow, a screening tool (Applied or GapJumpers) if bias enters at review.
4. Week 2 - Confirm client buy-in for blind screening before running it on a live mandate.
5. Week 3 - Run one live role through the new tool end to end, tracking the shortlist composition before and after.
6. Week 3 - Document the process for any bias-audit or diverse-slate reporting a client requires.
7. Week 4 - Price against the published ranges above rather than the vendor's opening quote, and confirm current product status on any recently-acquired tool.
8. Week 4 - Decide on the standing process, not a one-off pilot. A single inclusive ad does not satisfy an ongoing compliance obligation.

Frequently Asked Questions

What is the best AI tool for inclusive sourcing in 2026?

There is no single best tool because the category splits into three different jobs. Textio and Datapeople fix job-ad language, Pin, Untapped and Handshake widen the candidate pool, and Applied and GapJumpers remove bias from screening. Pick based on where diversity actually drops off in your own process, not a generic ranking.

Is Textio worth it for a small recruitment agency?

Usually only at volume. Textio's enterprise pricing runs from roughly $15,000 to $50,000 a year, which makes more sense for an agency writing dozens of job ads a month than for a boutique desk running a handful of searches. A smaller team may get more value from Datapeople's tiered approach or a lighter internal style guide.

What happened to Mathison?

Mathison was acquired by Changeforce in May 2024 and now sits inside Changeforce's broader DEIB consulting offering alongside Jennifer Brown Consulting, rather than operating as an independent self-serve product. Its Equal Hiring Index and inclusive-language browser extension are still referenced in market comparisons, but a buyer should confirm current availability directly with Changeforce.

Does blind screening actually reduce hiring bias?

Structured, anonymised screening removes some of the clearest bias vectors - name, photo, age, university - from the earliest review stage, which is the point where research consistently shows unconscious bias has the most room to operate. It does not remove bias entirely, since interviews and final decisions still involve human judgement, but it demonstrably narrows the window where it can act unchecked.

Why does the EU Pay Transparency Directive matter for sourcing tools?

The Directive requires employers to disclose a salary range before interview and run a non-discriminatory, gender-neutral recruitment process, with member states due to transpose it into national law by June 2026, though most missed that deadline. That pushes pay-range accuracy and inclusive-language checks into the same job-ad workflow, which is exactly the ground Textio and Datapeople already cover.

Do agencies need a dedicated tool, or can a general AI sourcing platform like SeekOut or hireEZ do this job?

A general sourcing platform can widen a candidate pool, but most were not built with a specific commitment not to let demographic proxies influence matching, and none of them touch job-ad language or screening bias. If a client contractually requires diverse slates or bias-audited screening, a dedicated tool from this list is the safer choice, run alongside a general sourcing platform rather than instead of it.

Does boilr help with diverse or inclusive sourcing?

Not directly. boilr has no debiasing, blind-screening or demographic-filtering feature, and its Candidates module matches against a role brief and the agency's Company Brain, not diversity criteria. It sits upstream on the BD side, deciding which companies and roles to work based on objective signals rather than recruiter network warmth, which is a different, earlier problem than candidate-pool or screening bias.

What should an agency check before signing a bias-audit compliance tool contract?

Confirm whether the tool's audit trail satisfies the specific regulation a client operates under - NYC's Local Law 144 and the EU AI Act's high-risk deployer obligations have different documentation requirements - and get written confirmation the vendor's bias audit methodology is current, since enforcement in this space is tightening through 2026 and 2027.

Sources

Information sourced from public vendor pages, market comparisons and regulatory guidance as of September 2026.

1. Pin - Best Diversity Sourcing Tools for Inclusive Hiring (2026)
2. Changeforce - Changeforce Welcomes JBC & Mathison to the Team
3. SeekOut - The 5 Best Ways to Use AI Tools for DEI Hiring
4. Vendr - Textio Software Pricing and Plans
5. Sapia.ai - Blind Hiring Software: The 8 Best Tools in 2026
6. Ravio - EU Pay Transparency Directive: The Complete Guide for Employers (2026)
7. Warden AI - EU AI Act for Hiring: Risk Tiers and Compliance Steps
8. SelectSoftware Reviews - Best Candidate Sourcing Tools 2026
9. boilr.ai - Candidates: Signal-Backed Candidate Sourcing
10. Datapeople - Pricing
11. Capterra - Applied Software Pricing and Reviews
12. Deloitte - NYC Local Law 144-21 and Algorithmic Bias
13. Ongig - What Is "Diverse Slate" Hiring? (2026 Update)