Purpose-built AI for
top-quality job posts
- and much more
Lyser is now HR-ON Comply. AI that applies 20+ best practices to your job posts, now expanded into full pay transparency software. Pay ranges, language checks, and pay analytics in one place.
⭐️⭐️⭐️⭐️⭐️ 4.8 on G2 and trusted by 100+ Industry leaders
One tool for job posts, language, and pay
Documentation ready when it's needed
Audit-ready documentation is generated automatically, and real-time pay data lets you fix differences before they become an obligation. Reporting becomes a routine, not a race against the deadline.
A bigger, better applicant field
Pay ranges and inclusive, on-brand job posts attract more qualified candidates from day one, instead of losing them before they click apply.
One set of data for HR, Legal, and management
Pay analysis, language checks, and pay ranges in one place, instead of three separate tools. No reconciling numbers before every meeting.
Job ad optimiser
Create or optimise job ads with purpose-built AI, and save up to 90% of the time and cost of writing them.
Check 20+ best practices. Every job ad gets a score, with clear improvement areas and suggested changes you can accept. Set a minimum score before an ad can be published.
Fix issues automatically. Lyser restructures ads that don’t follow your template and adds your DEIB statement where it belongs. Every flagged issue comes with context and a suggested fix.
Start from scratch. Turn hiring manager notes into a finished job ad, built on your guidelines, previous job posts, and proven best practices.
Sound human. Unlike general AI tools, Lyser avoids recognisable AI language, so your job ads read like they were written by people.
Pay transparency, from the job post to the pay report
Pay transparency laws are expanding fast, from California to the EU. It’s not just the law: 68% of candidates expect to see salary information in job posts, and 44% avoid jobs without it.
Compliant before you publish. Every job ad is checked for missing or inconsistent pay details, with location-based checks against the pay disclosure rules where you hire.
Pay ranges based on data. Pay ranges write themselves into the job post, linked to data, so they’re based on facts, not guesswork.
Pay analytics you can explain. See the gender pay gap per category and across the organisation. Compare pay by level, role, and field of work, with dashboards instead of spreadsheets.
Job architecture. Structure positions into job families and career levels, so roles can be compared on an objective basis.
Documentation ready when you need it. Audit-ready documentation is generated automatically, so reporting becomes a routine.
Every job post compliant and on-brand, by default
Most companies’ job posts are scattered across roles, hiring managers, and teams. The result is inconsistency, not from lack of care, but because daily work takes over.
One way of writing, whoever writes. Set up your structure, tone of voice, EVP, and guidelines once. From there, every job post follows them automatically.
Always up to date on the rules. National hiring rules are mapped for pay transparency, inclusive language, DEI statements, and GDPR, so you stay compliant whether you hire in New York, Copenhagen, or Munich.
One dashboard for quality and risk. Track performance and compliance across all job posts in one place. Spot issues before they become problems, without manual checks.
Reach more qualified candidates with the right words
Many job ads contain subtle gender-coded language that signals who belongs. The same bias shows up around age, ethnicity, and neurodiversity, and it quietly shrinks your applicant pool. The fix is simple: replace a few words. The impact is big.
How it works:
- Analyse. Masculine- and feminine-coded words and other biased language are identified.
- Adapt. Get neutral, inclusive alternatives that keep your own tone of voice.
- Publish. Post job ads that meet EU inclusion requirements and attract a broader range of talent.
Backed by research. The language model is built on the research behind Lyser and Develop Diverse, including Gaucher, Friesen, and Kay’s 2011 study on gendered wording in job ads, and is updated with the latest language research. It’s not a general-purpose AI model.
