Stop rewriting the same job description from scratch every time
AI generates consistent job descriptions grounded in your role taxonomy, past JDs, and company tone. Recruiter reviews and customizes the strategic bits.
Discuss a pilot →Quick facts
- Business size
- HR teams writing > 20 JDs / year, staffing agencies, recruiting operations
- Timeline
- 3-4 weeks test, 1-2 months pilot, 2-3 months production
- Budget range
- Pilot from €15k.
- Hardware
- Cloud-based.
- Data needed
- Past JDs (20+ ideal), role taxonomy, company tone / brand guidelines.
- Evolution
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- Genesis
- Custom-built
- Product
- Commodity
Solved and everywhere. Wire up a service and go.
What this scale means
Further right means more proven and cheaper. Further left means newer and riskier. Here is the test for each step.
- Commodity
- You could get the result yourself from a ready service, with almost no work. We rarely take these on.
- Product
- A vendor already sells this result turnkey, like shelf recognition from Trax or document reading from ABBYY. If one of them fits you, use it. You come to us when it does not: when it has to run on your own servers, cost less, or fit systems the product cannot reach.
- Custom-built
- Vendors sell only the parts. A tool like Tableau hands you charts, but the dashboards and metrics for your business still have to be built. That build is the work, and that is us.
- Genesis
- The approach exists but does not work reliably yet. You are betting on it maturing, so it costs more and carries more risk.
Expected outcomes
The Problem
Hiring managers and HR teams write job descriptions from scratch, every time, despite having written dozens of similar ones before. The output is inconsistent across the team, different sections, different tones, different boilerplate. Some are great, some are mediocre, and the variance affects how candidates perceive the company.
Worse: most JDs miss things that should be there. The right benefits section, the right culture-fit framing, the right candidate-stage messaging. Templates exist; they’re rarely current; nobody uses them.
LLMs make this much easier. A JD-generation tool grounded in your role taxonomy, past JDs, and company tone produces a consistent first draft in minutes. The recruiter or hiring manager reviews, customizes the strategic bits (specific team context, unique role challenges), and ships.
What the Solution Does
A JD-generation tool tuned for your company.
- Role taxonomy, structured catalog of role types, seniorities, departments.
- Past-JD corpus, your historical JDs feed the model.
- Company tone, your brand voice, benefits, culture-fit framing.
- Generation, given the role and its context, AI generates a structured draft.
- Review, recruiter / hiring manager customizes specific elements.
- Publish, to your ATS, careers page, job boards.
Where It Fits
This makes sense if you…
- Write > 20 JDs / year
- See inconsistency across team-generated JDs
- Have past JDs to learn from
- Want to upgrade JD quality without adding recruiter time
This is probably not the right time if you…
- Hire rarely
- Have a tightly-template-driven JD process that already works
- Have no past JD corpus to ground generation
Business Value
Drafting time. Typically a 70-90% reduction. A 1-hour JD becomes a 5-minute review.
Consistency. One structure, one benefits framing, one tone across the team.
Quality uplift. Less-experienced recruiters produce JDs closer to senior-recruiter quality.
Application-rate impact. Possible, better JDs attract better applicants. We measure during pilot but don’t promise large lifts.
How It Works
1. Configuration
We work with you to define role taxonomy, brand voice rules, required JD sections, and benefits / culture content. This becomes the canonical configuration.
2. Past-JD ingestion
Your historical JDs feed the model. The AI learns your voice, your typical structures, your phrasing patterns.
3. Generation
Hiring manager fills a brief form: role, seniority, key responsibilities, team context. AI generates a structured draft with all standard sections (mission, responsibilities, requirements, nice-to-have, benefits, culture).
4. Review and customization
Recruiter / hiring manager reviews in their preferred document tool. Customizes strategic bits (team-specific context, unusual requirements, salary range).
5. Publish
Output to ATS, careers page, job boards via API. Or copy-paste, many ops prefer this.
Stack
LLM-backed (OpenAI / Anthropic / self-hosted), RAG over past JDs, structured template engine, integration with ATS / careers-page API.
What You Need to Make This Work
Data. Past JDs (20+ ideal). Role taxonomy. Brand voice / culture content.
Integrations. ATS or careers-page integration (optional).
Hardware. Cloud-based.
Team. HR lead. Hiring managers for pilot.
Implementation Roadmap
1. Test (3-4 weeks)
Configure taxonomy. Ingest past JDs. Build generator. Pilot with 2-3 hiring managers. Output: a working generator with measured time savings.
2. Pilot (1-2 months)
Roll out to broader hiring team. Iterate on tone and structure. Output: a working production deployment.
3. Production (2-3 months)
Full rollout. Continuous improvement.
Keep in Mind
- Bad past JDs lead to bad new JDs. If your past JDs have problems (jargon, bias, length), the AI learns them. We curate past examples.
- Bias risk. JD language can deter under-represented candidates. We integrate bias-detection tools where appropriate.
- Generic AI tone is bad. “We’re looking for a passionate, hard-working individual” reads like a chatbot wrote it (because one did). Tone tuning is real work.
- Salary disclosure rules vary. Some jurisdictions require salary in JDs. The system should respect local rules.
- One-off bespoke roles still need human authorship. Some roles (founding-team, unique cross-functional, executive) benefit from real human writing. The system shouldn’t replace that.
FAQ
How does this differ from off-the-shelf tools (Ongig, Textio)?
Commercial JD tools are excellent for organizations that want a ready-made product with no setup. Our approach is the right choice when you need: custom taxonomy, fully on-prem deployment, integration with non-standard ATS, or substantial tone-customization.
Multi-language?
Yes. LLMs handle multilingual JD generation; per-language tuning recommended.
Will the AI handle bias detection?
Light integration with bias-detection libraries. Full bias-audit is a separate scope.
Can the system handle role-progression / career-ladder JDs?
Yes, structured role taxonomy supports this.
Ready to Discuss?
If you write JDs at meaningful volume and consistency / quality is a real pain, this is a quick deployment. We’ll walk through your past JDs and your taxonomy, and tell you what to expect.
Part of: AI Assistants ↗