Job description generation

Job description generation is an automation that turns a short intake, the role, level, must-have skills, and team context, into a complete, structured job post written in your company voice. It uses your past approved postings and brand guidelines so every role reads consistently, and it applies inclusive, bias-aware language by default. It does the first draft a hiring manager would otherwise write from scratch, then hands it over for review. The point is not to invent the role. It is to get an accurate, on-brand draft in front of the hiring manager in minutes instead of hours, ready to edit and post.

A static template gives you the same skeleton every time, but the manager still fills in and words every section by hand, and templates drift as people copy old posts. A generic ChatGPT prompt writes fast but does not know your voice, your past roles, or your standards, so it invents requirements and reads like every other AI post. This is set up on your actual postings and brand voice, checks wording for inclusivity, and pushes the draft into the tools your team already uses. You own the requirements and the final edit. It handles the blank-page part and keeps every role consistent.

It helps, but a person still reviews. Research by Gaucher, Friesen, and Kay (2011) showed that masculine-coded wording in job ads, words like competitive and dominant, makes roles less appealing to women and can shrink your applicant pool. The system is set up to favor neutral, inclusive phrasing and to flag wording that tends to narrow who applies. It does not remove human judgment. It cannot know your full context, and inclusive language keeps evolving, so a hiring manager reviews every draft. The value is a consistent, bias-aware starting point, not a guarantee, and the person posting still owns the final wording.

Yes, always. This drafts. It does not auto-post. Every job description goes to the hiring manager to check the requirements, adjust the seniority and must-haves, and edit the wording before anything is published. The hiring manager owns what the role actually is, because only they know the real must-haves and the team context. The automation removes the blank page and the formatting, and it keeps the voice consistent, but the person opening the role is the one who approves and posts. Nothing reaches a candidate without a human sign-off.

On the drafting and delivery side, it connects to your applicant tracking system, such as Greenhouse, Ashby, Lever, or Workable, so approved posts push straight into your hiring pipeline. It also works with Notion, Google Docs, and Slack for intake and review, and with your CRM such as Attio. It can pull from your existing job posts and brand guidelines wherever those live. Most tools with an API can be added. The audit maps your exact stack and connects the drafting step to wherever your team already opens and manages roles.

Usually one to two weeks. The first days go to gathering your best existing job posts, your brand voice, and the structure you want every role to follow, then setting up the inclusive-language checks. After that the drafting runs against a few real roles so the output gets tuned to your voice and standards before anyone relies on it. You are producing usable drafts quickly, and the tuning window is what makes those drafts read like your company wrote them rather than a generic tool.

Two parts. Tooling runs as a modest monthly cost for the model usage and any connectors, typically a small per-user or per-role range that scales with how much you hire. Implementation is a fixed scope, quoted once the audit maps your voice, templates, tools, and review flow, so you are pricing a defined build rather than an open-ended retainer. The audit itself is where the scope and price get set against every other opportunity in your business, so you are not guessing at effort up front.