Announcing o11's backing from Y Combinator.
HR Teams: AI Policy Writing in Google Docs
Every HR department is sitting on a documentation problem they rarely talk about openly. Somewhere in a shared drive, there are forty to sixty policy documents, employee handbooks, onboarding guides, and compliance manuals that all need to reflect current law, current company practice, and current organizational language. When the FMLA rules shift, when your state passes new paid leave legislation, when the company updates its remote work stance for the third time in two years, every affected document needs to change. Not just one document. Every document that references the affected policy.
Most HR teams handle this with a combination of institutional memory and manual search. Someone remembers that the PTO policy is referenced in the handbook, the offer letter template, and the manager’s guide, but forgets it also appears in the onboarding checklist. The result is a documentation landscape riddled with inconsistencies. Employees read conflicting information. Legal reviews surface outdated clauses. New hires receive handbooks that contradict what their manager told them during orientation.
o11 For Google Docs sits inside your existing Google Workspace and treats policy writing as what it actually is: a structured, rule-driven process that benefits enormously from AI assistance. Not a chatbot drafting generic paragraphs, but a creation layer that understands your existing documents, your compliance requirements, and your organizational voice.
Drafting Handbook Sections from Compliance Requirements
The typical process for writing a new handbook section starts with an HR generalist reading through a regulation, cross-referencing it with existing company policies, and then drafting language that satisfies both legal requirements and readability for a general employee audience. This process takes hours per section and requires constant toggling between legal source material and the draft document.
With o11, the drafting step collapses. You feed in the compliance requirements and let the AI produce a first draft that already accounts for the regulatory language.
“Draft a new handbook section on workplace accommodations under the ADA. Reference our existing reasonable accommodation request process in the HR Procedures doc. Use the same tone and heading structure as the rest of the Employee Handbook.”
o11 reads your existing handbook formatting, pulls context from the HR Procedures document in your Drive, and produces a section that fits structurally and tonally with everything already written. The output is not a generic legal template. It is a draft that looks like your HR team wrote it, because it was built from your existing materials.
“Review this draft section against current EEOC guidance on interactive process requirements. Flag anything that is missing or inconsistent.”
Instead of sending the draft to outside counsel for a $500 review of basic compliance alignment, your team gets a first-pass check immediately. The attorney review still matters for final sign-off, but the document arriving on their desk is already 90% there.
Updating Policies When Regulations Change
Regulation changes are the single biggest source of HR documentation pain. A new state law passes, and suddenly your leave policy, your handbook, your onboarding materials, and your manager FAQ all need updates. The challenge is not writing the new language. It is finding every place the old language appears and ensuring consistency across all of them.
o11 handles this because it operates across your Google Workspace, not just inside a single document.
“Our state just passed a new paid family leave law effective January 1. Review our Employee Handbook, the PTO Policy doc, and the New Hire Onboarding Guide. Identify every section that references family leave and flag what needs to change.”
The output is a comprehensive list of affected sections across multiple documents, with specific language highlighted and suggested revisions for each. No more relying on someone’s memory of which documents reference which policies.
“Update all flagged sections to reflect the new 12-week paid family leave entitlement. Keep our existing tone and ensure the eligibility criteria match the statute requirements.”
o11 makes the edits across your documents while preserving your formatting, heading styles, and organizational voice. Every change lives in Google Docs’ native version history, so your legal team can review exactly what changed and when.
Building Onboarding Documentation from Role Descriptions
Onboarding is where HR documentation meets the real world. A new hire’s first week experience depends heavily on the quality of their onboarding materials, and those materials are almost always assembled manually from a patchwork of existing documents. The hiring manager provides a role description. HR pulls the relevant policies. IT provides the systems access checklist. The result is a Frankenstein document that nobody is proud of.
o11 turns role descriptions into structured onboarding guides without the assembly line.
“Create an onboarding guide for a new Senior Account Manager. Pull the role responsibilities from the job description in our Hiring folder. Include relevant sections from the Employee Handbook on PTO, benefits enrollment deadlines, and expense reporting. Add the first-week checklist from our Onboarding Templates folder.”
The output is a single, coherent onboarding document that pulls from your existing sources and assembles them into a guide that reads like it was custom-written for the role. Because structurally, it was. o11 respects the formatting of each source document and harmonizes the tone across sections that were originally written by different people at different times.
“Add a 30-60-90 day milestone plan based on the role responsibilities. Keep the milestones specific and measurable.”
The AI generates milestones that tie directly to the role description, not generic goals like “learn the company culture” but specific deliverables pulled from the actual job responsibilities your team defined.
Before and After: The HR Documentation Workflow
Before o11: A regulation changes. An HR generalist spends a full day searching through shared drives to find every affected document. They manually draft updated language, attempting to maintain consistency across a handbook, three policy documents, and an FAQ. The drafts go to legal review, come back with redlines, and the generalist spends another half-day reconciling changes. Two weeks later, someone discovers the onboarding guide was missed. Total time: 15-20 hours spread across multiple weeks.
After o11: The same regulation change triggers a single prompt. o11 identifies every affected section across all documents in minutes. Updated language is drafted consistently, reflecting both the new regulation and existing company voice. Legal review happens on a complete, consistent set of changes. The entire update, from identification to final draft, takes two to three hours. Nothing gets missed because the search is comprehensive, not memory-dependent.
Why o11 Instead of a General-Purpose AI
Generic AI tools like ChatGPT or Gemini can draft policy language. That is not the hard part. The hard part is drafting language that fits your existing documents, your existing tone, your existing structure, and your existing compliance framework, all at once.
o11 works inside Google Docs. It reads your other documents in Drive. It understands your formatting and heading conventions. When it drafts a new handbook section, that section does not arrive as a plain text blob that needs to be reformatted and pasted in. It arrives as a properly structured section within your existing document, formatted to match what is already there.
For HR teams, this distinction is the difference between “AI that can write” and “AI that can write for us, in our documents, using our standards.” The former is interesting. The latter is genuinely useful.
