Satori HR OS gives people teams governed AI agents that know your policies, control their own data access, and run routine work autonomously. We build it, wire it to your org, and keep it governed.
ask Is this promotion increase within band?
80% of enterprise AI pilots fail within 12 months. Not because the AI was wrong, but because no one governed it. Gartner, 2025
Every AI hiring decision or policy action needs a documented trail. One ungoverned output creates legal exposure that takes months to unwind.
Without access control, an agent can surface a colleague's salary, a disciplinary record, or a confidential exit package. Breaches happen silently.
AI-suggested offers acted on without review. Wrong comp bands applied. The same error repeated across twenty hires, with no way to audit or reverse it.
A plain-language ask goes in. It is routed, checked, gated, and logged. No step is optional, and nothing bypasses the audit trail.
You ask in plain language. The orchestrator routes the request to the right specialist. No manual selection, no prompt engineering.
| Agent | What it handles |
|---|---|
| Recruiting | JD generation, screening, interview kits, offer drafts, pipeline status |
| Onboarding | Day 1 checklists, access requests, buddy assignment, 30/60/90 plans |
| Policy Q&A | Instant answers from the HR manual, flags edge cases, never invents policy |
| Compensation | Revision drafts, band compliance, increment letters, benchmarking |
| Performance | Review prep, manager talking points, normalisation, PIP documentation |
| L&D | Training plan drafts, learning paths, vendor coordination, budget tracking |
| Exit Management | Separation checklists, FnF calculation, knowledge transfer, clearances |
| HR Analytics | Headcount, attrition, hiring velocity, compliance dashboards |
These eight cover the work most people teams repeat every week. The architecture is modular, so we build additional agents around how your organisation actually runs. Recent examples include Attendance and Leave, Payroll query handling, Employee helpdesk, Workforce planning, Compliance and audit reporting, and Benefits administration. Every new agent inherits the same governance: scoped data access, human approval gates, and a full audit trail.
This is what makes it a second brain rather than a chatbot: the system knows your org, respects your permissions, and remembers what it has done.
Domain rules, decision logic, and your org's institutional knowledge live in structured files per agent. Policies, decisions, and context are stored and retrieved by meaning, not reset on every session.
Row-Level Security at the database, not the app layer. Agents inherit the requesting user's permissions on every call. An agent can only see what that user is allowed to see. This is a core part of what we deliver, not a setting you configure yourself.
Every AI action is written to a log that cannot be edited or deleted. No UPDATE, no DELETE. The trail only grows. Compliance, legal, and HR governance all have a single source of truth.
The same question, four askers, four outcomes. Confidentiality is enforced at the database, and the agent never invents a plausible answer it is not authorised to give.
| Asked by | The agent returns |
|---|---|
| Priya's HRBP | Full history, with context |
| A peer manager | "Not authorised" |
| Priya herself | Her own record only |
| A recruiter | "Not authorised" |
| Use case | Today | With the OS |
|---|---|---|
| Repeated drafting | JDs, letters, and checklists done by hand, every time | Drafted in minutes; your team reviews and approves |
| Policy questions | A steady stream of routine queries lands on HRBPs | Answered from your manual; HRBPs freed for real work |
| Review prep | Hours per manager, every cycle | Talking points and data pre-generated; managers edit, not assemble |
| Knowledge retention | Leaves with the person when they resign | Persists in the memory layer for their successor |
One conversation. Two hours of your team's time. A clear picture of what is worth automating first, and what should stay human.
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