Your team wants AI that can answer questions, route approvals, and get things done. But the data it needs — salaries, contracts, performance reviews, negotiation positions — has rules about who sees what. Those rules live in people's heads. In the org chart everyone sort-of knows. In the policies nobody reads. You can't hand that to an LLM and hope.
Three guarantees. All in code.
Not guidelines for an LLM. Hard boundaries that can't be talked around.
Identity-aware
Every request is tied to a real person in your org chart. Oker knows who's asking and what they're allowed to see — before anything is returned.
Policy-enforced
Access checks run inside tool functions, not in system prompts. A contractor can't sweet-talk their way to salary data.
Fully auditable
Every data access, every decision, every tool call — logged. Not just 'the AI responded' but exactly what it accessed, for whom, and why.
Same question. Different outcomes.
A contractor asks about salary ranges for an open role.
What's the salary range for the senior engineer role?
The approved range is 120k–150k. The hiring manager noted they'd accept 115k if other qualifications are strong.
What's the salary range for the senior engineer role?
There's an open Senior Engineer role. Salary details are restricted to the hiring manager and HR. I can connect you with the right person.
The layer between
Your team talks to Oker in Slack. Oker handles identity, access, and routing — then delegates safely.
Starts with people ops.
Expands to everything.
People data is the hardest to get right — salaries, contracts, performance. We start there because if we can secure that, we can secure anything.
Shared primitives that work for any domain:
“We wanted AI agents but couldn't solve access control. Oker solved it in a day.”
“The audit trail alone justified it. We can show exactly what the AI accessed and why.”
“We went from 'AI is too risky' to 'AI is deployed' in a week.”