Execution control for AI agents.
ALIS sits between an AI agent and the systems it can act on — deciding what may execute automatically, what requires human approval, and what must be refused.
Every decision is recorded in a tamper-evident, hash-chained, action-level audit trail.
Evaluate ALIS on one synthetic workflow
Small non-production evaluation using synthetic data. No customer data or production access required.
How ALIS works
- 1. An AI agent proposes an action.
- 2. ALIS evaluates the action against execution policy.
- 3. Decision: ALLOW / HUMAN APPROVAL / DENY.
- 4. A permitted action proceeds within bounds.
- 5. The decision is recorded in the tamper-evident audit chain.
Verified current capabilities: execution-policy enforcement · allow / human approval / deny · human-approval gating · blocked-operation refusal · evidence-required execution refusal · fail-closed behavior · tamper-evident hash-chained per-action audit trail.
Synthetic demonstrations exist for financial workflows, cybersecurity / IAM, and regulated workflows. Internal technical demonstrations — not customer traction.
Use cases
Financial services
Control what an AI agent may execute in sensitive workflows.
Cybersecurity
Gate privileged remediation actions and refuse prohibited operations.
Regulated workflows
Require supporting evidence or context before sensitive actions proceed.
Enterprise automation
Add execution authority and human approval between an agent and its tools.
Design partners
We're looking for a small number of design partners to evaluate ALIS on one workflow — synthetic data only, no production access.
Evaluate ALIS on one synthetic workflow
Reza Safarzadeh — Founder / Product Architect · founder@helloalis.com