Sistava

AI Policy and SOP Enforcement | Consistent Rule Application

AI Operations Team

Apply your rules consistently to every request

Policy and SOP Enforcement makes sure every ticket, request, and exception is handled the same way your best operator would handle it. Your AI Employee reads the relevant policy, checks the inputs against it, and either acts within the rules or escalates the case for human review. No more drift between operators. You upload your policies, SOPs, and runbooks once. The Employee maps each incoming request to the matching policy, walks through the steps in order, and records what it did at each one. When an exception falls outside the rules, the Employee flags it with the relevant policy passage so a human can decide. Manual enforcement varies by who is on shift. Standard operating procedures exist on paper but get applied differently in practice. With an AI Employee in the loop, the policy is the path. The same input produces the same outcome whether the request lands at 9 AM or 3 AM, on a Monday or a Sunday.

Benefits

How It Works

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At a Glance

< 1 min
Policy match per request
100%
Cases logged with policy version
24/7
Consistent rule application
80%
Reduction in policy variance

Policy as the single path

Every request follows the policy, end to end. The Employee does not improvise based on what feels right. It reads the rules, checks the inputs, and either takes the action the rules allow or escalates. This eliminates the variance that comes from different operators handling the same case differently. Two requests with the same inputs produce the same outcome, regardless of when they arrive or which Employee picks them up. That is the contract policy was always meant to provide and rarely did in manual operations.

Exception escalation with context

Real work has cases that policy did not anticipate. When an Employee hits an input combination that falls outside the rules, it stops, packages the case with the matched policy and the reason it could not act, and escalates to a human reviewer. The reviewer decides, and the decision can be encoded as a new branch in the policy so the same exception is handled automatically next time. The library gets sharper with each escalation, and the rate of escalations drops as the policy matures.

Audit trail per case

Every case is recorded with the policy version applied, each rule that was checked, the inputs at each step, and the final outcome. Auditors can replay any case and see exactly what the Employee did and why. Compliance reviews stop being a sampling exercise and become full coverage. When a customer disputes an outcome, you can show them the policy that was in effect, the inputs at the time, and the action the rules required. Disputes get resolved on facts instead of memory.

FAQ

How does the Employee know which policy applies?

It reads the request type, fields, customer tier, and other context, then matches against the conditions in each policy. When two policies overlap, you can set a Duty about which one wins or have the Employee escalate the conflict.

What happens when a policy gets updated?

The Employee picks up the new version on the next request. The audit log records which policy version was applied to each case, so you can see exactly when behavior changed and trace any case back to the policy in effect at the time.

Can the Employee handle exceptions or edge cases?

It tries the policy first. When inputs fall outside the rules, it escalates with the relevant policy passage, the request data, and a reason. A human decides and the decision can be added to the policy as a new branch for next time.

How do we know the policy was applied correctly?

Every case has a full audit log with the matched policy version, each step the Employee took, and the final outcome. Reviewers can replay any case and verify the rule was followed.

Does this work with policies in PDF, Notion, or Confluence?

Yes. The Employee reads policies from the connected sources directly. Updates in Notion or Confluence propagate within minutes. PDFs are indexed at upload and re-indexed when replaced.