SASKI SDK / Interaction governance

Put the rulebook around
the conversation.

De-risk applications that interact with people by governing input and output, then attesting the resulting decisions.

How SASKI fits

Govern and attest the full application flow.

SASKI SDK application flow showing pre-LLM risk analysis, risk-based prompt assembly or crisis routing, post-LLM validation, and audit evidence.

Select the diagram to open the full-size version.

01 / PRE-LLM

Analyze the request

Evaluate safety, privacy, policy, legal, and jurisdiction signals before content reaches the model.

02 / ROUTE

Respond to risk

Allow low-risk traffic, add configured governance for medium risk, or invoke the application’s critical protocol without calling the model.

03 / POST-LLM

Validate the response

Check model output against configured safety and policy controls before the application returns it to the user.

04 / ATTEST

Retain the decision

Produce attestation with decision details, reason codes, Shadow Mode results, replay data, and reporting evidence.

The interaction boundary

Apply the rulebook before and after the model.

01 / Before the model

Protect sensitive input

Apply configured PII redaction and rulebook checks before content reaches the model.

02 / During the workflow

Route consequential signals

Use deterministic rules for crisis handling, escalation, and other consequential paths.

03 / After the model

Validate the response

Evaluate the model’s output against the rulebook before returning it to the user.

Built around your context

Different users. Different rulebooks.

De-risk the application with controls that reflect its users, data, and consequences.

SASKI SDK separates the governance rulebook from the model’s conversational instructions. Configure the relevant rules, test them against representative traffic, and retain attestation for the resulting decisions.

Start with an evaluation or Shadow Mode discussion to understand the decisions SASKI would make without changing production responses.

Explore the evidence behind the approach ↗

Operational efficiency

Spend tokens on the conversation.

Moving suitable repeated instructions into deterministic rulebooks can reduce prompt overhead. Estimate the effect using your own traffic and input-token price.

Try the Tokenator ↗

Start with your use case

What should your AI
be allowed to do?

Let’s talk