> For the complete documentation index, see [llms.txt](https://intelfactor.gitbook.io/doc/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://intelfactor.gitbook.io/doc/intelfactor-ai/validation-and-rollout/validation.md).

# Validation

How IntelFactor is validated on-site, and what evidence is produced.

Defines on-site validation gates and the evidence you should expect.

### What this covers

Validation means proving the system behaves as specified in your environment.

This documentation stays customer-safe.

It focuses on:

* Acceptance criteria and rollout gates
* Evidence and traceability expectations
* Operational checks before and after go-live

### Typical rollout gates

Teams usually validate in stages.

* **Connectivity gate** Event delivery works end-to-end.
* **Continuity gate** Outcomes continue locally during WAN loss.
* **Evidence gate** Each outcome links to auditable evidence artifacts.
* **Review workflow gate** Humans can review, override, and annotate outcomes.

### What “acceptance tests” look like (conceptually)

Acceptance tests should be observable and repeatable.

Examples:

* Defect injection or representative defect sets
* Known-good control samples
* Latency and throughput checks at the inspection point
* Failure drills (power loss, network loss, storage pressure)

### Out of scope

We do not publish:

* Model internals or training methods
* Customer thresholds and tuning values
* Site-specific runbooks

For deployment-specific validation plans, contact your IntelFactor point of contact.

### Next steps

* [Acceptance tests & rollout gates](/doc/intelfactor-ai/validation-and-rollout/acceptance-tests-and-rollout-gates.md)
* [Evidence & retention policy](/doc/intelfactor-ai/architecture-and-data/evidence-and-retention-policy.md)
* [Interfaces](/doc/intelfactor-ai/architecture-and-data/interfaces.md)


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://intelfactor.gitbook.io/doc/intelfactor-ai/validation-and-rollout/validation.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.