# Building a more reliable web for agents and people.

> Learn why Aiscovery is building evidence-based testing for websites used by AI agents, and what it includes today.

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- Last updated: 2026-10-21

We are creating evidence-based quality assurance for a customer channel that websites were not originally designed to serve.

- [Apply as a launch partner](https://aiscovery.io/partners)
- [Review the methodology](https://aiscovery.io/methodology)

## Make agent journeys observable, testable and safer to improve.

A website can be technically available yet fail when an agent must compare options, understand a control or complete a task. Aiscovery connects those failures to step-by-step evidence and practical fixes.

## The interface is changing. The responsibility stays with the website owner.

AI discovery, browser agents and structured tools are becoming additional paths into digital products. Teams need a way to inspect the experience without treating every model error as a website defect.

- **From pages to tasks:** Readiness depends on whether a real goal can be completed, not only whether a page can be crawled.
- **From one browser to many executors:** Results must retain agent, model, policy, viewport and environment context.
- **From scores to evidence:** A useful finding needs an observed result, reproduction path and verifiable final state.

## Product principles guide every audit decision.

The methodology is designed to remain useful as agents, interfaces and emerging standards change.

- **Tasks before vanity metrics:** Critical customer journeys carry more weight than a long checklist of low-impact signals.
- **Evidence before inference:** Literal checks decide a result. An AI check may only confirm it or flag it for review from the same evidence, never turn a failure into a success.
- **Boundaries before automation:** Authorization, stop gates, redaction and test environments are part of the run contract.
- **Specific causes before generic blame:** Every failure is attributed to your website, the agent, a policy boundary or our platform. Platform failures are ours and never count against your site.

## One method, with explicit layers of responsibility.

Aiscovery owns the check definitions, evidence contract and verdict rules. Website owners define expected outcomes and authorize scope. Agent vendors remain responsible for their models and policies.

- **Aiscovery:** Scenario definitions, controlled execution, evidence collection, evaluation and reporting.
- **Website team:** Business expectations, access boundaries, fixtures, acceptance criteria and remediation ownership.
- **Agent provider:** Model behavior, product policy, tool availability and vendor-specific limitations.

## Built privately, described honestly.

Aiscovery is available on paid plans and works closely with a small group of design partners. Public copy separates what runs today from what is marked Coming later.

### Current focus

- Automatic site checks of public pages with a ranked fix list
- AI crawler rules and AI-facing files such as robots.txt and llms.txt
- Named AI agents trying key tasks, with step screenshots

### What we do not claim

- Guaranteed rankings, citations or permanent agent compatibility
- Unauthorized testing or irreversible production actions
- Results for AI agents we did not run

## Bring us one journey worth testing.

The clearest way to contact the team is a launch-partner application with the website, workflow and expected outcome in scope.

- [Start a partner application](https://aiscovery.io/partners)
- [Request a demo](https://aiscovery.io/demo)

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