# AutoAEO — product

> AutoAEO is a 24/7 Answer Engine Optimization platform that keeps brands, products and services cited by the world's leading AI answer engines.

## Summary

AutoAEO checks whether AI answer engines actually mention a brand when someone asks the questions that matter in its market, then tells that brand what to change so it gets cited. Every check archives the raw engine answers it was based on, together with a content hash a third party can recompute, so results can be audited rather than taken on trust. Coverage, scores and citations are tracked over time so movement is visible.

## What it is

- A platform that measures and improves how often AI answer engines cite a given subject (a brand, product, service, technology, solution, case study or event).
- Not an SEO rank tracker: it reports whether the subject appears inside an AI-written answer, with the sources the engine relied on.
- An optimisation loop: audit, fix, publish, re-check, report.

## Who it is for

- Marketing and growth teams whose buyers now ask AI assistants for recommendations instead of scrolling search results.
- Founders and small teams that cannot afford to be invisible in AI answers.
- Agencies and resellers who need AEO reporting they can hand to their own clients (White Label plan).
- Developers who want AEO data programmatically (Developer plan, public API).

## Capabilities

- Answer engine audits: asks each covered engine the questions that matter in the subject's market and records whether the subject is mentioned.
- Six-dimension scoring: answerability, structure, evidence, freshness, authority and compliance.
- Raw-answer archiving: stores the engine's answer, when it was taken, how it was obtained, and any citations it used.
- Verifiable results: a SHA-256 hash over the archived snapshot lets an independent party confirm a report was not edited.
- Content optimisation: generates drafts aligned with each engine's collection and compliance expectations.
- Citation tracking: monitors citation status over time and reports gaps and movement.
- Competitor and benchmark context: positions a subject against its sector rather than in isolation.
- Reporting: per-subject reports and shareable summaries.
- Public API access on the Developer plan and above: read subjects, audits and usage programmatically, with webhook callbacks for payment and status events.
- Back office for operators: tenants, billing, channels, engines, tickets, simulations, backups and configuration.

## How it works

1. Create a subject: a brand, product, service, technology, solution, case study or event, with its market and language.
2. AutoAEO generates the questions a real buyer would ask an AI engine in that market.
3. Each covered engine is asked those questions; the answer is archived with its timestamp, retrieval mode and citations.
4. The subject is scored on six dimensions that determine how easily AI systems can collect and quote it.
5. AutoAEO produces prioritised fixes and optimised content that match each engine's collection and compliance rules.
6. The material is published, then re-checked on a schedule so citation movement is tracked rather than assumed.

## Audit dimensions

- **answerability** — Whether the subject is described in the direct, question-shaped way an AI engine can lift into an answer.
- **structure** — Headings, lists, FAQ markup and schema that make the content machine-parseable.
- **evidence** — Concrete facts, numbers and named sources — engines prefer claims they can attribute.
- **freshness** — How current the material is; stale pages lose citation share.
- **authority** — Signals that the subject is a recognised source: references, third-party mentions and an authoritative origin.
- **compliance** — Alignment with each engine's collection and content policies, so optimisation is not working against them.

## Engines covered

- ChatGPT (OpenAI)
- AI Overviews (Google)
- Gemini (Google)
- Perplexity (Perplexity AI)
- Claude (Anthropic)
- Copilot (Microsoft)
- Grok (xAI)
- Meta AI (Meta)
- Le Chat (Mistral AI)
- DeepSeek (DeepSeek)

## What makes it different

- Evidence, not opinion: every result carries the raw engine answer it came from, so customers can read what the engine actually said.
- Independently verifiable: a SHA-256 content hash is computed over each archived snapshot and can be recomputed by any third party.
- Honest about method: each snapshot records whether it was queried from the engine or inferred, so model recall is never presented as a real citation.
- Time series, not a snapshot: repeated checks build a history of citation movement that cannot be reconstructed after the fact.
- Sector benchmarks: subjects are compared with their sector, so a score has meaning.
- Compliance-aware: optimisation follows each engine's published collection rules instead of generic keyword tactics.

## Where it is not a fit

- Customers in mainland China — the covered engines and materials are aimed at markets outside mainland China.
- Teams looking for traditional keyword rank tracking; AutoAEO measures citations inside AI answers, not search result positions.
- Anyone wanting a one-off report only; the product's value comes from repeated checks building a history.

Last updated: 2026-09-23. Canonical: https://autoaeo.work/llms.txt