AEO Table
Summary
Your brand vanishes from AI-generated answers and you have no way to prove it happened, let alone track when it started — because no click fires, no referrer logs, and traditional analytics never saw the loss.
AEO Table addresses that gap by running structured queries across ChatGPT, Google AI Overview, and Perplexity, then freezing each run as an immutable snapshot so you can compare what AI channels said last month against what they say now. The core loop is four steps: define your brand, scope a task with target questions and competitors, trigger a run, and pull a report with citations intact. It works well for teams that need repeatable evidence — share-of-answer metrics, competitor appearances, and the source domains driving citations. The ceiling arrives fast for teams that need to act on that evidence programmatically: there is no API, no webhook, and no way to pipe results into your existing data stack without manual export.
Bottom line: Pick this when a marketing or SEO team needs to show stakeholders that a competitor is eating AI answer share — and plan around it when your data team asks to automate the ingestion.
Pricing Plans
SubscriptionLast verified 2 days ago- Price
- $20/mo
- Free Tier
- 50 credits, 1 active brand, 3 Tasks, watermarked share links and PDF exports
VALIDATE THE WORKFLOW
Use the core AEO Table workflow before connecting paid billing.
- 50 credits
- 100 launch credits
- 1 active brand and 3 Tasks
- Channel selection and basic reports
- Watermarked share links and PDF exports
- Create account
Grow
For operators ready to run recurring AI search visibility checks. Most Popular for focused brand operators.
- 1,000 credits / month
- 2,000 launch credits / month
- Scheduled monitoring and historical report trends
- Full report evidence and citation depth
- 3 active brands and 10 Tasks per brand
Scale 5X
For running more queries, channels, and ongoing monitoring cycles. For higher-volume monitoring.
- 5,000 credits / month
- 10,000 launch credits / month
- Higher-volume recurring Runs across channels
- More room for multi-channel analysis
- 10 active brands and unlimited Tasks per brand
Enterprise
For larger programs that need custom limits, onboarding, and support.
- Custom credit allocation
- Launch and migration support
- Security and procurement workflow support
- Custom contract billing
View full pricing on aeotable.com →
Pricing may have changed since last verified. Check the official site for current plans.
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Pros
Sign in to edit- Immutable Run snapshots freeze the exact question set, providers, and competitor scope alongside the answer evidence, so month-over-month comparisons don't silently shift when AI models update — which means you can defend 'visibility dropped in March' with a timestamped record rather than a hunch.
- Citation-level evidence shows which source domains AI channels are pulling from when they mention or skip your brand, so content and SEO teams can prioritize which third-party placements actually drive AI visibility instead of optimizing in the dark.
- Cross-provider coverage across ChatGPT, Google AI Overview, and Perplexity in a single task run, so you avoid maintaining three separate manual query logs to get a consolidated picture of where your brand stands in AI-generated answers.
- Public share links and PDF export let account managers deliver polished visibility reports to clients or executives without requiring stakeholders to log in, which removes the friction that normally turns good data into a slide that never gets acted on.
- Multi-brand and multi-task structure means an agency managing ten clients can scope each monitoring job independently — separate question sets, competitor lists, and markets — without runs from one client contaminating the evidence for another.
Cons
Sign in to edit- There is no API and no webhook, so every run result lives inside the platform until someone manually exports it. Teams that need AI visibility data joined to a BI dashboard, CRM, or performance report have no automated path — they copy, paste, or download PDFs. When that friction compounds across weekly runs for multiple clients, the manual overhead becomes the bottleneck.
- The credit-based model caps how many queries you can run on the free tier, and the docs describe the free allocation as a one-time launch grant rather than a recurring allowance. Teams that want daily or high-frequency monitoring hit the credit ceiling and must move to a paid tier — at which point they are evaluating cost per run against building their own query layer on top of provider APIs.
- Monitoring is limited to ChatGPT, Google AI Overview, and Perplexity. Teams that need visibility into Bing Copilot, Claude, Gemini, or regional AI answer surfaces have no coverage here. When a client's target market skews toward a provider not on the list, the platform's evidence set is structurally incomplete — and teams in that position move toward custom monitoring solutions that can target arbitrary endpoints.
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About
- Platforms
- Web-based SaaS
- API Available
- No
- Self-Hosted
- No
- Last Updated
- 2026-06-10T08:01:14.354Z
Best For
Who it's for
- Marketing and brand teams
- SEO professionals tracking AI search
- Agencies managing multiple clients
- Companies monitoring competitive AI visibility
- Brand operators needing recurring visibility checks
What it does well
- Track brand mentions across AI search engines
- Monitor how AI systems cite your content vs. competitors
- Measure AI search visibility trends over time
- Generate shareable reports on AI visibility
- Manage multiple brands and monitoring tasks
Integrations
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Frequently Asked Questions
- Is AEO Table free?
- AEO Table is a paid tool ($20/mo). No permanent free tier is offered.
- Is AEO Table open source?
- No — AEO Table is a closed-source tool. Source code is not publicly available.
- What platforms does AEO Table support?
- AEO Table is available on: Web-based SaaS.
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Curated lists that include this category
Most analytics stacks record clicks that never happened because an AI answer recommended a competitor and the buyer stopped there. AEO Table monitors that blind spot by querying ChatGPT, Google AI Overview, and Perplexity with the buyer questions you define, then capturing brand mentions, competitor appearances, and cited source domains in a frozen snapshot the vendor calls a Run. The workflow is deliberately scoped: you set up a Brand, create a Task that fixes the question set, market, and competitor list, trigger a Run, and review the resulting report — which includes an executive summary, provider-level breakdowns, and citation evidence. PDF export and public share links let you hand findings to stakeholders without granting platform access.
The differentiating design choice is immutability. Each Run preserves the exact inputs — questions, providers, competitor set — alongside the answer evidence, so reports function as a historical record of what AI channels actually said rather than a live dashboard that silently updates. That makes it defensible for client reporting or internal escalations where ‘things changed’ is not a sufficient explanation.
For brand and SEO teams running periodic audits or agency practitioners managing multiple clients, the task-and-run model maps cleanly onto a monthly or weekly cadence. The tool breaks down when a team needs continuous monitoring with alerts, wants to join AI visibility data to CRM or BI tooling, or needs to run queries at a volume that exceeds the credit allowance on the free tier. There is no API to automate runs or export data programmatically, which means any workflow beyond manual review requires copy-paste or PDF extraction. Teams with engineering capacity who hit that wall tend to move toward building custom query pipelines against provider APIs directly.
Infrastructure is U.S.-hosted by default, and the vendor states that hosting, database, and AI routing use SOC 2 audited providers — relevant for teams in regulated industries where data residency is a compliance checkbox rather than a nice-to-have.