AEO Table vs Agentype

AEO Table and Agentype are both business tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

AEO Table

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.

Agentype

Spotter runs the lead lifecycle on autopilot: capturing contacts from multiple listing sources, qualifying them through SMS and WhatsApp conversations, matching them to properties, and scheduling viewings — without a human touching the thread until a warm handoff. The vendor states the AI assistant 'acts immediately' on natural language commands, so pipeline moves happen as you describe them rather than through menu clicks. Lead fatigue prevention is a stated design goal, meaning the system tracks contact frequency to avoid burning prospects. Where it breaks: the scraped page content does not support claims about CRM integrations, MLS data connections, or API extensibility beyond what the vendor describes generically, so teams with complex existing tech stacks should verify compatibility before committing.

Attribute	AEO Table	Agentype
Pricing	Paid	Paid
Price	$20/mo	$79/month
Free trial	No	14 days
Open source	No	No
Has API	No	Yes
Self-hosted option	No	No
Platforms	Web-based SaaS	Web (cloud-based); mobile access mentioned
Pros	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.	Automated first-response over SMS and WhatsApp means a lead who submits at midnight gets a qualifying conversation started before your competitors open their laptops. Lead fatigue prevention tracks contact frequency across the pipeline, so the system stops messaging a prospect who has gone cold rather than burning them with a sixth follow-up. Natural language pipeline control means moving a deal forward or reassigning a lead is a typed instruction, not a sequence of CRM field updates — which removes the administrative overhead that causes pipeline data to go stale. MLS listing description and social media post generation runs from the same lead and property data already in the system, so agents avoid re-entering information into a separate content tool. Intelligent property-to-lead matching against stated preferences reduces the manual work of sorting which listings to send to which buyers — a task that compounds badly across a 50-lead pipeline.
Cons	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.	The vendor page does not document specific CRM integrations or MLS data connections. A team running an established CRM cannot confirm data sync behavior before starting a trial — and if the integration does not exist, they are maintaining two separate systems or migrating cold, which is a project, not an onboarding. No self-hosted option is available. Teams operating under data residency requirements or brokerage compliance policies that restrict cloud data handling have no deployment path here — that is the condition under which they go to a competitor offering on-premise or private-cloud deployment. The AI qualification and follow-up conversations happen over SMS and WhatsApp, which are the right channels for many markets but wrong for enterprise or commercial real estate buyers who expect email-first or portal-based communication — the system's engagement model does not flex to those buyers.

Bottom line

Only Agentype exposes a public API. Choose based on which difference matters most for your workflow.

Frequently asked questions

What is the difference between AEO Table and Agentype?

AEO Table is Paid, while Agentype is Paid. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is AEO Table better than Agentype?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.