CommonAR is an AI-powered networking app that uses facial recognition and augmented reality to enhance in-person events. It helps you identify people around you, understand why you should connect, and provides conversation starters to foster meaningful interactions.
graph.one is an AI-powered personal CRM that helps you map, search, and leverage your professional network. It syncs with your email and calendar to automatically build a relationship graph, allowing you to find the right people through natural language search and uncover the warmest introduction paths.
CommonAR is an AI-powered networking app that uses facial recognition and augmented reality to enhance in-person events. It helps you identify people around you, understand why you should connect, and provides conversation starters to foster meaningful interactions.
graph.one is an AI-powered personal CRM that helps you map, search, and leverage your professional network. It syncs with your email and calendar to automatically build a relationship graph, allowing you to find the right people through natural language search and uncover the warmest introduction paths.
| Feature | CommonAR | graph.one |
|---|---|---|
| Primary category | Management | Relationship Management |
| Added | 2025-08-05 | 2025-08-02 |
| Pricing | Freemium | Free |
| Official website | commonar.com | graph.one |
| Product type | Website | Website |
| Performance data | ||
| User rating | Not verified | Not verified |
| Comments | 0 | 0 |
| Monthly visits | 3.5K | 449 |
| Monthly growth | Not verified | -54.7% |
| Favorites | 127 | 99 |
| Details | View details | View details |
Compare CommonAR and graph.one by monthly reach, traffic trend, visit depth, top regions, and acquisition sources.
In the CommonAR vs graph.one monthly traffic comparison, CommonAR currently shows 3.5K visits and graph.one shows 449; CommonAR has about 7.7 times the visible traffic of graph.one, an absolute difference of about 3K visits. This reflects visible reach, not feature quality or paid users.
Only graph.one has complete third-party traffic details; CommonAR uses visits recorded inside ToolMage. These scopes cannot estimate market share directly, and on-site views should not be treated as the product’s total website traffic.
| Country/region | Percentage | Traffic |
|---|---|---|
| 🇺🇸United States | 100% | 449 |
This in-depth CommonAR vs graph.one comparison uses only the product records, taxonomy, audience, traffic, and community signals available on this page. CommonAR is primarily listed under “Management”, while graph.one is primarily listed under “Relationship Management”, so the first decision is whether your actual task matches their recorded scope.
The structured fields currently show these decision-relevant differences: Primary category (CommonAR: Management; graph.one: Relationship Management); Pricing (CommonAR: Freemium; graph.one: Free); Monthly visits (CommonAR: 3.5K; graph.one: 449); Favorites (CommonAR: 127; graph.one: 99); Website (CommonAR: commonar.com; graph.one: graph.one). These facts are more useful for selection than brand visibility alone.
In the CommonAR vs graph.one monthly traffic comparison, CommonAR currently shows 3.5K visits and graph.one shows 449; CommonAR has about 7.7 times the visible traffic of graph.one, an absolute difference of about 3K visits. This reflects visible reach, not feature quality or paid users.
Only graph.one has complete third-party traffic details; CommonAR uses visits recorded inside ToolMage. These scopes cannot estimate market share directly, and on-site views should not be treated as the product’s total website traffic.
The current traffic scope is not sufficient for a reliable product ranking. Treat monthly visits as a market-interest signal, then decide using taxonomy, use cases, pricing, and a like-for-like trial rather than reading exposure as product capability.
CommonAR and graph.one currently overlap in shared categories: Networking; shared tags: contact management, lead generation, and professional networking. This can place both on the same shortlist, but it does not prove equal implementation, depth, or cost.
CommonAR's unique categories/tags are Management, augmented reality, event management, facial recognition, ios app, and networking; graph.one's are Relationship Management, Lead Generation, AI search, CRM, network analysis, relationship management, startup tools, and venture capital. These unique fields are the strongest differentiators: validate the product whose recorded scope matches the task instead of following traffic alone.
CommonAR has no verified rating, 0 comments, 127 favorites, and 111 likes；graph.one has no verified rating, 0 comments, 99 favorites, and 104 likes。
Neither product has enough rating or comment samples for a credible reputation ranking.
Put CommonAR on the priority trial list when the task aligns with “Management” and especially Management, augmented reality, event management, facial recognition, ios app, and networking. This follows recorded positioning and does not imply unlisted capabilities are absent.
CommonAR also currently records: pricing is freemium, product type is website, 3.5K on-site monthly views, no verified user rating. Verify any hard requirement around price, platform, or reach before trial, and do not let sparse review data substitute for testing.
Put graph.one on the priority trial list when the task aligns with “Relationship Management” and especially Relationship Management, Lead Generation, AI search, CRM, network analysis, and relationship management. This follows recorded positioning and does not imply unlisted capabilities are absent.
graph.one also currently records: pricing is free, product type is website, 449 verified monthly visits, no verified user rating. Verify any hard requirement around price, platform, or reach before trial, and do not let sparse review data substitute for testing.
The available data describes positioning, public visibility, and community signals, but it cannot prove output quality, speed, integration effort, privacy, or long-term cost in your workflow. Before deciding, run the same representative tasks in CommonAR and graph.one, then record completion time, accuracy, manual corrections, and the real paid threshold. A like-for-like trial turns this comparison into a defensible adoption decision.
