Diligent is an AI-powered platform for fintechs and banks, designed to automate and enhance due diligence on business customers. It leverages Generative AI to investigate online presence, uncover network connections to high-risk entities, and monitor for emerging risks, strengthening fraud and AML compliance.
Sphinx is an AI-powered compliance platform designed for financial institutions and fintechs. It automates and streamlines KYC, AML, and fraud detection processes by providing real-time adverse media screening, PEP and sanctions checks, and ultimate beneficial owner (UBO) verification. The platform uses AI agents to eliminate compliance bottlenecks, reduce risk, and ensure regulatory adherence with deep, explainable insights.
Diligent is an AI-powered platform for fintechs and banks, designed to automate and enhance due diligence on business customers. It leverages Generative AI to investigate online presence, uncover network connections to high-risk entities, and monitor for emerging risks, strengthening fraud and AML compliance.
Sphinx is an AI-powered compliance platform designed for financial institutions and fintechs. It automates and streamlines KYC, AML, and fraud detection processes by providing real-time adverse media screening, PEP and sanctions checks, and ultimate beneficial owner (UBO) verification. The platform uses AI agents to eliminate compliance bottlenecks, reduce risk, and ensure regulatory adherence with deep, explainable insights.
| Feature | Diligent | Sphinx |
|---|---|---|
| Primary category | Compliance | Compliance |
| Added | 2025-08-14 | 2025-08-03 |
| Pricing | Paid | Paid |
| Official website | www.godiligent.ai | sphinxlabs.ai |
| Product type | Website | Website |
| Performance data | ||
| User rating | Not verified | Not verified |
| Comments | 0 | 0 |
| Monthly visits | 9.3K | 3.4K |
| Monthly growth | 17.5% | Not verified |
| Favorites | 104 | 138 |
| Details | View details | View details |
Compare Diligent and Sphinx by monthly reach, traffic trend, visit depth, top regions, and acquisition sources.
In the Diligent vs Sphinx monthly traffic comparison, Diligent currently shows 9.3K visits and Sphinx shows 3.4K; Diligent has about 2.7 times the visible traffic of Sphinx, an absolute difference of about 5.8K visits. This reflects visible reach, not feature quality or paid users.
Only Diligent has complete third-party traffic details; Sphinx 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 Kingdom | 30.42% | 2.8K |
| 🇺🇸United States | 25% | 2.3K |
| 🇮🇳India | 20.28% | 1.9K |
| 🇳🇱Netherlands | 15.46% | 1.4K |
| 🇪🇸Spain | 8.84% | 819 |
This in-depth Diligent vs Sphinx comparison uses only the product records, taxonomy, audience, traffic, and community signals available on this page. Diligent is primarily listed under “Compliance”, while Sphinx is primarily listed under “Compliance”, so the first decision is whether your actual task matches their recorded scope.
The structured fields currently show these decision-relevant differences: Monthly visits (Diligent: 9.3K; Sphinx: 3.4K); Favorites (Diligent: 104; Sphinx: 138); Website (Diligent: www.godiligent.ai; Sphinx: sphinxlabs.ai); Added (Diligent: 2025-08-14; Sphinx: 2025-08-03). These facts are more useful for selection than brand visibility alone.
In the Diligent vs Sphinx monthly traffic comparison, Diligent currently shows 9.3K visits and Sphinx shows 3.4K; Diligent has about 2.7 times the visible traffic of Sphinx, an absolute difference of about 5.8K visits. This reflects visible reach, not feature quality or paid users.
Only Diligent has complete third-party traffic details; Sphinx 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.
Diligent and Sphinx currently overlap in shared categories: Compliance, Automation, and Fraud Detection; shared tags: AML, fintech, fraud detection, KYC, regtech, and risk management. This can place both on the same shortlist, but it does not prove equal implementation, depth, or cost.
Diligent's unique categories/tags are Business Verification, compliance automation, due diligence, llm, and regulatory technology; Sphinx's are compliance, financial crime, PEP Screening, sanctions screening, and UBO Verification. These unique fields are the strongest differentiators: validate the product whose recorded scope matches the task instead of following traffic alone.
Diligent has no verified rating, 0 comments, 104 favorites, and 112 likes；Sphinx has no verified rating, 0 comments, 138 favorites, and 154 likes。
Neither product has enough rating or comment samples for a credible reputation ranking.
Put Diligent on the priority trial list when the task aligns with “Compliance” and especially Business Verification, compliance automation, due diligence, llm, and regulatory technology. This follows recorded positioning and does not imply unlisted capabilities are absent.
Diligent also currently records: pricing is paid, product type is website, 9.3K 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.
Put Sphinx on the priority trial list when the task aligns with “Compliance” and especially compliance, financial crime, PEP Screening, sanctions screening, and UBO Verification. This follows recorded positioning and does not imply unlisted capabilities are absent.
Sphinx also currently records: pricing is paid, product type is website, 3.4K 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.
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 Diligent and Sphinx, 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.
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