Check the Gender of a Name

How We Check Gender by Name

Our advanced AI analyzes names using machine learning algorithms trained on millions of data points. We provide a simple yet powerful API that you can easily integrate into your applications for real-time gender prediction.

🚀 API Integration Ready

Get instant results with our RESTful API - perfect for user onboarding, personalization, and demographic analysis.

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Full Name Parsing

We analyze both first and last names to provide more accurate gender predictions using advanced algorithms.

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Multi-Language Support

Our system identifies names across different languages, accents, and cultural backgrounds from around the world.

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Global Coverage

We cover names from 195+ countries including USA, UK, India, China, Brazil, Germany, France, Japan, and many more.

✨ Trusted by developers worldwide•🔒 Privacy-first approach•⚡ Lightning-fast responses

CSV Support for Larger data

firstName	lastName	gender	genderProbability	age	ageProbability	country	countryProbability
Alice	Smith	female	0.98	28	0.94	USA	0.95
Bob	Johnson	male	0.96	35	0.91	Canada	0.92
Charlie	Brown	male	0.89	22	0.87	UK	0.88
Diana	Prince	female	0.97	30	0.93	France	0.91
Emily	Clark	female	0.93	25	0.89	Australia	0.9
Fatima	Khan	female	0.94	27	0.92	Pakistan	0.93
Juan	Martinez	male	0.91	32	0.88	Spain	0.89
Liu	Wei	male	0.92	29	0.9	China	0.9
Olga	Ivanova	female	0.95	31	0.91	Russia	0.92
Sara	Ahmed	female	0.9	26	0.86	Egypt	0.88
Tom	Hansen	male	0.87	34	0.85	Norway	0.86