Agify.io
Agify.io is a name-to-age prediction API that estimates the most likely age of a person based on their first name, using statistical analysis of a database of name-age associations derived from social profiles and public records across multiple countries. The API returns a single predicted age along with the number of data points used for the prediction, which serves as an indicator of prediction confidence.
The prediction model works by calculating the average age of individuals with that name in the statistical database. Names that are predominantly given to people born in a specific era — either because the name was fashionable at a particular time or because it has generational cultural associations — will produce reasonably accurate age estimates. Names that span generations evenly will produce an average age close to the population mean with lower discriminatory power.
Country-specific prediction allows the age estimation to be conditioned on a country code (ISO 3166 alpha-2), improving accuracy by using name-age data specific to that country rather than the global dataset. Different cultures have different naming traditions and generational naming trends, so country-specific mode can significantly improve prediction accuracy for regionally concentrated names.
Batch lookup mode accepts multiple first names in a single API request, returning an age estimate for each — making it efficient for bulk enrichment of user databases or contact lists without the overhead of individual API calls per name.
For Nigerian applications, Agify.io provides a practical approach to demographic estimation without requiring users to disclose their birth year or age. While the accuracy for Nigerian-specific names depends on how well-represented those names are in Agify's statistical database, many Nigerian names — particularly those with cross-cultural adoption or those used by the diaspora in countries with larger data samples — will produce useful estimates.
Nigerian e-commerce platforms can use age predictions to refine product recommendation algorithms — different age groups have different shopping preferences, and even rough age-bracket estimates from names can improve recommendation relevance. Nigerian fintech apps with age-gated features can use Agify predictions as one signal in age verification supplementary checks.
Nigerian marketing platforms and digital advertising tools can use estimated age distributions from user name databases to model audience demographics — informing campaign targeting and content strategy with data-driven age segmentation without requiring users to explicitly provide age information.
Like Genderize.io and Nationalize.io, Agify.io offers a free tier of 100 predictions per day without requiring any API key, making it trivially easy to test for a specific use case. Paid tiers at $9/month (1 million predictions per day) and $29/month (10 million per day) support large-scale production use. REST API calls are simple GET requests with the name as a query parameter.
Agify.io's country-specific mode uses localized datasets that account for naming trend generational patterns in specific countries. Nigerian names present a complex case — Yoruba, Igbo, and Hausa names each have their own generational patterns that may not be well-represented in Agify's global dataset. However, Nigerian English names and names common in the Nigerian diaspora may have better coverage.
The sample count field in Agify responses is critical for calibrating confidence. A prediction based on 50,000 samples should be weighted much more heavily than one based on 50 samples. Nigerian developers integrating Agify for decision-making should set minimum sample count thresholds below which predictions are treated as low-confidence and flagged accordingly rather than acted upon automatically.
Agify, Genderize.io, and Nationalize.io form a complementary trio that together provide age, gender, and nationality predictions from names. Nigerian developers building comprehensive user profiling features can call all three APIs for a single name to build a probabilistic demographic profile without requiring users to self-report demographic information at registration.