Genderize

Let your AI agent know who it's talking to with Genderize name predictions

Your AI agent predicts customer gender from first names in real time, personalizing salutations and recommendations during live conversations. Process single names or batch up to ten at once, with statistical probability scores that guide confident personalization decisions.

Chosen by 800+ global brands across industries

Statistical name intelligence at conversation speed

Genderize brings probability-based gender predictions into every chat, so your AI agent personalizes interactions with data-driven confidence rather than blind assumptions.

Predict Single Name

Customer shares their first name during chat. Your AI agent queries Genderize, receives a gender prediction with probability and sample count, and personalizes the conversation instantly. High-confidence names get immediate personalization.

Batch Name Processing

Your agent collects multiple names from a group booking or team signup. Genderize processes up to ten names in a single API call, returning individual predictions so the agent can personalize follow-ups for each person efficiently.

Localized Batch Predictions

Processing names from a specific country? Your agent passes the ISO country code to Genderize's localized batch endpoint, improving accuracy for region-specific names. Same names, different countries, different correct predictions.

Confidence Scoring

Every Genderize response includes a probability score and data count. Your agent uses these signals to decide whether to personalize boldly or stay neutral. A 98% confidence triggers full personalization; a 60% confidence triggers gender-neutral language.

Country-Scoped Lookup

An ambiguous name arrives. Your agent detects the customer's country from their browser locale or profile data, passes the country_id parameter to Genderize, and gets culturally accurate results instead of global averages.

Diacritics and Multilingual Support

Customer names with accents, umlauts, or non-Latin characters? Genderize handles them natively. Your agent sends names as-is, and the API applies fallback matching to find results even when exact diacritics do not appear in the database.

Genderize

Use Cases

Data-driven personalization at work

Real scenarios where AI agents use name-based gender predictions to craft tailored interactions, from e-commerce recommendations to event registration follow-ups.

Tailored Product Suggestions Based on Name

A shopper types their name in your e-commerce chat widget. Your AI Agent sends it to Genderize and receives a 94% male prediction. The agent adjusts product carousels and recommendations to prioritize the men's collection. Relevance improves, browsing time increases, and the shopper finds what they need faster without manually filtering.

Group Event Registration Personalization

An event organizer submits ten attendee names through your booking form. Your AI Agent batch-processes all names through Genderize in a single API call, enriches each registration record with gender predictions, and generates personalized welcome emails with appropriate salutations. Your events team saves thirty minutes of manual data enrichment per group booking.

Culturally Aware Customer Greetings

A customer named Yuki messages from Japan. Your AI Agent detects the JP locale, passes both name and country code to Genderize, and receives a localized prediction. Instead of guessing with a global average, the agent uses Japan-specific data to choose the right greeting. Cultural sensitivity handled automatically, zero human intervention required.

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Genderize

FAQs

Frequently Asked Questions

How accurate are Genderize predictions, and how does the agent handle uncertainty?

Genderize returns a probability score (0 to 1) and sample count for each name. Your agent can be configured with a confidence threshold. Names above 85% probability get full personalization. Names below that threshold receive gender-neutral messaging. You control the cutoff based on your brand's comfort level.

Can the agent process multiple customer names simultaneously?

Yes. Genderize supports batch requests of up to ten names per call. Your agent uses this for group bookings, team signups, or any scenario where multiple names arrive at once. Each name gets its own independent prediction with separate probability scores.

What does the count field in Genderize responses mean?

The count indicates how many data rows Genderize examined to produce the prediction. A count of 50,000 for the name 'John' means 50,000 records supported the result. Higher counts mean more reliable predictions. Your agent can use count as a secondary confidence signal alongside probability.

Does Genderize work with names that have accents or special characters?

Yes. Genderize supports diacritics from any language and non-Latin alphabets. The API first tries an exact match, then falls back to a diacritics-stripped version, then attempts to parse full names into first names. Your agent can send names as customers type them without preprocessing.

How is using Tars with Genderize different from calling the API directly?

Direct API calls require custom integration code for each channel and use case. Tars wraps Genderize into conversational logic, deciding when to query, what confidence threshold to apply, and how to adjust messaging. One setup covers web chat, WhatsApp, and SMS with consistent personalization across all channels.

Does Tars store the gender predictions from Genderize?

Tars queries Genderize in real time during conversations. Predictions are used within the active session to personalize messaging. Tars does not build a separate database of gender predictions. If you want to persist this data, configure your agent to write it to your CRM as a separate step.

What happens if Genderize cannot predict gender for a given name?

Genderize returns null for the gender field when no prediction is available. Your agent detects this and falls back to gender-neutral language. The conversation continues smoothly, and the customer never knows a prediction was attempted. No dead ends or error messages.

Can country-specific predictions override global results for the same name?

Yes. Passing a country_id parameter narrows the prediction to country-specific data. The name 'Kim' returns different gender probabilities for South Korea versus the United States. Your agent can detect customer locale and apply the appropriate country filter automatically.

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Privacy & Security

We’ll never let you lose sleep over privacy and security concerns

At Tars, we take privacy and security very seriously. We are compliant with GDPR, ISO, SOC 2, and HIPAA.

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