Wit.ai

Intents, Entities, and Traits

Model user goals as intents and extract entities like locations, times, or product names. Traits capture style or sentiment that shapes responses. Label examples, add variations, and evaluate coverage. With clearly defined meaning, apps route actions correctly, validate inputs, and respond consistently, even when phrasing shifts between short commands, questions, and conversational turns. Utterance history improves intent recall while reducing surprising matches.

Training and Evaluation

Import utterances, bootstrap from logs, and balance classes. Tests measure precision, recall, and confusion across intents and entities. Suggested examples target weak spots. Versioned datasets keep experiments traceable. By running evaluation regularly, teams avoid silent regressions and maintain accuracy as new features, markets, and seasonal phrases enter traffic for the assistant. Locale models cover many languages for assistants, bots, and devices globally.

Realtime Parsing APIs

Send text or audio transcripts to parse and receive intents, entities, traits, and confidence. Low latency endpoints support interactive chat and voice. Rate limits and retries handle bursts. Structured outputs slot into business logic without brittle regex. Because results are consistent across locales, products scale language features without rewriting routing layers per region. Roles and permissions protect apps, validation, and production access safely.

Multilingual and Localization

Create apps per locale and reuse patterns. Models support several languages and scripts. Transliteration and normalization ease mixed content. Training can share concepts while adjusting examples to culture specific terms. This design helps teams ship features globally while keeping meaning precise, reducing maintenance and improving user experience across diverse audiences. Metrics report confusion, coverage, and drift with exportable snapshots today.

Security, Governance, and Ops

Role based access protects apps and training data. Webhooks connect review flows. Exports archive models for audits. Analytics track drift and failure modes by slice. Conflict checks warn about overlapping intents before they ship. With governance and visibility in one place, organizations evolve assistants confidently while keeping predictable operations and clear ownership. Batch import and export keep training sets consistent across environments now.