What is Wit?
Wit enables developers to add a modern natural language interface to their app or device with minimal effort. Precisely, Wit turns sentences into structured information that the app can use. Developers don’t need to worry about Natural Language Processing algorithms, configuration data, performance and tuning. Wit encapsulates all this and lets you focus on the core features of your apps and devices.
Wit is a tool in the NLP / Sentiment Analysis category of a tech stack.
Who uses Wit?
4 companies reportedly use Wit in their tech stacks, including Nina, Wbot, and Prattle.
7 developers on StackShare have stated that they use Wit.
- Voice-enabled Android and iOS apps
- Rasberry Pi based home automation commanded by speech
- Google Glass apps accepting voice commands
- Robots and drones dialog interfaces (ROS)
- SMS-based information or remote control services
- IM-based information or remote control services
- "Quick add" features a la Google Calendar (replacing a form with free text input)
- Natural Language querying a la Facebook Graph Search (turning a sentence into a database query)
- Personal Assistants a la Apple’s Siri
Wit Alternatives & Comparisons
What are some alternatives to Wit?
See all alternatives
Iti is an API that makes it very easy for developers to create applications or devices that you can talk to. Any app, or any device, like a smart watch, Google Glass, Nest, even a car, can stream audio to the Wit API, and get actionable data in return. We turn speech into actions. Think Twilio for Natural Language, with Stripe-level developer friendliness.
It is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. It comes with pre-trained statistical models and word vectors, and currently supports tokenization for 49+ languages.
rasa NLU (Natural Language Understanding) is a tool for intent classification and entity extraction. You can think of rasa NLU as a set of high level APIs for building your own language parser using existing NLP and ML libraries.
It provides general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch.
It is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.