Text Classification for Developers
Taylor is the API for classifying unstructured text in real-time. Developers use Taylor's API to label and build products around their free text without the infrastructure and maintenance overhead. Taylor has helped engineering teams efficiently tag large-scale text data (like user-generated content or web-scraped data). Why Taylor? Our customers previously relied on LLMs for tagging text. They soon hit rate limits, high latency, and accuracy issues (LLMs are optimized for generative tasks, not structured classification). With Taylor, engineering teams get access to our proprietary models purpose-built for text classification. We have no rate limits, guaranteed latency, and straightforward pricing.
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Hey everyone! 👋 We're Ben & Brian.
TL;DR Use Taylor’s API to tag your free text faster and cheaper than a LLM can.
THE PROBLEM High-volume, high-frequency text data (like user-generated content, chat completions, and lead communication logs) is difficult to sort, productize, and manage. You can tag text with LLMs, which works well on smaller datasets but is challenging at scale due to rate limits, high latency, and accuracy issues (“creative” labels). What if you could classify text in real time with no rate limits?
OUR SOLUTION An API for classifying your text in milliseconds into hierarchical labels. For example, use our out-of-the-box topic classification model to tag your free text by topic.
OUR ASKS
- Make a request to our topic classification model! Log in, grab your API key, and then use this starter code to get back topic labels.
- Not the labels you need? Email us (founders@trytaylor.ai) or book time here.
- Follow us on Twitter and LinkedIn to stay on top of our latest models and roadmap!
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How did your company get started? (i.e., How did the founders meet? How did you come up with the idea? How did you decide to be a founder?)
Ben and Brian met at the 2015 Speech & Debate Nationals the summer before the start of their freshman year at Stanford. Since then, they’ve been best friends and are currently roommates in San Francisco. After graduating with his master’s in computer science, Ben was most recently an applied machine learning researcher in a Stanford research lab. After graduating with his bachelor’s in engineering, Brian was most recently a product manager on the front lines of early AI adoption in industry. Ben and Brian started Taylor because they saw companies rushing to adopt AI but struggling to adapt one-size-fits-all chat models for their use cases.