Taylor AI

What is Taylor AI?

Taylor AI is an advanced data engine designed to work with unstructured natural language data. It operates similarly to BigQuery or Athena but is specifically tailored for handling unstructured content. Engineers can use Taylor AI to craft data pipelines that extract valuable information from cluttered cloud file systems, ensuring that datasets remain clean and curated for use. Taylor AI is dynamic and adapts to daily fluctuations in data requirements, allowing pipelines to evolve as needed. Users can train AI models using Taylor AI's AI Toolkit to classify text data during ingestion, integrating functions like text embedding and classification directly within the data pipeline. This facilitates structuring data and understanding user messages and sentiments, leading to actionable insights such as purchase propensity.

Who created Taylor AI?

Taylor's founder is not explicitly mentioned in the provided documents. However, it is stated that Taylor AI, Inc. is the company behind the Taylor data engine. The company is located at 2261 Market Street, San Francisco, CA 94114. If you need more detailed information about the founder, further research beyond the provided documents may be necessary.

What is Taylor AI used for?

• Enriching External, Third Party Data
• Automating the structuring of unstructured data
• Creating specific data classifiers
• Working with unstructured natural language data
• Crafting data pipelines to extract valuable information
• Training AI models to classify text data
• Integrating functions like text embedding and classification directly within data pipelines
• Handling the complexity of understanding user messages and sentiments
• Determining user propensity to buy from natural language inputs
• Seamlessly working with cloud services like S3
• Dynamic Data Pipelines
• AI-Driven Classification
• Customizable Data Extraction
• Integration-Friendly
• User-Centric Insights
• Creating data pipelines to extract valuable information from cluttered cloud file systems
• Training AI models to classify text data during ingestion
• Offering tools to create specific data classifiers
• Dynamic Data Pipelines: Adapts to daily data requirement changes
• AI-Driven Classification: Automates the structuring of unstructured data
• Customizable Data Extraction: Offers tools to create specific data classifiers
• Integration-Friendly: Seamlessly works with cloud services like S3
• User-Centric Insights: Determines user propensity to buy from natural language inputs

Who is Taylor AI for?

• Software developers
• Engineering managers
• Engineers

How to use Taylor AI?

To use Taylor effectively, follow these steps:

1. Understand Taylor's Functionality: Taylor is a specialized data engine for unstructured natural language data similar to BigQuery or Athena, tailored for unstructured content.

2. Create Dynamic Data Pipelines:

   • Craft data pipelines to extract valuable information from messy cloud file systems.
   • Benefit from its adaptability to daily data requirement changes.

3. Utilize AI-Driven Classification:

   • Train AI models with Taylor's AI Toolkit to classify text data during ingestion.
   • Automate the structuring of unstructured data for easier analysis.

4. Customize Data Extraction:

   • Use Taylor's tools to create specific data classifiers as per your project needs.
   • Tailor the extraction process for a more personalized data structuring experience.

5. Integrate Seamlessly:

   • Easily integrate Taylor with cloud services like S3.
   • Embed text and utilize trained classifiers for efficient data structuring within your pipelines.

6. Obtain User-Centric Insights:

   • Understand user sentiments and messages to derive actionable insights like purchase propensity.

7. Try Out Taylor:

   • Sign in with Google or GitHub to start using Taylor without the need for a credit card.
   • Take advantage of the user-friendly interface and powerful machine learning capabilities from day one.

By following these steps, you can effectively harness Taylor's capabilities for structuring unstructured data and deriving valuable insights for your projects.

This guide outlines the key steps to maximize the utility of Taylor as a valuable tool for handling unstructured natural language data efficiently.