Run entity extraction at scale using AI
How to use AI to easily configure and deploy NLP entity extraction at scale
What is entity extraction and why is it valuable?
Entity extraction identifies and categorizes relevant entities, such as names, organizations, or locations, in text data. This lets you easily understand and analyze your text data.
- What it lets you do: Automate content organization, keyword extraction, relationship mapping, trend analysis, and more.
- Use cases: Extract entities from news articles, research papers, blog posts, social media content, legal documents, product descriptions—any text amount of text-based, data large or small
- Some potential impacts on your business: Improved content discovery, enhanced customer engagement, accelerated decision-making, and increased efficiency (no more manually extracting entities from your text data!).
The Old Way: Why entity extraction was previously hard to use
Traditional entity extraction methods faced several challenges:
- Expensive: High costs for manual labor or specialized software.
- Time-consuming: Extensive setup and training times for models.
- Accuracy: Reliant on the quality and quantity of available data.
- Technical skills required: In-depth knowledge of machine learning and programming.
The New Way: Now anyone can perform entity extraction at scale
Introducing AirOps Entity Extract - the easiest, fastest, and most scalable way to perform entity extraction:
Accessible to all: You can easily perform entity extraction with AirOps Entity Extract without any technical background!
Use it anywhere:
- AirOps' Web App
- Browser and IDE Extensions: Chrome/Firefox Extensions or our VSCode Extension
- Directly in Google Sheets: AirOps handy Gsheet formula
- Directly in your data warehouse (e.g., Snowflake) / database with user-defined SQL function
- Via API
Scalable: Extract entities from 1 or 1,000,000+ pieces of text data without compromising performance.
Start free today: Experience the magic with no upfront investment