AI-Powered Content Extraction
Lutra's AI-powered extraction understands the structure and hierarchy of website data. It can identify fields, types, and even nested information, such as tables within tables or linked datasets, and convert them into structured formats ready for use in databases, spreadsheets, or APIs.
Common use cases
Product and Pricing Catalogs: Collect structured product details like names, descriptions, prices, and categories from e-commerce sites (e.g., Amazon, Shopify).
Real Estate Listings: Extract nested data such as property details, pricing, agent contact information, and location coordinates (e.g., Zillow, Redfin).
Job Market Insights: Scrape job postings with structured fields like job title, company name, salary, and requirements (e.g., Indeed, LinkedIn).
Market Intelligence: Gather competitor product features, pricing tiers, and customer reviews with field-level granularity (e.g., G2, Capterra).
Event Data Collection: Extract structured details like event names, dates, locations, and speaker profiles from event or exhibition websites.
Academic and Research Data: Extract structured references, citations, and dataset details from academic repositories (e.g., PubMed, arXiv).
Inventory Management: Collect SKU-level details, stock levels, and supplier information from supplier or distributor sites.
Data formats
Lutra can parse and produce data formats in any type you want. It uses Python under the hood and understands types natively. For example, it can seamlessly transform raw website data into
JSON for APIs
CSV for analytics tools
Excel for business reporting
Google spreadsheets
How to use Lutra
Start a new chat session with Lutra.
Provide Lutra with an example website you'd like to work with, and what data you want from them. For example,
"Read https://www.zillow.com/profile/Ben%20Kinney and extract the number of sales in the last 12 months, total sales, average price, number of reviews, and rating"
You can include detailed instructions on the data format including data types. You do not need to tell it the HTML structure, Lutra will process the website to be in a format that is adapted for AI extraction.
Refine the extraction with Lutra
Provide feedback to Lutra on the extraction quality and output format. You can tell it data it missed, how you want to format the output differently, and any changes you want to make.
Save the process as a Playbook after you have verified the extraction is working well.
Scale up the extraction to more websites by asking Lutra to process a list of websites (you can provide this via a spreadsheet). You can also automate the process by scheduling the playbook.
Frequently asked questions
What websites can Lutra scrape?
Lutra works best with website that have static pages (possibly rendered with javascript). It can handle most websites including paginated content. If you find that Lutra is not loading the data correctly, you can ask it to "wait a few seconds for the content to load" or provide a specific css element that it should look for.
Is it legal to scrape websites?
Scraping legality varies by jurisdiction and website. Users are responsible for ensuring compliance with local laws and website terms of service.How much data can Lutra handle?
Lutra is scalable and supports everything from small-scale projects to enterprise-level extractions. However, it's always a good idea to verify with small tests before scaling.