About IvyCheck

IvyCheck provides an API that enables real-time web searches and data scraping, delivering structured information without the complexities of HTML or JavaScript. This technology allows enterprise teams to access up-to-date insights from live web data, eliminating outdated information and enhancing decision-making processes.

```xml <problem> Large language models (LLMs) often require access to real-time information to provide accurate and up-to-date responses, but integrating web search and data extraction capabilities can be complex and prone to errors due to dynamic website structures and anti-scraping measures. Existing solutions often return unstructured data, requiring additional processing before it can be effectively used by LLMs. </problem> <solution> IvyCheck provides an API designed to enable LLMs to access and utilize live web data. The API automates real-time web searches and extracts structured information from web pages, eliminating the need for developers to handle HTML, CSS, or JavaScript. By specifying token limits, users can restrict the result length and receive only the most relevant sections, optimized for integration with LLMs. The service also implements measures to avoid being blocked while scraping live websites. </solution> <features> - Real-time web search and data extraction via a simple API endpoint - Structured data output in Markdown format, optimized for LLM consumption - Configurable token limits to control the length of search results - Reranking models to prioritize the most relevant content for LLMs - Automated handling of website scraping, bypassing anti-scraping measures </features> <target_audience> The primary target audience includes AI application developers and enterprise teams who need to integrate real-time web data into their LLMs for tasks such as question answering, content generation, and data analysis. </target_audience> ```

What does IvyCheck do?

Where is IvyCheck located?

IvyCheck is based in Berlin, Germany.

When was IvyCheck founded?

IvyCheck was founded in 2022.

How much funding has IvyCheck raised?

IvyCheck has raised 500000.

Who founded IvyCheck?

IvyCheck was founded by Dustin Lange.

Dustin Lange - Co-founder/CEO

Location

Berlin, Germany

Founded

2022

Funding

500000

Employees

1 employees

Major Investors

Y Combinator

Looking for specific startups?

Try our free semantic startup search

IvyCheck

Score: 100/100

AI-Generated Company Overview (experimental) – could contain errors

Executive Summary

ivycheck.com 300+

Crunchbase

Founded 2022 – Berlin, Germany

Funding

Estimated Funding

$500K+

Major Investors

Y Combinator

Team (<5)

Dustin Lange

Co-founder/CEO

Company Description

Problem

Large language models (LLMs) often require access to real-time information to provide accurate and up-to-date responses, but integrating web search and data extraction capabilities can be complex and prone to errors due to dynamic website structures and anti-scraping measures. Existing solutions often return unstructured data, requiring additional processing before it can be effectively used by LLMs.

Solution

IvyCheck provides an API designed to enable LLMs to access and utilize live web data. The API automates real-time web searches and extracts structured information from web pages, eliminating the need for developers to handle HTML, CSS, or JavaScript. By specifying token limits, users can restrict the result length and receive only the most relevant sections, optimized for integration with LLMs. The service also implements measures to avoid being blocked while scraping live websites.

Features

Real-time web search and data extraction via a simple API endpoint

Structured data output in Markdown format, optimized for LLM consumption

Configurable token limits to control the length of search results

Reranking models to prioritize the most relevant content for LLMs

Automated handling of website scraping, bypassing anti-scraping measures

Target Audience

The primary target audience includes AI application developers and enterprise teams who need to integrate real-time web data into their LLMs for tasks such as question answering, content generation, and data analysis.