IvyCheck offers an API that enables real-time web searches and data scraping, providing clean, structured information for AI applications without the need for coding. This technology addresses the challenge of accessing up-to-date web data, ensuring that AI models receive accurate and relevant information without the complications of HTML or JavaScript.
Funding
$500K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Large language models (LLMs) require access to current, factual information to generate accurate and reliable responses, but accessing and structuring real-time web data for LLMs is complex and requires significant engineering effort. Existing web scraping solutions often return unstructured HTML, CSS, and JavaScript, which are difficult for LLMs to process, and can be easily blocked.
Solution
IvyCheck provides an API designed to furnish LLMs with clean, structured, and up-to-date information extracted from the web. The API automates real-time search engine requests and web page scraping, delivering data in a format optimized for LLM consumption. By handling the complexities of web data extraction and structuring, IvyCheck allows developers to focus on building AI applications without needing to manage HTML parsing, anti-scraping measures, or data cleaning pipelines. The API's reranking models ensure that only the most relevant sections of web pages are extracted, respecting token limits and optimizing context window usage.
Target Audience
The primary users are AI application developers and product teams integrating LLMs into their workflows, particularly those requiring real-time, factual information from the web.
Features
- API endpoints for both web search (`/search`) and direct answer generation (`/answer`) based on live web data
- Real-time web search and scraping capabilities designed to avoid blocking
- Data extraction and structuring, eliminating the need to process HTML, CSS, or JavaScript
- Output in Markdown format, optimized for LLM ingestion
- Token limits to restrict result length and ensure context window compatibility
- Reranking models to prioritize the most relevant content sections
- JSON response format including extracted answer and source URLs