SearchCans

SearchCans is an API-first data layer for AI applications that need fresh web search results and clean page content. It combines Google and Bing SERP APIs with a Web to Text “Reader” API so agents, RAG pipelines, and LLM tools can search, fetch, and turn messy pages into structured, LLM‑ready text in a single workflow. Its architecture focuses on high throughput, real-time search data, and predictable pricing for developers building AI systems.

What is SearchCans? SearchCans is an API-first data layer for AI applications that need fresh web search results and clean page content. It combines Google and Bing SERP APIs with a Web to Text “Reader” API so agents, RAG pipelines, and LLM tools can search, fetch, and turn messy pages into structured, LLM‑ready text in a single workflow. Its architecture focuses on high throughput, real-time search data, and predictable pricing for developers building AI systems. Key Features: Multi‑Engine Search APIs: Unified POST endpoint for Google and Bing search, including organic results, People Also Ask, and Knowledge Graph fields tailored to AI grounding. Reader API (Web to Text): Converts any URL into structured JSON and Markdown, stripping boilerplate so text can feed directly into RAG or summarization steps. Parallel Lanes & Lane Stacking: Concurrency model with “lanes” for many requests in flight at once, plus stacking plans to raise throughput without changing integration code. Developer‑Friendly Responses: Clean JSON schemas that work smoothly with tools like LangChain and LlamaIndex, plus straightforward Bearer token authentication and clear error codes. Pros AI‑Native Design: Search plus Reader combination fits agentic workflows and RAG pipelines without custom scraping infrastructure. Cost‑Focused Pricing: Credits start at roughly $0.