Firecrawl
Firecrawl crawls and scrapes websites into clean Markdown and structured data built for language models. It renders the JavaScript-heavy pages that simple HTTP scrapers miss, follows a site to a set depth, and returns content ready for indexing or extraction, through either a hosted API or a self-hosted deployment of the open-source service.
View the repo · firecrawl/firecrawl ↗Teams that need market, competitor, supplier or listing data collected reliably for analysis, and retrieval projects that want a company website or documentation site turned into indexable content. It is aimed at repeatable pipelines rather than one-off copy jobs; for a single page, a simple fetch will do. Anyone whose target data sits behind logins or paywalls should stop and take advice before scraping anything.
Install itpip install firecrawl-pyRespect robots rules and each target site's terms, and collect only permitted, public data; a capable tool does not settle the compliance question. Crawls can balloon, so cap depth and page counts before pointing it at a large site, and expect layouts to change, which means monitoring and re-testing extraction rather than assuming last month's run still holds. Self-hosting the full service involves several moving parts, so budget for that or use the hosted API.
Where Blash AI comes inWe build the research workflow around the crawl: Firecrawl collects, an extraction step structures what matters, a model summarises against your questions, and the output lands in a dashboard or a weekly brief through n8n. Fed into Chroma, crawled content also becomes retrieval material, so an assistant can answer from your market landscape as well as your internal documents.
When you want this running on your real stack, that is the engagement
Book an AI audit →n8n →
n8n is workflow automation you run on your own infrastructure. It connects the applications a business already uses through a visual editor, with hundreds of prebuilt nodes, a code step for anything bespoke, and native support for calling language models mid-flow. Think of it as a self-hosted alternative to Zapier or Make that you own outright, including every piece of data passing through it.
Ollama →
Ollama runs open-weight language models on your own machine or server. One command downloads a model and serves it behind a local API that most existing AI tooling can talk to, so an application built against a hosted provider can point at your own hardware instead. Sensitive data stays inside your network, there are no per-token fees, and it keeps working with no internet connection at all.
LiteLLM →
LiteLLM is a gateway that lets you call models from Anthropic, OpenAI, Google and many other providers through one consistent API. It runs as a Python library inside your code or as a standalone proxy server in front of your whole stack, with cost tracking, per-key budgets, rate limits and automatic fallbacks for when a provider has a bad day.