Unstructured
Unstructured is a Python library that turns messy real-world files into clean, structured elements an AI workflow can use. It ingests PDFs, Word documents, PowerPoint, emails, HTML and scanned images, detects titles, paragraphs, lists and tables, and emits typed elements with metadata ready for chunking and embedding. It exists because business documents rarely arrive as tidy text.
View the repo · Unstructured-IO/unstructured ↗Anyone extracting fields or text from statements, contracts, invoices and reports at volume, and any retrieval project whose source material lives in mixed formats. It suits pipelines that need one consistent interface across many file types rather than a separate parser per format. If everything you process is a dense, table-heavy PDF, a dedicated converter such as Marker from this library may give cleaner output for that specific shape of document.
Install itpip install "unstructured[all-docs]"The full installation pulls in heavyweight dependencies, including OCR engines and layout models, so build it into a container once rather than fighting local environments repeatedly. Scanned documents route through OCR, where quality varies with the scan, so keep confidence checks in the pipeline. Above all, reconcile extracted figures against a source of truth: a parser that is right most of the time still needs an exception queue for the times it is wrong.
Where Blash AI comes inDocument extraction is a common first Blash build because the payback is quick and measurable. We tune the parsing to your actual document types, reconcile extracted data against your accounting or operational systems, queue low-confidence items for human review, and feed the clean output into retrieval on Chroma or workflows in n8n rather than leaving it in a folder.
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.