Knowledge Base
Upload your documents, websites, and files. Orckai converts them into searchable vector embeddings, then uses retrieval-augmented generation to answer questions with precise, cited sources. Your AI stops hallucinating and starts referencing.
From PDFs and spreadsheets to raw text and web pages, Orckai parses virtually any document format your organization works with. Every file is automatically chunked, embedded, and indexed for instant semantic retrieval.
Full-text extraction from single-page and multi-hundred-page PDFs. Orckai handles scanned documents, embedded tables, multi-column layouts, and form fields. Headers, footers, and page numbers are stripped automatically so your search index stays clean and relevant.
Native DOCX parsing preserves heading hierarchy, bullet lists, numbered lists, and table structures. Orckai reads styled content and uses heading levels to create semantically meaningful chunks, so a section titled "Refund Policy" stays together as a single retrievable unit.
Raw text files, log files, configuration files, and any unstructured text content. Orckai applies intelligent splitting to produce high-quality searchable segments even when the source has no formatting or markup to guide segmentation.
Each row in your CSV becomes a searchable record with column headers preserved as context. Product catalogs, employee directories, inventory lists, and pricing tables are all indexed with their column names so queries like "price of Widget X" return the exact row.
Multi-sheet workbooks are processed sheet by sheet. Orckai reads cell values, respects merged cells, and uses header rows to label data. Financial reports, project trackers, and data exports are converted into text representations that preserve the tabular relationships your team relies on.
Markdown files retain their heading structure for natural chunk boundaries. Website scraping fetches live pages, strips navigation and boilerplate, and indexes the meaningful content. Follow links to crawl multiple pages from a single URL, building a knowledge base from an entire documentation site or wiki.
Orckai knowledge bases are designed for organizations that need data isolation, auditability, and integration with existing storage infrastructure. Every knowledge base is scoped to your organization and protected by role-based access control.
Each organization has its own isolated set of knowledge bases. Documents uploaded by one organization are never visible to another. Row-level security in PostgreSQL enforces this at the database layer, not just the application layer. Reprocess any knowledge base at any time to re-chunk and re-embed documents after you change chunking settings or upgrade embedding models.
Point Orckai at a URL and it will fetch the page content, strip navigation chrome and boilerplate, and index the meaningful text. Enable link following to crawl an entire documentation site, help center, or wiki from a single seed URL. Scraped content is chunked and embedded just like uploaded files, so your knowledge base can include both internal documents and public web content.
Connect Google Drive, OneDrive, SharePoint, or Dropbox as document sources. Orckai pulls files directly from your cloud storage, processes them through the RAG pipeline, and keeps your knowledge base current. Combined with the built-in MinIO S3-compatible storage and local filesystem backends, you can centralize knowledge from every system your organization uses.
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