Product overview
Document-to-context infrastructure for AI applications.
ContextBoard turns unstructured enterprise documents into the structured, verified context AI systems actually need. It is not another chatbot or another vector database -- it is the layer between your files and every system that reads them: RAG pipelines, extraction workflows, review queues, and autonomous agents.
The pipeline
Every document moves through the same five stages, regardless of format:
- Parsing. A clean-room parser converts PDF, DOCX, PPTX, XLSX, and CSV files into one normalized document model -- headings, paragraphs, tables, lists, and footnotes, not just raw text.
- Extraction. Schema-driven extraction pulls exactly the fields a workflow needs, each with a confidence score and a citation back to the exact source text.
- Review. Low-confidence or required-but-missing fields land in a human review queue -- verify, correct, or reject, with the original model output preserved for audit.
- Context optimization. Context packs bundle selected documents or sections, deduplicate repeated content, and compress to fit a token budget.
- Delivery. Export to Markdown, JSON, OpenAI messages format, or RAG chunks -- or connect directly via the REST API or MCP server.
Explore further
- Document parsing -- the clean-room parsing architecture.
- Extraction and schemas -- field-level configuration and citation.
- Context engineering -- context packs and token budgets.
- Agent integrations -- the API and MCP server.