The Knowledge & Context Orchestrator turns sprawling document stores into clean, permission-aware knowledge any AI can retrieve and answer — prepared once for Claude, GPT, Gemini, NotebookLM or your own RAG, with every claim traceable to its source. The engine beneath Venture Intelligence, available on its own.
Modern AI is limited less by the model than by the data you feed it. KCO turns your document stores into clean, permission-aware, retrieval-ready knowledge — structured, deduped, redacted and sized for any model. Prepare once; ship it to Claude, GPT, Gemini, NotebookLM or your own RAG — or ask it directly. Your knowledge stays platform-agnostic, and every answer traces to its source.
For a few clean files, a direct upload is fine. On real corporate data — messy folders, PDFs, spreadsheets, versions and permissions — it breaks down fast.
A multi-stage pipeline — these are the foundations; the engine layers retrieval, enrichment and research on top.
A header on every file records its Drive path, name, type and ingestion time — so the model always knows where a chunk came from.
Small tables become clean Markdown; large tables become JSONL, one self-contained record per row — no broken lookups or column drift.
Layout-heavy PDFs and images are converted to clean text; with vision on, pages — including diagrams — are transcribed faithfully.
Each chunk carries its source permissions, so retrieval can be filtered to exactly what a given user is allowed to see.
PII is redacted before anything leaves — by configurable rules or Google Cloud DLP — with a full audit trail of what was masked.
Content-hash dedup, near-duplicate detection and newest-wins version grouping — with incremental re-runs that only reprocess what changed.
Every file and chunk is tagged with domain, keywords and classification — so retrieval can filter to exactly the right material.
Boilerplate, redundant whitespace and formatting bloat are stripped and normalized — more signal per token, lower cost, less “lost in the middle.”
Google Docs, Sheets, Excel, PowerPoint, PDFs, images, code and zip archives — resolved by content and extension, even when Drive mis-tags a file.
Every pack KCO writes is Markdown — the native language of large language models. Trained on huge amounts of it, models read its structure instead of guessing, so your knowledge lands cleaner, cheaper and more accurately. Write your prompts in Markdown too, and the model follows you the same way. Across the whole preparation — extraction, token cleansing, de-duplication and lean Markdown — a corpus typically shrinks 90%+, often up to ~95%, measured for every run.
Headings, lists and tables map directly to a document's structure, so the model reasons over the shape of your data — not just the words.
Plain text with tiny markup costs a fraction of the tokens of PDF, HTML or Word — so more of your knowledge fits the context window, at lower cost.
A # goal, ## sections, a - list of requirements and fenced examples make any model follow your instructions far more reliably.
KCO doesn't just tidy files — it prepares knowledge for how modern AI actually retrieves, and can answer over it directly.
Sized, tagged packs for Claude, GPT, Gemini and NotebookLM — plus embedding-ready RAG / vector JSONL. Prepare once, ship to any AI.
Heading-aware chunks carry their document and section context, with per-chunk keywords so your store can fuse keyword + vector search.
Splits at meaning boundaries — paragraph, then sentence — never mid-thought, for sharper recall than fixed-size windows.
Optional section- and document-level summaries indexed beside the chunks, so big-picture and multi-hop questions still land.
An optional knowledge graph and generated question/answer pairs connect facts across documents for analytical, connect-the-dots questions.
Built-in deep research rewrites the question and routes simple vs complex queries to single- or multi-agent answering — every claim cited to its source.
KCO doesn't just prepare your knowledge — it reasons over it. A team of AI agents plans, reads, writes and reviews, each role on the model it's best at, so hard questions get grounded, cited answers.
A lead agent breaks a complex request into focused sub-questions and assigns each one.
Subagents pull the relevant documents at once — with query rewriting (HyDE) to surface the right sources for each part.
A writer agent merges every finding into a single coherent, structured response.
A reviewer agent catches missing pieces and fills them before the answer reaches you.
Simple questions take a fast single pass; complex ones fan out to the full team — chosen automatically.
Every claim traces to its source document, and answers stay within each user's permissions.
KCO began inside a venture fund, but the problem it solves is universal. It has been used for deep research and analysis well beyond VC — including for a Swedish publicly-listed green-energy company, turning a sprawling document base into clean, source-traceable knowledge an AI could reason over.
Per-document access controls and a full audit trail — safe on sensitive IP and financials.
PII redacted before anything leaves your environment.
Scheduled re-runs keep your knowledge base fresh as files change.
