Most of what you know isn't in any AI model. It's in your PDFs, reports, research papers, meeting notes, and manuals. Generic assistants can't see any of it, so they answer your specific questions with general knowledge — confidently, and often wrong.
VeelIQ Context turns those documents into a private knowledge base. Upload once; the content is chunked and indexed. From then on, any connected AI client can search it semantically, quote it, and cite exactly which file an answer came from.
Problem
- Your AI can't answer questions about your documents because it has never seen them.
- Pasting documents into chats is manual, hits context limits, and leaks nothing back to future sessions.
- Uploading to one AI product locks the knowledge inside that product.
- Building your own RAG stack means running vector databases and embedding pipelines.
- Many hosted options are unclear about training on your data or isolating it from other tenants.
Solution
Upload documents to your Context workspace at app.context.veeliq.com. Each file is split into chunks and embedded for semantic search. When your assistant needs information, it calls search_knowledge; VeelIQ returns the most relevant chunks with similarity scores and source metadata, and the assistant composes a grounded, cited answer.
The knowledge base lives behind your personal MCP URL, so it works identically in Claude, ChatGPT, Cursor, or any MCP client — one corpus, every tool. Isolation is per-user with row-level security, and your content is never used for model training. For evaluation criteria, see private knowledge bases for AI.
Benefits
- Specific answers with citations instead of confident guesses.
- Upload once, query from everywhere — no per-app re-uploading.
- No infrastructure: chunking, embeddings, and retrieval are managed.
- Sources are listable and deletable; the corpus stays under your control.
- Workspaces separate corpora that shouldn't mix (clients, teams, personal).
Features used
Knowledge upload and semantic search from Context, with workspaces for separation and memory for the facts that emerge from your reading.
Examples
- Researcher — 40 papers in a workspace; asks comparative questions and gets chunk-level citations.
- Owner's manuals — upload the PDF once, then ask "what does this dashboard light mean?" from any device's AI.
- Team handbook — policies uploaded once; every teammate's assistant quotes the same source of truth.
FAQ
How is this different from NotebookLM? # +
What can go into the knowledge base? # +
Is the knowledge base really private? # +
Will answers cite my documents? # +
How much can I store? # +
Can my team query the same knowledge base? # +
Related
Upload your first PDF free and ask your AI about it a minute later.