Now with multi-projection architecture

Living knowledge from your documents

DeepContext builds an evolving, compressed understanding of your entire document corpus. Upload anything — PDFs, code, research, policies — and chat with a grounded AI that actually knows your data.

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quickstart.py

import deepcontext

# Create a workspace and upload documents
ws = deepcontext.Workspace.create("research-papers")
ws.upload("attention-is-all-you-need.pdf")
ws.upload("scaling-laws-2024.pdf")

# Chat with your documents
answer = ws.query(
    "How do scaling laws affect transformer architecture choices?"
)

print(answer.text)       # Grounded, cited response
print(answer.sources)    # Document chunks used
print(answer.confidence) # 0.94

Built for serious knowledge work

DeepContext isn't just RAG. It's a living knowledge system that evolves as your documents grow.

Narrative Subsumption

Each document is woven into a living corpus — not just chunked and embedded. New information is merged, redundancies removed, contradictions flagged.

Multi-Projection Architecture

Your data exists simultaneously as compressed summaries, narrative corpora, and raw embeddings. Each projection serves different retrieval needs.

Hybrid Retrieval

Combines semantic embedding search, filesystem grep (via sandboxed VMs), and projection-aware routing. Gets what embeddings miss.

Semantic Memoization

Similar queries hit a cache of previous retrieval sessions. Repeat and near-duplicate questions are served instantly.

Sandboxed Execution

Each workspace runs in an isolated Firecracker microVM. Ripgrep, LSP, and file operations execute safely inside the sandbox.

Cost-Aware Recipes

Retrieval recipes adapt to your budget. Quick lookups stay cheap. Deep research uses more compute. You control the tradeoff.

Ready to build living knowledge?

Start free. Upload your first documents. See how DeepContext transforms how you work with information.

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