Paradocs is the local-first AI workspace for research teams.
Paradocs unifies documents, datasets, notebooks, citations and code in one secure workspace. AI runs locally by default, grounded in your team’s knowledge graph.
manuscript.docx
Results
The intervention improved sample stability.
SUGGESTED REVISION
After predefined exclusions, results suggest improved stability and warrant further validation.
Review revision ->Mapping Knowledge
Turn scattered files, notes, and data into a living semantic map your AI can reason over.
Surgically Precise Retrieval
Grounds AI reasoning in your structured knowledge base. Trace every answer back to the exact source, relationship, and context it came from.
Dense Context Compression
Preserve meaning across millions of data points without flooding the context window.
Uncompromising
Data Sovereignty.
Use local-by-default AI models where your data already lives. Nothing sensitive gets sent to public AI tools.
Self-Hosted Enterprise
Deploy the entire Paradocs stack on your internal servers or air-gapped environment. Total isolation.
Bring Your Own Key (BYOK)
Connect your own API keys. Route all traffic through your own endpoints.
Safe AI & Reasoning
Integrations with verified partners providing strict, auditable non-logging guarantees. No model training.
Public AI providers
ChatGPT, Claude, Gemini, public cloud APIs
Why Paradocs
Built for research teams that cannot afford to lose context.
Research work is rarely contained in one clean document. A single project can span PDFs, spreadsheets, Jupyter notebooks, scripts, citations, drafts, figures, meeting notes, and shared folders. Paradocs turns those materials into a connected workspace so teams can move from reading to analysis to writing without breaking the chain of evidence.
The product is designed for labs, R&D groups, computational scientists, medical researchers, and data-intensive teams that need AI help without uploading sensitive or unpublished work to public tools by default. Documents, data, code, citations, and notes can remain close to the user or institution while the knowledge graph preserves how claims, files, and results relate to each other.
That connected memory makes Paradocs useful for literature reviews, reproducible analysis, manuscript drafting, institutional knowledge, and long-running projects where new collaborators need to understand what was done, what supports each conclusion, and what changed over time.
Field Notes
Frequently Asked Questions
Need details on privacy, file support, deployment, or beta access?
Full FAQClosed beta
Unify your research.
Keep your data local.
We are opening Paradocs to researchers and teams who need AI across real work without surrendering control of their data.