Private Research Intelligence
Find the evidence in
your private research.
Search files, notes, and conversations with scoped local retrieval, then create grounded answers without sending your workspace to Korvo. Direct reference-manager ingestion and exact evidence links are planned, not yet released.
No account needed — the download starts right away.
Pro is $19/month or $149/year. Planned public price: $299/year or $29/month. Access provided within 7 days.
The Problem
Your reference manager stores papers.
It can't think across them.
You've collected hundreds of papers over months. You know the answers are in there. But when it's time to synthesize - write a literature review, compare arguments, find gaps - you hit a wall.
AI loses your metadata
Dump PDFs into ChatGPT and every citation becomes "according to the document." Authors, year, journal - all gone.
Reference managers are for storage, not synthesis
Zotero, Mendeley, EndNote - excellent at organizing. Useless at answering "what does my library say about X?"
Cloud AI requires uploading sensitive research
Unpublished manuscripts, proprietary datasets, embargoed findings - sent to servers you don't control.
No traceability from synthesis to source
AI gives you a paragraph. You can't click through to the exact passage in the exact paper. The reasoning chain is lost.
How It Works
Five steps. Private sources to inspectable synthesis.
Collect
Add permitted papers, reports, notes, and source files to a private Korvo project.
Index
Korvo stores complete searchable text locally and adds compatible semantic embeddings when a configured provider is available.
Retrieve
Hybrid retrieval searches the permitted project scope and reports keyword-only or other degraded states honestly.
Synthesize
Ask questions across project evidence and create grounded outputs while keeping provider use explicit and user-controlled.
Verify
Inspect selected sources and uncertainty. Exact evidence-span links and metadata-aware academic citations remain quality-gated roadmap work.
Before & After
What changes when your citations
are metadata-aware.
Before
Dump 200 PDFs into ChatGPT → AI says "according to the document"
After
AI says "(Smith & Doe, 2024)" with full bibliographic metadata
Before
Upload sensitive unpublished research to cloud servers
After
Workspace corpus stays local. Cloud prompts use your BYOK provider — or Ollama offline.
Before
Manually search papers one at a time in your reference manager
After
"What does my library say about X?" → cited synthesis in 30 seconds
Before
Lose the reasoning trail behind your literature review
After
Decision Journal traces every synthesis back to its source papers
Citation Quality
Real citations, not filename references.
AI Response - Literature Synthesis
The literature suggests that AI tools significantly affect decision-making processes (Smith & Doe, 2024), particularly in contexts where information overload is a factor (Johnson, 2023). However, some researchers argue that the effect is moderated by domain expertise (Lee et al., 2024).
References (clickable → source passage)
1. Smith, J. & Doe, J. (2024). The Impact of AI on Decision Making. Journal of Artificial Intelligence, 42, 123–145.
2. Johnson, R. (2023). Information Overload in the Age of AI. Cognitive Science Review, 18(3), 45–67.
3. Lee, S., Park, H., & Kim, J. (2024). Domain Expertise as a Moderator… Proceedings of CHI 2024, 892–901.
Illustrative target experience. Exact source-location links remain a release gate, not a current promise.
Built For
People who think across
hundreds of sources.
"I've read 200 papers and can't synthesize them."
Bring papers into a private project today. Metadata-aware library import and exact (Author, Year) evidence links are planned after retrieval and grounding benchmarks pass.
"Screening 2,000 abstracts manually takes weeks."
Use project-scoped retrieval today. A review-first academic ingestion queue is planned; unattended screening will remain gated on provenance and safety benchmarks.
"I have 3 years of industry reports and no way to query them."
Add reports to a project and ask cross-document questions with scoped local retrieval. BibTeX metadata reconciliation remains on the academic roadmap.
"My thesis needs a literature review across 150 sources."
Organize source PDFs in a private project and create grounded syntheses. Direct Zotero, Mendeley, RIS, and BibTeX workflows are planned, not yet released.
Compatible With
Designed to work alongside major reference managers.
Planned RIS/BibTeX reconciliation will use exports from tools such as these after the academic-ingestion entry gates pass.
Zotero
10M+ · Free, open source
Mendeley
6M+ · Elsevier-owned
EndNote
Enterprise · ~$250/year
Papers
macOS · ReadCube
Paperpile
Web-based · Google Docs integration
JabRef
Open source · BibTeX-native
Keep using Zotero, Mendeley, or EndNote for library organization. Today, add source files to Korvo projects manually; direct metadata-aware import is roadmap work.
Privacy
Your unpublished research stays in a local-first workspace.
Local RAG indexing
Papers are chunked, embedded, and stored locally. No cloud vector databases. No uploads of your corpus to Korvo servers.
Bring your own keys
Use your own OpenAI, Anthropic, Google Gemini, Moonshot (Kimi), or OpenRouter API keys. Prompts and selected context go directly to that provider under your agreement — or stay fully local with Ollama.
No training on your data
Your papers, notes, and synthesis outputs are never used to train any model. Your research stays yours.
Full data control
Delete any paper, library, or project at any time. Export everything. No lock-in, no residual copies.
Stop re-reading papers.
Start synthesizing them.
Import your library. Ask hard questions. Get cited answers. Local-first workspace, traceable outputs, BYOK or fully offline.
No account needed — the download starts right away.