Paperzilla monitors new papers and paper alerts, filters them against your research topic, ranks what matters, and delivers a structured feed to your inbox, app, RSS reader, CLI, MCP server, or AI research agent.
Create a research feed
Track papers from arXiv, bioRxiv, medRxiv, ChemRxiv, forwarded Google Scholar alerts, and other paper alert emails.
System overview
Messy paper streams in. Structured research feeds out.
Inputs
Relevance Engine
Outputs
Inputs
Preprints, alerts, newsletters, custom sources
arXiv
preprint
bioRxiv
preprint
medRxiv
preprint
ChemRxiv
preprint
Scholar Alerts
email
Journal Alerts
email
Custom Sources
custom
Paperzilla
Relevance Engine
Topic filter
Scoped to your research interest
Relevance ranking
Strongest matches rise first
Feedback loop
Learns what matters over time
improves over time
Outputs
Feeds for people, tools, and agents
Feed
App Feed
human
Mail
Email Digest
human
RSS
RSS / Atom
human
MCP
MCP Server
agent
>
CLI Access
agent
1
Connect sources
2
Tune relevance
3
Get focused feeds
The problem
Research updates are scattered everywhere
New papers do not arrive in one clean stream. They appear across preprint servers, journal alerts, Google Scholar alerts, newsletters, lab updates, and topic-specific services. Some papers are essential. Most are noise.
The more specific your research interest, the harder it becomes to maintain a feed that is broad enough to discover surprises but focused enough to be useful.
You shouldn't check 5+ sources every day
Scattered across arXiv, Scholar, newsletters, journals
Keyword alerts miss the point
Too broad or too narrow, never just right
Manual curation doesn't scale
Fifty abstracts a week is not a research workflow
Agents need scoped context
Broad web search is noisy input for research tasks
How it works
One pipeline for continuous research monitoring
01
Input
Bring in the paper streams
Paperzilla harvests papers from arXiv, bioRxiv, medRxiv, and ChemRxiv. Forward Google Scholar alerts or other paper emails directly into Paperzilla, and existing alerts join the same relevance workflow.
02
Relevance
Filter and rank by relevance
Each project defines what matters for a specific research topic. As new papers arrive, Paperzilla filters and ranks them against that topic. The relevance engine improves over time using recommender-style feedback.
03
Output
Deliver structured output
The result is a focused research feed delivered how you work: daily or weekly email digests, in-app feed, RSS/Atom, MCP server access, and CLI for agent workflows.
Built for everyone
Research feeds your team can read and your agents can use
Paperzilla is designed for both human review and automated research workflows. One filtered feed, consumed multiple ways.
R
For researchers & teams
Humans get less noise
✓ Read a focused paper feed in the app
✓ Receive daily or weekly email digests
✓ Subscribe by RSS / Atom
✓ Stop manually checking scattered sources
AI
For AI agents & automation
Agents get better context
✓ Access topic-specific feeds through MCP
✓ Use CLI workflows for research automation
✓ Build recurring literature briefings
✓ Give agents a current, filtered paper stream
Use cases
What people build with Paperzilla
Features
What Paperzilla gives you
◎
Paper source intake
Monitors arXiv, bioRxiv, medRxiv, ChemRxiv, with additional sources on request.
✉
Forwarded alert intake
Forward Google Scholar or other alert emails directly into Paperzilla.
⊙
Topic-based filtering
Create a project around a research interest. Papers are filtered against it continuously.
↑
Relevance ranking
Strongest matches rise above adjacent or low-signal results automatically.
↻
Feedback-driven learning
The feed improves over time using recommender-style feedback on what matters.
▤
Human-readable delivery
In-app feed, daily or weekly email digests, or RSS/Atom subscription.
⌘
Agent-ready delivery
Expose the filtered feed through MCP server access and CLI for automated workflows.