PAPERZILLA
High-signal always up-to-date research data feeds
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About Paperzilla

Built to keep research current

Paperzilla exists because the important research update you need to know about rarely arrives neatly packaged. It may appear in a preprint feed, a dataset archive, a software repository, a patent database, a Google Scholar alert, a journal email, a newsletter, or a source you forgot to check.
Paperzilla turns that scattered stream into focused research data feeds: it harvests new papers and alerts, connects them with paper-related datasets, software repos, and patents, filters and ranks everything for your specific research topics, and delivers the results in formats that humans and AI agents can actually use.
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Why Paperzilla exists

Keeping up with research is a harder problem than it used to be

Keeping up with research used to mean checking a few journals, alerts, and conferences. Now it means monitoring preprint servers, dataset archives, software repositories, patent databases, search alerts, source-specific emails, newsletters, and fast-moving fields where the framing can change in weeks.
The problem is no longer just discovery. The problem is staying current with confidence.
Researchers should spend less time monitoring sources and more time understanding what changed.
Instead of sending more alerts
Paperzilla filters harder
Instead of a generic feed
Paperzilla tunes to your research context
Instead of human-only summaries
Output is available to tools, scripts, and agents
What Paperzilla is building

The relevance layer between research sources and the workflows that matter

Paperzilla is the relevance layer between the world's research sources and the workflows where research decisions happen.
Harvesting papers from arXiv, bioRxiv, medRxiv, and ChemRxiv
Paper-related datasets, software repos, and patents
Accepting forwarded research-alert emails, including Google Scholar-style alerts
Filtering and ranking incoming papers and related research data against your topics
Improving relevance over time through feedback and recommender-style signals
Delivering focused output through the app, email digests, RSS/Atom feeds, CLI access, MCP server access, and API access
The goal is not to make another place where papers and records pile up. The goal is to turn the research-data firehose into current, structured research context that can move with you.
Delivered to
App
App feed
Read in-product
human
Mail
Email digest
Daily or weekly
human
RSS
RSS / Atom
Any feed reader
human
MCP
MCP server
AI agent access
agent
CLI
CLI access
Script and automate
agent
API
API access
Custom integrations
agent
Product principles

What guides the product

01
Signal over volume
The best research feed is not the one with the most results; it is the one that reliably surfaces what you would have regretted missing.
02
Grounded in sources
Paperzilla points back to the original papers and source records, and treats summaries as a starting point, not a substitute for reading the source.
03
Tuned to your context
A relevant paper, dataset, repository, or patent for one researcher may be noise for another. Paperzilla is built around specific research topics, not generic categories.
04
Useful for humans and agents
The same filtered stream should work for a researcher skimming a digest and for an AI agent preparing a briefing.
05
Portable by default
Research context should not be trapped in one interface. Paperzilla supports multiple outputs so users can consume the feed where it is most useful.
Who it's for

People who cannot afford to be surprised by the research stream

The common thread is the same: too many papers, datasets, repositories, patents, and alerts appear across too many places, and missing the right update at the wrong time is expensive.
Researchers
Track narrow topics without living in alerts. Follow specific methods, disease areas, model families, or benchmark evaluations.
PIs & lab heads
Keep projects, proposals, and students aligned with new work without spending hours curating feeds manually.
R&D teams
Spot papers, datasets, software releases, and patents that may change a roadmap or prevent duplicated effort.
AI agent builders
Use Paperzilla as a focused, current input stream for research agents, better than broad web search for research-data tasks.
Built close to the problem

A founder-led product for workflows that are becoming more automated

Paperzilla is a founder-led product built for research workflows that are becoming more continuous, more automated, and more agent-assisted.
The product started from a practical frustration: alert systems often create more work than they remove. They tell you that updates exist, but they do not reliably tell you which papers or records matter for your exact context, why they matched, or how to feed that information into the tools you already use.
Paperzilla is built to close that gap.
Contact

Get in touch

General
Questions, partnerships, feedback, or support
hello@paperzilla.ai
Founder
Contact the founder directly
mark@paperzilla.ai
Press

Press information

Short boilerplate
Paperzilla helps researchers, R&D teams, and AI agents stay current with relevant research data by turning scattered papers, datasets, repositories, patents, and alerts into focused, high-signal research feeds.
Press contact
mark@paperzilla.ai
Longer boilerplate
Paperzilla is a research monitoring platform that helps users keep up with fast-moving research without living in alerts. It harvests new papers from preprint sources and forwarded research-alert emails, connects them with paper-related datasets, software repos, and patents, filters and ranks incoming records against specific research topics, and delivers the results through the app, email digests, RSS/Atom feeds, CLI access, MCP server access, and API access for AI research agents.
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Want to understand the product?

Explore the product page to see how Paperzilla turns scattered research sources into focused feeds for humans and AI agents.
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