ScienceToStartup is an Agent Operating System for Research Commercialization
A daily commercialization score on every paper arXiv publishes in cs.AI - for founders, investors, and researchers deciding what to ship next.
Platform surfaces and supporting routes
API and MCP Platform for Turning Research Papers into Buildable Product Signals.
Stable evidence receipt, viability score, citations, and execution handoffs on one public example URL.
Citation-first answer surface that turns paper context into research-to-product judgment.
Durable research-area page with paper counts, trend direction, authors, and top questions.
Public prediction ledger with backtest calibration, frozen score decomposition, and reasoning chains for every paper.
Connect agents to paper discovery and proof retrieval without scraping the UI.
Preserve source context from proof surface to workspace creation and follow-on runs.
Use citation-first answers and source context as a direct input into execution.
Take weekly rankings into shortlist selection and launch-pack generation.
Install ScienceToStartup into ChatGPT developer flows with MCP and stable docs.
Use the remote MCP endpoint from Cursor for proof retrieval and workflow execution.
Connect Claude to the same proof surfaces and action flows through remote MCP.
Hosted agent interface for tools, resources, and workspace-native follow-on actions.
One platform, eight ways to use the same ranked corpus — built so each product answers a single question well.
Today's top AI papers, sorted by what people are actually building and citing.
Read a paper next to its citations, code, and benchmarks in one view.
Turn a paper's claims into a runnable spec you can hand to an engineer.
Screen papers, pull out the numbers, and keep an audit trail of every decision.
A persistent place for your notes, decisions, and project context across sessions.
Track sectors, labs, and companies through a single research queue.
Find builders by what they have shipped, not by the keywords on their resume.
See which AI repos are gaining stars, commits, and contributors day by day.
Updated every morning at 08:00 UTC. One paper out of everything cs.AI published yesterday.
The scores aren't a vibe. Every rank is auditable, every method is published.
Every viability score links to the passages, commits, and benchmark rows it cites. Audit any rank in under a minute.
See an audit trail →An open rubric across novelty, defensibility, market pull, and execution risk. No black box.
Read the rubric →Model card, prompts, and weighting are public. Pull requests welcome.
View methodology →ScienceToStartup ingests every new AI paper on arXiv, scores it for commercial viability, and surfaces the strongest candidates as fundable startup ideas. Founders, investors, and operators use it to spot research worth building before it trends elsewhere.
Signal Canvas for citation-first research synthesis; the Daily Dashboard for today's top papers; Build Loop for build/watch/verify decisions; Foresight for verifiable predictions; Talent for finding builders; Buildability for reproducibility receipts; Trends for narratives; and a public MCP server for agents.
Three audiences. Founders looking for technical wedges that have a chance of becoming a $1B company. Investors needing an early-warning system for emerging AI breakthroughs. Researchers exploring whether their own paper could ship as a product or oss tool.
arXiv ingestion runs daily at 20:05 UTC; enrichment + scoring at 20:30 UTC; publish at 21:00 UTC. New papers appear on the dashboard within a few hours of being posted to arXiv. GitHub star velocity refreshes daily. Topic narratives regenerate weekly.
A single quiet-canvas page at `/dashboard` showing today's highest-Signal-Fusion papers, the Today Rail of must-read items, recent prediction-market activity, GitHub velocity changes, and the action rail for what to do next. Refreshed every morning after ingestion completes.