API and MCP Platform for Turning Research Papers into Buildable Product Signals.
Turn papers, topics, benchmarks, datasets, and Signal Canvas threads into buildable product signals for your agents and operator workflows.
Owned Distribution
Weekly benchmark movers, commercializable papers, proof surfaces, and installable workflows for developers and operators.
Trust and Proof
Papers, topics, benchmark snapshots, datasets, and Signal Canvas surfaces are the acquisition wedge. They earn trust first, then route people and agents into the programmable system.
Example paper page
Stable evidence receipt, viability score, citations, and execution handoffs on one public example URL.
Signal Canvas
Citation-first answer surface that turns paper context into research-to-product judgment.
Topic proof layer
Durable research-area page with paper counts, trend direction, authors, and top questions.
Benchmark scoreboard
Weekly ranking surface for high-signal papers and ranked commercialization comparisons.
Developer Workflows
Build on stable paper, topic, benchmark, and Signal Canvas routes with explicit REST and MCP contracts instead of trying to infer the product from the dashboard alone.
Research paper MCP server
Connect agents to paper discovery and proof retrieval without scraping the UI.
Paper to workspace automation
Preserve source context from proof surface to workspace creation and follow-on runs.
Signal Canvas API
Use citation-first answers and source context as a direct input into execution.
Benchmark to launch pack
Take weekly rankings into shortlist selection and launch-pack generation.
Install and Integrate
Cursor, Claude, OpenAI, llms files, OpenAPI, and the remote MCP endpoint all point into the same acquisition story and proof inventory.
OpenAI / ChatGPT
Install ScienceToStartup into ChatGPT developer flows with MCP and stable docs.
Cursor
Use the remote MCP endpoint from Cursor for proof retrieval and workflow execution.
Claude
Connect Claude to the same proof surfaces and action flows through remote MCP.
Remote MCP server
Hosted agent interface for tools, resources, and workspace-native follow-on actions.
Freshness Truth
Proof and developer surfaces stay available, but anonymous dashboard metrics are hidden until the next canonical snapshot lands.
Snapshot stale
The latest landed snapshot is older than the freshness target. Treat rankings as stale until the next batch lands.
Freshness
Canonical route: /
The latest landed snapshot is older than the freshness target. Treat rankings as stale until the next batch lands.
Product
Daily Dashboard, Signal Canvas, Build Loop, Evidence, and operator tools
Proof Layer
Canonical proof surfaces for papers, topics, rankings, and datasets
Developers
Agent OS overview, guides, examples, and install paths
Trends
Live desk, entities, narratives, topics, and methodology
Resources
Benchmarks, datasets, glossary, database, and reference assets
Company
About, articles, changelog, careers, legal, and contact
Daily Dashboard
Canonical operator view with the map, previews, velocity, and predictions
Signal Canvas
Citation-first synthesis and handoffs from proof into action
ScienceToStartup is an AI-powered research intelligence platform that discovers which AI research papers could become the next breakthrough startup. We analyze papers from arXiv daily and rank them by commercial viability using our proprietary Signal Fusion algorithm.
We use our Signal Fusion algorithm that combines four signals: a GPT-4o viability score (1–10), community unicorn probability predictions, GitHub star velocity, and citation momentum. The composite score surfaces the papers with the highest commercial startup potential.
Yes. The core dashboard, paper analysis, topic pages, and research trends are completely free. We offer enterprise features like TTO dashboards, scout reports, and API access for institutional users.
Papers are ingested daily from arXiv. Viability scores are computed on ingestion. GitHub stars and citation counts update daily. Topic summaries regenerate weekly. Articles are published daily based on news analysis.
The Viability Score (1–10) measures how likely an AI paper is to become a fundable startup, based on code availability, author commercialization track record, market timing, and competitive landscape.