Cutonce.ai is an AI-powered research automation platform that lets investors build visual research pipelines for stock screening and data extraction. Users chain filter, score, and AI nodes on a no-code canvas to encode their investment thesis and schedule recurring runs across SEC filings, earnings transcripts, and fundamentals. The platform outputs ranked shortlists to Google Sheets, Slack, or webhooks, with every step inspectable and reproducible.
Funding
Funding not disclosed
Founders
Product
Problem
Investors tracking large universes of stocks face a time-consuming problem: key data—like segment growth or dividend safety metrics—is scattered across unstructured earnings transcripts and filings, buried in different sections and phrased inconsistently across companies. Manually extracting this information for even a few hundred names takes days, and naive keyword searches fail to handle the variation in how companies report the same metric.
Solution
Cutonce.ai provides a visual, no-code research platform that lets investors build automated pipelines to screen and extract data from SEC filings, earnings call transcripts, and fundamentals. Users chain together filter, score, AI, and compare nodes on an interactive canvas, defining explicit logic for how a thesis turns into a ranked shortlist. The platform runs these pipelines on demand or on a scheduled basis, delivering results to Google Sheets, Drive, email, Slack, or a webhook. Each run is logged and reproducible, and extracted fields carry source quotes for auditability, ensuring outputs are transparent and comparable across an entire universe.
Target Audience
Primary users are equity researchers, fundamental analysts, and quantitative investors who need systematic, repeatable screening and data extraction across large stock universes to surface opportunities before they are priced in.
Features
- Visual canvas interface for chaining filter, score, AI, and compare nodes without writing code
- Pre-built universe presets (S&P 500, NASDAQ 100, sector indices) or custom ticker lists with built-in SEC filings, earnings transcripts, and fundamentals data
- AI extraction nodes that convert unstructured transcripts into structured schema with every field linked to its source quote
- Validation and normalization nodes that check units, flag missing fields, and capture reporting basis (e.g., YoY vs. QoQ, FFO vs. AFFO) for comparability
- Scheduled runs with output to Google Sheets, Drive, email, Slack, or webhooks, with full run logging for reproducibility
- Specialized templates and build logs for use cases like REIT AFFO dividend-safety screening and earnings-call segment-growth extraction