Inspiration
Traditional financial tools impose an frustrating compromise: investors can either conduct deep-dive research on a tiny handful of stocks or run incredibly shallow screens across the broader market. When boutique RIA analysts and independent investors want to build deep, institutional-grade research pipelines, they usually face massive engineering hurdles, complex API architectures, and data fragmentation. We wanted to build a tool that removes this infrastructure overhead entirely, empowering non-technical analysts to build complex financial data data workflows instantly.
What it does
Cutonce lets users construct a comprehensive, automated equity research pipeline without writing any code. Analysts can systematically connect their targeted universe of stocks, build chain filters, define scoring metrics, and use specialized AI nodes to encode their entire investment thesis.
In a single automated pass, the platform processes deep datasets—including SEC EDGAR filings, 10-K financial health checks, insider trading indicators (Form 4), earnings call transcripts, analyst consensus, and FRED macroeconomic data. The workflow finishes by delivering a completely ranked, scored shortlist of investment actionable items straight to a Slack workspace, Google Sheets, or an inbox.
How we built it
We built the core engine to cleanly ingest and normalize massive, disparate data streams from financial institutions, government portals, and transcription services. We built a visual node-based framework that handles logical filtering, conditional chains, and customizable AI evaluation blocks. The backend efficiently manages data orchestration, running parallel automated passes across hundreds of tickers at once, and maps the aggregated, scored output to popular integration endpoints like Google Sheets and Slack APIs.
Challenges we ran into
One of our biggest hurdles was tackling data ingestion latency and text-parsing accuracy, particularly with dense 10-K financial statements and real-time earnings call transcripts. Building reliable AI nodes that can accurately extract executive sentiment or flag subtle insider trading signals without producing massive false positives required extensive prompt refining and deep structural optimizations within our data parsing pipelines.
Accomplishments that we're proud of
We successfully compressed complex data engineering tasks that typically take an entire research team weeks to execute down to just a couple of minutes. Seeing boutique analysts effortlessly track granular metrics like earnings call quality and custom fundamentals screening parameters concurrently—all without an engineering background—is a huge win for us.
What we learned
We learned that accessibility to clean data is only half the battle. The real value for investment professionals lies in custom execution logic. Empowering users to build custom, thesis-driven scoring models through a simple, non-technical visual workflow is far more valuable than giving them another static financial dashboard.
What's next for Cutonce
We are continually expanding our integrated data networks to include alternative data streams, deeper global market tracking, and more advanced predictive AI analytics models. Our ultimate goal is to become the default data pipeline infrastructure for independent financial analysts worldwide. Check out our platform at https://www.cutonce.ai/
