ShinobiData - Bloomberg grade equity research platform
ShinobiData is a modern financial data and analytics platform built to deliver stealth-level insight into the US stock market.
Project Snapshot
Highlights
Positioned a Bloomberg-grade equity research platform against a $499/month incumbent, owning the zero-to-first-paying-cohort motion alongside the founder.
Translated a deep engineering surface (10k+ tickers, 200+ screener fields, sub-50ms filters, MCP server) into a narrative buyers could grasp in 30 seconds.
Designed the anti-Koyfin price anchor so the price read as a discount on Bloomberg rather than a premium over free tools.
Built the channel mix that took the product from zero audience to its first paying cohort without paid acquisition.
Ran a dual-ICP motion where one side pays for the product and the other amplifies it.
About the Company
ShinobiData is a Bloomberg-grade equity research platform — a technically dense product with 10k+ tickers, 200+ screener fields, sub-50ms filters, and an MCP server — that needed a non-technical wedge to reach its audience.
Scope of Work
Positioning and narrative
Defined the wedge against Bloomberg/Koyfin and wrote the one-line value prop that anchored every downstream surface.
ICP segmentation
Split the audience into two non-overlapping ICPs (retail investors + AI-agent developers) with separate narratives and conversion paths.
Pricing and packaging
Anchored pricing against the $499/month incumbent so the product read as a discount on Bloomberg, not a premium over free tools.
Launch sequencing
Architected the multi-stage launch: warm-up content → HN → Product Hunt → Claude & OpenAI Apps directories.
Distribution and channels
Built the channel mix across HN, Product Hunt, finance Twitter/X, Reddit, and AI-dev communities with clean attribution.
MCP ecosystem partnerships
Turned Claude & OpenAI Apps directory listings and agent-builder partnerships into a referral loop.
Early-user pipeline
Sourced the first paying cohort by hand via direct outbound; converted early users into testimonials and referrals.
Analytics and instrumentation
Set up end-to-end funnel measurement from day one so every channel test and pricing decision ran on real data.
The Challenges
Selling a Bloomberg-grade product to people who have never paid for one
Inverted the pricing narrative, anchoring downward from the $499/month incumbents rather than upward from free; a generous unauthenticated surface let the product's depth do the selling before the paywall appeared.
Reaching two ICPs without diluting either message
Treated MCP as a separate product surface with its own narrative, channels, and documentation. Same backend, two front doors.
Building distribution with no paid budget against incumbents with sales teams
Used the MCP server as a Trojan horse; earned channels compounded while the incumbents' paid channels didn't.
Outcomes
Owned the zero-to-first-paying-cohort motion alongside the founder, positioning a Bloomberg-grade equity research platform against a $499/month incumbent.
Tech Stack
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