Case Study
ShinobiData - Bloomberg grade equity research platform
- Client
- ShinobiData
- Role
- Founding GTM
- Markets
- Singapore
- Industry
- Fintech / Equity Research
- Timeline
- 2025 – 2026
ShinobiDataHighlights
- 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.
Project Brief
ShinobiData is a modern financial data and analytics platform built to deliver stealth-level insight into the US stock market.
About the Company
Context before the build.
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
What Hahlex shipped.
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.
Live
Project status
8
Workstreams
7
Core technologies
Challenges and Outcomes
The work behind the result.
Challenges
- Selling a Bloomberg-grade product to people who have never paid for oneInverted 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 messageTreated 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 teamsUsed 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
Systems and tools used.
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