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Portfolio risk and scenario analytics for crypto holders. With REST and MCP API, and innovative Risk Workbench for deep investigations.

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Website, pricing, platforms and company facts side by side.
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| Website | sentralis.io | open-gpt.app |
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| Company | Startup from Switzerland · 1 - 9 employees · 2026 | — |
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In their own words, as submitted to SaaSHub.


Sentralis is portfolio intelligence for crypto holders: the analytical depth of a professional risk desk, applied to your own positions. See your portfolio the way a risk professional does. Nine scenario engines quantify how your holdings behave under any market condition you choose: Instant...
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What each product offers, as listed by its team.


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An editorial look at what each product does well and who it suits.


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As answered by people managing Sentralis and https://open-gpt.app/.
Sentralis's answer
Sentralis's uniqueness comes from the combination of institutional-grade risk and scenario analytics applied to the user's own crypto portfolio, with AI agents on top that turn the numbers into insight, and a workflow that serves a first-time holder and a professional equally well.
Sentralis's answer
Self-directed crypto holders who want to understand their portfolio with the rigour a professional desk would apply, without institutional tooling. Around that core: semi-professional investors managing meaningful positions, contributors and analysts working on DAO or project treasuries, and developers building agents or workflows on the API and MCP server.
Sentralis's answer
Portfolio trackers show what a user holds and how it has performed. On-chain analytics platforms describe the market as a whole. Sentralis answers the question in between: how the user's specific portfolio behaves under conditions the user choose, and which positions drive that behaviour. It replays more than fifteen real market episodes against the user's own book, quantifies exchange and custody exposure, and keeps watching through scheduled reports and alerts. It is non-custodial and read-only throughout, and hypothetical portfolios can be analysed without connecting anything.
Sentralis's answer
React 19, TypeScript and Vite on the front end. Node.js 20 with Express on the back end, split into an API process and a BullMQ worker on Redis. PostgreSQL with TimescaleDB for time-series market data. A dependency-free TypeScript analytical core shared by browser and server. Python with numpy, pandas and scikit-learn for covariance preparation. The MCP server is built on the official Model Context Protocol SDK.
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