Normalization as Artemis platform moat

This cost base matters because Artemis is not selling five different products, it is selling the same cleaned dataset through five different interfaces. The expensive work is paying vendors, ingesting raw feeds, and deciding what counts as the same metric across chains and assets. Once that pipeline exists, Terminal serves visual exploration, Sheets serves spreadsheet users, API serves product builders, Snowflake serves data teams, and Analyst serves users who want answers instead of raw tables.

• The real fixed asset is normalization. Artemis describes its job as creating a GAAP like standard for crypto, because blockchains define users, fees, and activity differently. That mapping work can be reused everywhere, which means each new surface raises revenue potential without recreating the data layer.
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• The Snowflake product shows the leverage most clearly. Artemis says some datasets are too large for a REST API and are delivered by private Snowflake share instead. That lets one backend support both lightweight spreadsheet pulls and heavy institutional SQL workflows, widening spend from analysts to full data teams.
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• This is also how Artemis competes with tools like Dune and Token Terminal. Dune is strong for open ended querying and datashare, while Token Terminal stays narrower on crypto fundamentals. Artemis is trying to own the standardized metrics layer, then package it for different jobs across research, dashboards, and machine readable delivery.
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The next step is turning the dataset from a research tool into core investment plumbing. As more customers plug Artemis into spreadsheets, warehouses, and agent workflows at the same time, the company can spread data costs over larger contracts and become harder to replace, because swapping vendors would break multiple parts of the same investment process.