Engines, not wrappers.
Go on servers, Rust at the edge, TypeScript for interfaces, Python for training. The engine is the product; the application is proof it works.
Why we exist
The runtime was the missing layer.
Enterprise AI spent a decade wrapping models in frameworks. The wrappers made demos fast and production expensive: extra runtimes, fragile dependencies, audit trails reconstructed after the fact.
Dintellinno takes the opposite position. Own the execution substrate, compile the agent, and make performance, isolation and evidence properties of the engine itself. Then use that engine as the backend for every application we deliver.
Evidence by construction.
The ledger is written by execution. Compliance artefacts exist because the agent ran, not because a project was funded afterwards.
Europe as the design target.
GDPR and the EU AI Act shape the runtime contract, the evidence map and the deployment model — not the marketing page.