Regulated firms face an impossible choice: ship AI and fail audits, or don't ship and fall behind. Deino resolves it — every forecast carries an audit manifest, and the invariants live in the Rust workspace, not a toggle.
The infrastructure for regulated AI is different from the infrastructure for ordinary AI. It has to be defensible — not at demo time, but six months later, when a regulator asks why the model decided what it decided.
First Principles: What a Regulated Forecast Needs
Strip it down: a regulated forecast needs the forecast, the lineage (what model, what data, what version), and the audit manifest (the defensible record of the decision). Without all three, the forecast is a liability; with all three, it's an asset.
- The forecast itself.
- The lineage — model, data, version.
- The audit manifest attached to every output.
The Design Decision: Invariants in the Workspace
The four invariants Deino refuses to relax live in the Rust workspace, not in a feature toggle. That's the compliance argument: a toggle can be flipped; a workspace invariant can't. The load-bearing decisions are encoded where they can't be quietly relaxed.
One Workspace, Many Products
Every Deino product sits on the same 19-crate Rust workspace and versioned Geo-Neural Layer — switch tiers without migrating data. The substrate is the asset; the products are surfaces on it. Buying Deino is buying the substrate, not a single tool.
Who It's For
From the CTO picking infrastructure to the compliance officer who defends it — one platform, one ontology. Free → Pro → Enterprise (unlimited capacity, Custom Axes, sovereign deployment, dedicated CSM). The platform is the unit of value, not the seat. BYO LLM keys; built deep, not fast.
Conclusion
Deino is forecasting infrastructure for firms that have to defend every AI decision. The audit manifest on every output and the invariants in the Rust workspace are what make a regulated forecast defensible — not the forecast itself.
About FACTA
FACTA helps startups and growth-stage teams turn AI into production systems that keep running — not demos that impress once.
We design the architecture around the parts that actually break under real usage: tooling you own, credentials you control, failover, cost controls, observability. The boring infrastructure that keeps a system alive after launch.
Led by Matías Baglieri and Carolina Fogliato, we focus on one thing:
AI leadership that builds. Not just advises.
See what a defensible forecast looks like at the Deino product page.
Related: audit manifests on every output and model lineage.
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