Every company wants useful AI. Few can afford the chaos that comes with it.

Cloud LLMs mutate daily. A prompt that worked yesterday breaks today. You don’t own the model, the weights, or the risk surface. Hallucinations are still running rampant through sensitive AI workflows.

Self-hosting sounds sovereign but turns into a maintenance treadmill: patches, GPUs, uptime, compliance audits. Data privacy remains a liability: the more you automate, the more you expose.

We built: Pyrinas MaaS (Model-as-a-Service) gives organizations their own private, fully serviced model stack.

Core mechanics:

We build, fine-tune, and deploy the model inside your boundary.

All inference runs on your hardware or our sealed unit (no data egress).

Continuous digital-twin testing keeps outputs within a 99% expected-result window.

Built-in data-labeling loop retrains and validates new information automatically.

Compliance layer emits auditable packets for HIPAA, GDPR, and FedRAMP alignment.

Predictable pricing: one flat model service fee; no tokens, no usage roulette.

In short: we turn AI from an experiment into infrastructure.

Yeah, yeah... what about it?:

Persistent problem -> MaaS outcome Model drift -> Deterministic inference validated on-prem Runaway API costs -> Flat cost, predictable ops budget Privacy exposure -> Encrypted, sealed, customer-keyed runtime Audit pressure -> Automated evidence packets Stagnant models -> Continuous labeling + incremental retrain

A 30-person company running on Pyrinas typically reclaims about 1,350 staff-hours per year and saves around $50k in variable API spend while meeting compliance without a full-time ML ops team.

Early-builder offer Sovereignty Suite: $25k list -> $15k Sovereignty Lock for teams that complete the 30-Day Sprint (data + workflow setup).

Technical trade-offs

12–32 core NPU configs; GPU optional.

Self-serve fine-tuning planned Q1 2026; handled via managed gateway today.

Determinism favors precision over open-ended creativity.

Zero telemetry by design; no usage analytics.

Open discussion

What part of your business would break if your model gave an inconsistent answer during an audit?

How much of your current AI stack would you rebuild if "predictable" and "private" were the default settings?

When you say you own your data, do you actually own the model that learned from it?

If every workflow was 99% repeatable, what new problems could your team finally trust AI to handle?

How close are you to regulatory exposure you can’t explain to a board or investor?

Whitepaper: https://pyrinas.co/the-convergence

We’ll be in the thread all day discussing architecture, validation methodology, and compliance design.

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