1. Executive Summary (Abstract)

The enterprise AI landscape is currently paralyzed by a Sovereignty Trilemma: the inability to balance Privacy, Latency, and Cost within public cloud ecosystems. While 78% of regulated LATAM enterprises require local AI deployments to meet compliance (CNBV/GDPR).

Zyrabit-SLM breaks this cycle. This paper details a Local-First architecture using optimized Small Language Models (<14B parameters) that achieve an 81% reduction in OPEX and a 9x improvement in P99 latency by moving inference from the cloud to the user’s controlled infrastructure.

2. The Infrastructure Gap

Public AI APIs pose three critical risks to the LATAM market:

3. The Zyrabit 3-Product Ecosystem

Our strategy is built on three distinct pillars to serve the market from community adoption to high-security enterprise nodes:

• Zyrabit SLM Protocol (Open Core):

  An MIT-licensed engine designed to run 1B-8B parameter models on consumer-grade hardware. It provides the foundation for local inference without internet dependency.

• Zyrabit Cortex (Enterprise):

  Our flagship closed-source orchestration layer. Built in Go 1.22+, it provides Zero-Trust data planes, air-gapped support, and automated sanitization of sensitive data (PII).

• Zyrabit Lab:

  A specialized R&D unit focused on high-impact sectors (Insurance, Forensics) to build domain-specific SLMs optimized for regional languages and regulations.

4. Technical Architecture: The Tiering System

We categorize hardware capabilities to match computational reality:

5. Implementation & Commercialization

Unlike SaaS competitors, Zyrabit avoids the "Subscription Trap."

6. Leadership & Vision