What is data sovereignty?
The first large language model built natively for Azerbaijani, deployed entirely on your own infrastructure. Available in 587B, 99B and 39B sizes.
What Is Data Sovereignty?
Data sovereignty is the principle that data remains subject to the laws, governance structures, and control mechanisms of the entity — or nation — where it originates and resides. In practical terms, it means an organization retains full legal and physical ownership of its data, deciding where it is stored, who can access it, and how it is processed. This principle has grown increasingly important as organizations adopt digital systems that span multiple jurisdictions, because the moment data crosses a network boundary it may become subject to foreign regulations, third-party terms of service, or unforeseen legal claims that the originating organization never consented to.
Why Data Sovereignty Matters for AI Deployments
Full legal ownership — your data remains under your jurisdiction and internal governance policies at all times, with no ambiguity about which entity holds rights over stored inputs or generated outputs.
Zero external exposure — fully on-premise deployment ensures that sensitive queries, intermediate computations, and model responses never leave your network boundary under any operating condition.
Regulatory confidence — aligning AI operations with national and industry data-protection requirements becomes straightforward when all processing occurs within your own infrastructure, removing dependence on external compliance promises.
Complete auditability — every inference request stays within your own logging and monitoring systems, giving compliance and security teams a single, authoritative record that is never fragmented across external providers.
Reduced third-party risk — eliminating cloud intermediaries removes an entire class of data-breach vectors, contractual lock-in risks, and service-discontinuation scenarios that are outside your organization's control.
Operational continuity — your AI capability remains fully available even when external connectivity is restricted, degraded, or deliberately blocked, ensuring uninterrupted service regardless of network conditions.
How Allmaz Delivers Data Sovereignty for Azerbaijani AI
Fully On-Premise Deployment
The model runs entirely within your own infrastructure. Data never leaves your network, giving your organization unambiguous physical and legal control over every query and response.
First Native Azerbaijani LLM
Built from the ground up for the Azerbaijani language — not adapted from a generic multilingual base — the model was trained on over 651 million curated Azerbaijani words, ensuring authentic linguistic understanding.
Purpose-Built Native Tokenizer
A dedicated tokenizer handles the ə character and the agglutinative morphology of Azerbaijani, delivering 4.6× greater efficiency on Azerbaijani text compared to tokenizers designed for other languages.
Flexible Parameter Sizes
Available in 587B, 99B, and 39B parameter configurations, allowing organizations to match model capability to their hardware capacity and performance requirements without compromising data sovereignty.
TUMLU Benchmark Validation
Model quality is verified against the TUMLU benchmark — 38,139 native Azerbaijani questions spanning 11 disciplines — providing a transparent, domain-broad measure of real-world language understanding.
How On-Premise Sovereign AI Works in Practice
Frequently Asked Questions about Data Sovereignty and This Model
Does any data get sent to Allmaz or a third party when we use the model?
No. Because the model is deployed fully on-premise, all processing happens within your own infrastructure. Neither Allmaz nor any external party receives your queries, intermediate computations, or model outputs at any point during normal operation.
Why does native Azerbaijani support matter for data sovereignty?
Generic multilingual models often require sending text to external services for preprocessing, or they produce lower-quality results that force teams to rely on third-party correction tools. A model built natively for Azerbaijani — trained on over 651 million curated Azerbaijani words and equipped with a tokenizer that correctly handles the ə character and agglutinative morphology — processes the language accurately on your own hardware, eliminating those external dependencies entirely.
Which parameter size is right for our organization?
The 39B model is well suited to organizations with more constrained hardware budgets or strict latency requirements. The 99B configuration offers a balanced mid-range option that combines strong language capability with moderate resource consumption. The 587B model is designed for maximum language understanding and is appropriate where infrastructure can support it. The right choice depends on your specific hardware environment, throughput needs, and use-case complexity.
What is the TUMLU benchmark and why does it matter?
TUMLU is a validation benchmark comprising 38,139 native Azerbaijani questions across 11 academic and professional disciplines. It provides an objective, domain-broad measure of how well the model understands real Azerbaijani language in practice, rather than relying on translated test sets or proxy evaluations that may not reflect genuine linguistic competence in Azerbaijani.
Can the model be fine-tuned on our internal data without that data leaving our environment?
Yes. Because the model runs entirely on your own infrastructure, any fine-tuning or domain adaptation work can be performed within your network perimeter. Your proprietary training data remains under your control throughout the entire process — from data preparation through training runs to the deployment of the updated model.
Ready to Keep Your Data Fully Under Your Control?
Explore how Allmaz's natively built Azerbaijani language model can be deployed on your own infrastructure — combining genuine data sovereignty with purpose-built linguistic accuracy. Contact the Allmaz team to discuss the right configuration for your organization.
Request a demo