Build an internal AI assistant
Build an internal AI assistant with Prometheus: a practical, on-prem approach built for Azerbaijani teams.
Deploy a High-Performance Internal AI Assistant for Azerbaijani
Most general-purpose language models struggle with the nuances of the Azerbaijani language, often misreading characters, fragmenting words, and producing unreliable outputs. Prometheus solves this by serving as the first large language model built natively for Azerbaijani. Trained on a curated corpus of over 651 million words and validated against the TUMLU benchmark—which comprises 38,139 native questions across 11 disciplines—Prometheus provides the linguistic precision and cultural nuance required for professional organizational use. Beyond linguistic accuracy, Prometheus is engineered for maximum security and operational flexibility. By deploying fully on-premise, it ensures that sensitive corporate data never leaves your internal network, eliminating the risks associated with third-party cloud APIs. Whether you are automating internal workflows or building a sophisticated knowledge base, Prometheus offers a practical, secure foundation for Azerbaijani teams to leverage generative AI without compromising data sovereignty.
The Strategic Advantages of Prometheus
Native Linguistic Precision: Specifically engineered to handle the ə character and complex agglutinative morphology that typically cause generic models to fail.
Absolute Data Sovereignty: Full on-premise deployment ensures your proprietary data and queries remain entirely within your own network.
Superior Computational Efficiency: Processes Azerbaijani text 4.6× more efficiently than non-native models, significantly reducing compute overhead.
Scalable Infrastructure Options: Available in 39B, 99B, and 587B parameter sizes to align with your specific hardware capacity and workload complexity.
Evidence-Based Performance: Validated on the TUMLU benchmark, providing a transparent and measurable quality baseline across 11 different disciplines.
Enterprise-Grade Grounding: Trained on 651M+ curated Azerbaijani words, ensuring outputs are grounded in high-quality, native language patterns.
Core Technical Capabilities
Native Azerbaijani Tokenizer
Prometheus uses a tokenizer purpose-built for Azerbaijani morphology. It correctly handles the ə character and the language's agglutinative structure, so words are not split incorrectly and meaning is preserved across complex grammatical forms.
Three Deployment Sizes
Available in 39B, 99B, and 587B parameter configurations, Prometheus scales to match your hardware and use-case requirements — from department-level assistants to enterprise-wide deployments.
Fully On-Premise
The model runs entirely within your own infrastructure. No queries, documents, or responses leave your network, making it suitable for organizations with strict data governance or confidentiality requirements.
TUMLU Benchmark Validation
Prometheus has been evaluated on TUMLU, a benchmark of 38,139 native Azerbaijani questions across 11 disciplines. This gives teams a concrete, independently structured quality reference rather than marketing claims alone.
Large Curated Training Corpus
Trained on over 651 million curated Azerbaijani words, Prometheus reflects the breadth and nuance of the language as it is actually written and used — not as a secondary output of a multilingual model.
Improved Processing Efficiency
Because the tokenizer and model are aligned to Azerbaijani text, Prometheus processes the language more efficiently than models designed for other languages, helping you get more from your available compute resources.
Implementation Roadmap for Prometheus
Common Questions About Prometheus
Does Prometheus send any data to external servers?
No. Prometheus is deployed fully on-premise, meaning all queries, documents, and responses remain within your own network at all times, ensuring total data privacy.
Why is a native Azerbaijani model superior to a multilingual one?
Generic models often struggle with Azerbaijani morphology and the ə character, leading to fragmented tokens and unreliable answers. Prometheus is 4.6× more efficient on Azerbaijani text and handles agglutinative structures natively for higher accuracy.
How do I determine which parameter size (39B, 99B, or 587B) is right for me?
The choice depends on your hardware and complexity needs: 39B is ideal for lighter workloads, 99B balances performance and resources, and 587B is designed for demanding enterprise-scale applications.
What is the TUMLU benchmark and how does it validate the model?
TUMLU is a rigorous benchmark consisting of 38,139 native Azerbaijani questions across 11 disciplines. It provides a transparent, measurable quality reference to ensure the model performs reliably across various domains.
Can Prometheus be integrated into our existing software ecosystem?
Yes. Prometheus is designed to integrate with internal knowledge bases, document repositories, and existing workflows. The Allmaz team provides support to ensure the model aligns with your current technical infrastructure.
Ready to Build Your Internal AI Assistant?
Talk to the Allmaz team about deploying Prometheus in your organization — on your infrastructure, in your language, on your terms.
Request a demo