Prometheus for Telecom
The first large language model built natively for Azerbaijani, deployed entirely on your own infrastructure. Available in 587B, 99B and 39B sizes.
Prometheus for Telecom: The First Native Azerbaijani LLM Built for Operators
Telecom operators in Azerbaijan manage millions of subscriber interactions every day, spanning Azerbaijani and Russian, under strict service-quality SLAs. Generic AI models were never designed for agglutinative Azerbaijani morphology: they misread the ə character, fragment words incorrectly, and route sensitive subscriber data through external servers — creating both accuracy gaps and compliance exposure. Prometheus is the first large language model built natively for the Azerbaijani language, trained on more than 651 million curated Azerbaijani words and validated on the TUMLU benchmark across 38,139 native questions and 11 disciplines. It is deployed entirely on your own infrastructure, so your contact center can handle high volumes accurately, keep churn in check, and never expose subscriber data outside your network.
Why Telecom Operators Choose Prometheus
Handle contact-center volumes at scale with a model that genuinely understands Azerbaijani and Russian subscriber conversations, capturing intent accurately rather than approximating it through a mismatched tokenizer.
Keep all subscriber data on-premise — call transcripts, account details, and usage records never leave your network, satisfying local data-residency requirements and internal security policies without compromise.
Reduce churn pressure by delivering faster, more accurate first-contact resolutions that improve subscriber satisfaction across every channel, from virtual agents to agent-assist tools.
Choose the right model size for each workload — 587B for complex retention and billing workflows, 99B for intent classification and agent assist, 39B for high-frequency FAQ deflection — balancing accuracy against infrastructure cost.
Process Azerbaijani text 4.6 times more efficiently than models without a native tokenizer, cutting compute overhead on high-traffic days and reducing the cost per interaction at scale.
Deploy with confidence backed by objective validation on the TUMLU benchmark, which covers 38,139 native Azerbaijani questions across 11 disciplines, providing a reproducible quality baseline before you go live.
Capabilities Built for Telecom Realities
Native Azerbaijani Language Understanding
Prometheus is trained on 651M+ curated Azerbaijani words and uses a native tokenizer that correctly handles the ə character and the full complexity of agglutinative morphology — so subscriber intent is captured accurately, not approximated.
Fully On-Premise Deployment
The model runs entirely within your own infrastructure. No subscriber conversation, account detail, or usage record is transmitted to an external server, giving your compliance and security teams full control.
Mixed AZ/RU Conversation Handling
Operators serve subscribers who switch between Azerbaijani and Russian mid-conversation. Prometheus is designed to follow these mixed-language interactions without losing context or requiring manual language-detection rules.
Scalable Model Tiers
Three parameter sizes — 587B, 99B, and 39B — let you match model capacity to use case. Deploy the largest model for complex billing disputes and retention workflows, and a lighter model for high-frequency FAQ deflection.
SLA-Aware Contact Center Automation
Fast, accurate first-contact resolution directly supports your service-quality SLAs. Prometheus can power virtual agents, agent-assist tools, and post-call summarization without the latency penalties of routing queries to external APIs.
Benchmark-Validated Reliability
Performance is measured against the TUMLU benchmark — 38,139 native Azerbaijani questions spanning 11 disciplines — providing an objective, reproducible quality baseline before you go live.
From Deployment to Live Subscriber Interactions
Frequently Asked Questions
Does Prometheus support both Azerbaijani and Russian in the same conversation?
Yes. Prometheus is designed to handle mixed Azerbaijani and Russian interactions without requiring separate models or manual language-switching logic. This is essential for operators whose subscriber base regularly moves between both languages within a single support session, as forcing a language-detection step introduces latency and creates points of failure in the conversation flow.
How does on-premise deployment affect our data-protection obligations?
Because Prometheus runs entirely within your own infrastructure, subscriber data — including call transcripts, account details, and usage records — never leaves your network. There is no external API call, no third-party cloud processing, and no data-sharing agreement to manage. This makes it significantly easier to satisfy local data-residency requirements, internal security policies, and any contractual SLA obligations that restrict where subscriber information may be processed.
Which parameter size is right for our contact center?
The 39B model suits high-frequency, lower-complexity tasks such as FAQ deflection and basic account queries where throughput and cost efficiency are the priority. The 99B model is a strong fit for intent classification, routing logic, and agent-assist workflows that require stronger language understanding. The 587B model is recommended for complex retention conversations, multi-turn billing disputes, and any task where maximum Azerbaijani language accuracy is critical. Many operators run multiple tiers simultaneously, routing interactions to the appropriate model based on detected complexity.
What does '4.6 times more efficient on Azerbaijani text' mean in practice?
Prometheus uses a native tokenizer that encodes Azerbaijani words — including agglutinative suffixes and the ə character — into fewer tokens than a generic tokenizer would produce for the same text. Fewer tokens per query means the model processes each interaction with less compute, which lowers cost per interaction and reduces response latency. When you are handling millions of subscriber interactions, that efficiency difference has a measurable impact on infrastructure sizing and operating costs.
How was Prometheus validated before release?
The model was evaluated on the TUMLU benchmark, which contains 38,139 native Azerbaijani questions spanning 11 disciplines. TUMLU was designed specifically to test genuine Azerbaijani language comprehension rather than translated or transliterated content, making it a meaningful quality signal for operators who need the model to perform reliably on real subscriber language rather than on sanitized test sets.
Can Prometheus be fine-tuned on our own subscriber interaction data?
Yes. Because the model is deployed on your own infrastructure, you retain full control over the training pipeline. You can fine-tune Prometheus on your historical call transcripts, chat logs, and product-specific terminology to improve accuracy on your particular subscriber vocabulary, tariff names, and service workflows — without sending any of that proprietary data to an external party.
Ready to Bring Native Azerbaijani AI Into Your Contact Center?
Talk to the Allmaz team about a deployment scoped to your subscriber volumes, infrastructure, and SLA requirements. We will help you select the right model size and integration path to get Prometheus running on your network.
Request a demo