Azati builds, integrates, and operates domain-specific language models that drive measurable results. They are fine-tuned on your data to understand your industry’s terminology and respect your security boundaries. We don’t pretrain foundation models from scratch – we fine-tune and adapt proven open-source and foundation models, ground them in your knowledge with RAG, and stay responsible for accuracy, drift, and cost once they’re in production.
Schedule a free LLM consultationAzati’s LLM development services cover the full lifecycle of enterprise large language models: fine-tuning and domain adaptation of open-source and foundation models, retrieval-augmented generation (RAG), LLM-based chatbots and AI assistants, voice and speech-to-text systems, prompt engineering, secure integration with existing systems, and production deployment – on cloud, on-premise, or EU-sovereign infrastructure. Azati (founded 2002; 300+ engineers; EU HQ in Warsaw, US HQ in New Jersey) specializes in domain-specific LLMs (DSLMs) for regulated industries and stays accountable for accuracy, drift, and cost after launch.
You need LLM features shipped on production-grade architecture – evaluated, observable, and maintainable – without a science project that never reaches users.
You need language models that respect data residency and security: on-premise or EU-sovereign deployment, no proprietary data sent to a public API.
You need senior engineering capacity for fine-tuning, RAG, evaluation, and drift control – without building the whole team in-house.
You need LLM automation that cuts real cost and cycle time on regulated workflows – with a partner who owns the result after go-live.
Generic LLMs are trained for everything and optimized for nothing in particular. In regulated, terminology-dense fields – finance, industrial, healthcare & life sciences, legal, and insurance – that gap shows up as hallucinations, missed domain meaning, and compliance risk. A DSLM closes it: smaller, cheaper to run, and grounded in your domain. Most DSLMs are built on top of existing foundation or open-source models and fine-tuned on targeted data – which is exactly how Azati builds them.
Because general-purpose GenAI has underdelivered on ROI, while domain-specific models cut cost and raise reliability – and the market is shifting fast toward them.
What the analysts project:
By 2027, Gartner predicts more than 50% of the generative-AI models enterprises use will be domain-specific – up from about 1% in 2024.
Spending on domain-specific GenAI models rose roughly 279% in a single year – from $302M (2024) to about $1.1B (2025).
By 2028, more than half of enterprises that build their own LLMs from scratch are expected to abandon the effort due to cost, complexity, and technical debt.
Versus general-purpose models, domain-specific models can cut development cost by up to ~50%, deploy faster, and reduce hallucination risk.
We fine-tune open-source and foundation models on your domain data – and lead with RAG and context engineering before heavier tuning, because grounding usually beats training for accuracy and cost.
Retrieval-augmented generation over your documents and databases, so answers trace back to your sources instead of being invented.
Human-in-the-loop checkpoints for high-stakes decisions – a requirement under the EU AI Act, and a design default for regulated workflows.
On-premise or EU-sovereign deployment of open-source models on your own GPUs, so sensitive data never leaves your perimeter.
Fine-tune and adapt open-source and foundation models on your domain data so they understand your terminology and meet your security requirements – without the cost and risk of pretraining from scratch. We lead with RAG and context engineering, and apply targeted fine-tuning where it measurably helps.
Turn unstructured text and speech into structured intelligence – entity extraction, classification, sentiment, semantic search, and LLM-powered summarization tailored to your industry.
Connect language models to the systems you already run – CRM, ERP, databases – with reliable, secure APIs and data pipelines, without disrupting current workflows.
Retrieval-augmented generation that grounds the model in your own documents and data, so answers cite your sources instead of inventing them. The backbone of accurate, trustworthy enterprise assistants.
Accurate speech recognition across languages, accents, and industry vocabulary – from real-time transcription to voice-driven automation.
Design, test, and refine prompts that deliver consistent, accurate outputs while reducing token usage and cost.
Context-aware conversational interfaces and assistants that handle complex queries and integrate with your business systems. For autonomous, tool-using agents, we extend into agentic architectures.
Production-ready deployment that grows with you – reliably across cloud, hybrid, on-premise, and EU-sovereign environments.
Strategic guidance to move from experimentation to production: technology selection, build-vs-buy, ROI modeling, and a clear roadmap.
Azati’s engineering centre is in the EU (Warsaw, Poland). That lets us deploy open-source LLMs on your own GPUs or on EU-sovereign infrastructure, with data staying inside your perimeter end to end. For European enterprises in regulated sectors, that removes the single biggest blocker to enterprise LLM adoption: sending proprietary data to an external model.
Discuss EU-sovereign LLM deploymentWhy it matters now
Proven in production: for a 12,000-employee insurer, Azati deployed open-source models on the client’s own GPU hardware – no source code or sensitive data ever left the corporate network – and for a regulated information-sharing platform, an on-premise LLM with custom encryption and role-based access delivered 100% data-privacy compliance.
LLM infrastructure is where AI budgets quietly leak: idle GPUs, oversized models doing small jobs, and every query routed to the most expensive endpoint. As enterprises scale GenAI, cost governance has become a board-level CIO priority – and Gartner expects GenAI cost uncertainty to drive rapid adoption of “augmented FinOps” tooling.
A fast, fixed-scope engagement that finds where your LLM spend is wasted and returns a prioritized plan to cut it – typically high ROI because the savings are recurring.
Match each task to the smallest model that meets quality – most queries don’t need your most expensive model.
Route requests between internal open-source models and external APIs automatically, keeping cost and data exposure down. (Azati built exactly this – “AIRouter” – for an enterprise insurer.)
Find idle and over-provisioned GPU capacity; right-size on-prem and cloud inference infrastructure.
Establish a cost-per-query baseline and monitoring so spend stays predictable as usage grows.
"We focus on what really slows down your business. Our approach is simple: start small, prove real value, then scale up."
LLMs don’t fail at launch – they erode after it. Providers deprecate model versions, prompts drift, costs creep, and outputs degrade against changing data. Azati’s build-and-operate model is designed for that window.
| Layer | Technologies |
|---|---|
| LLMs & model families | OpenAI · LLaMA · QWEN · BLOOM · Google T5 · BERT · RoBERTa · Whisper · Amazon Bedrock · Gemini API |
| RAG & orchestration | LangChain · LangGraph · LangSmith · LlamaIndex · Pinecone |
| Frameworks & serving | PyTorch · TensorFlow · Hugging Face · vLLM · Open WebUI · ONNX · NVIDIA CUDA |
| NLP & speech | spaCy · Sentence-Transformers · Google ASR · custom NER |
When you need autonomous, tool-using systems – multi-agent orchestration, guardrails, RAG & memory, agent evaluation, and stabilization of agents that misbehave.
Explore Agentic AIWhen the LLM has to plug into regulated workflows – compliance-grade orchestration, document-intelligence pipelines, ERP & CRM integration (SAP, Salesforce), output-validation contracts, audit-ready logging.
Explore AI IntegrationWhen you want us to run the model in production – continuous monitoring, prompt-drift detection, cost optimization, benchmarking, retainer support.
Explore Managed AIWhen the problem goes beyond language – computer vision, predictive analytics, document intelligence, and classical ML.
Explore GenAI & MLBecause an LLM is only as good as the data under it – data pipelines, data modernization & migration, data quality, ML feature stores, data strategy & governance, and predictive analytics.
Explore Data EngineeringWhen your incumbent systems can’t host AI yet – legacy system modernization, AI-ready architecture, microservices migration, cloud-native platforms, and DORA & EU AI Act alignment, so older cores can actually run modern LLMs.
Explore Legacy ModernizationWe build LLMs that speak your industry’s language – fine-tuned and grounded in your data for accuracy, relevance, and measurable impact.
An EU engineering team that deploys open-source models inside your perimeter – data residency and EU AI Act alignment built in, not bolted on.
Full-cycle delivery and build-and-operate: evaluation, drift control, and cost governance after launch – even without an internal MLOps team. Learn more about our approach.
Staff augmentation, a dedicated team, full project outsourcing, or a managed build-and-operate retainer – we match the engagement model to your goals, budget, and timeline, and help you choose the most suitable one rather than forcing a fixed package.
Describe your use case – domain-specific model, RAG assistant, on-prem deployment, or an AI FinOps audit. We come back within 24 hours with team availability and a rough plan. NDA on request before the first call, and the engineers who scope your work are the ones who build and operate it.
Let’s talkA DSLM is a language model fine-tuned or adapted for one industry or business function, trained on that domain’s terminology and data. Compared with general-purpose models, DSLMs deliver higher accuracy, fewer hallucinations, lower running cost, and better compliance alignment. Azati builds DSLMs by adapting open-source and foundation models – not by pretraining from scratch.
No – and for almost all enterprises we’d advise against it. Pretraining a foundation model from scratch is costly and slow, and Gartner expects most enterprises that try it to abandon the effort. Azati fine-tunes and adapts proven open-source and foundation models on your domain, and grounds them with RAG, which delivers production value far faster.
Usually both, in that order. Retrieval-augmented generation (RAG) grounds the model in your current data and is the fastest path to accurate, citable answers. Fine-tuning helps when you need the model to adopt domain terminology, tone, or task behavior that prompting and retrieval can’t achieve. Azati leads with RAG and applies targeted fine-tuning where it measurably improves results.
Yes. As an EU-based engineering team, Azati deploys open-source LLMs on your own GPUs or on EU-sovereign infrastructure, so proprietary data never leaves your perimeter. This is how we serve regulated enterprises that cannot send data to a US-based cloud or public API.
Through AI FinOps: right-sizing models to tasks, routing requests intelligently between internal open-source models and external APIs, optimizing GPU utilization, and benchmarking cost-per-query. Azati offers a fast, fixed-scope AI FinOps Audit that identifies recurring savings in existing LLM infrastructure.
RAG grounds answers in your sources so they can be verified; output validation and human-in-the-loop checkpoints catch high-stakes errors; and continuous evals plus prompt- and model-drift monitoring keep quality stable as data and model versions change.
It depends on scope. Domain adaptation and RAG-based assistants typically reach a working production pilot in roughly 6–10 weeks; larger programs are phased. Azati provides detailed timelines during the initial assessment.
Azati implements encryption in transit and at rest, access controls, and audit logging, with engineering aligned to GDPR, the EU AI Act, and HIPAA requirements. For sensitive workloads we offer on-premise and EU-sovereign deployment in isolated environments.
Yes. Azati designs APIs and integration layers connecting LLMs to CRM, ERP, databases, and custom applications, maintaining security and performance throughout.
Yes – via Managed AI: performance monitoring, model retraining, prompt optimization, drift detection, and cost governance, scoped to your needs.
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