Azati designs, trains, and operates custom machine-learning and generative-AI systems for enterprises – predictive models, computer vision, NLP, RAG, fine-tuned LLMs, and AI agents. We deliver AI systems your team can keep in production, not demos that break under real traffic.
Talk to an AI engineering leadAzati is a custom software and AI engineering company (founded 2002; 300+ engineers; EU HQ in Warsaw, US HQ in New Jersey) that builds and operates production-grade AI and machine-learning systems. Its advanced AI & ML practice covers custom ML model development, predictive analytics and AI-powered business intelligence, computer vision and video analytics, natural-language processing and conversational AI, generative AI (fine-tuned LLMs, RAG, multi-agent systems), document intelligence, and on-premise / private-cloud GenAI. Azati’s defining difference is production ownership – it stays accountable for model accuracy, drift, cost-per-query, and uptime after launch, including for clients with no internal MLOps team.
End-to-end machine-learning models built for your data and your decisions – from classification and forecasting to anomaly detection and recommendation engines.
Turn historical and real-time data into forecasts and decision-ready intelligence – embedded into the dashboards and workflows your teams already use.
Enterprise generative AI grounded in your knowledge – retrieval-augmented generation, fine-tuned and domain-specific LLMs, and multi-agent systems built for security and traceability.
Systems that understand language and speech – chatbots, voice agents, and analytics that turn unstructured text and audio into structured insight.
Real-time detection, recognition, and quality control that turn images and video into structured, automatable intelligence.
Convert document chaos into searchable, structured data – intelligent OCR, classification, and semantic search that understands meaning, not just keywords.
The engineering layer that makes models last: training at scale, fine-tuning on your domain, and the MLOps pipelines that keep accuracy from quietly decaying.
When data can’t leave your perimeter: generative AI and enterprise knowledge bases deployed on-premise or in your private cloud, fully under your control.
Whether you own engineering, IT, data, or transformation, the challenge is the same: getting AI onto production-grade architecture that's secure, observable, and maintainable – integrated with the systems you already run (ERP, CRM, data warehouse), able to run on-premise or in your private cloud when data residency demands it, and stable once real traffic hits. The hard part is rarely the model; it's everything around it – clean data, secure deployment, integration, and keeping accuracy from drifting after go-live. That's the work Azati is built for.
Move from experimentation to production with a clear path: identify high-impact use cases, assess AI readiness, and build the roadmap and infrastructure to get there.
Identify high-impact AI opportunities, define success metrics, constraints, and ROI expectations.
Evaluate data quality, build pipelines, perform feature engineering, set governance.
Architect the approach, run experiments, compare architectures, validate hypotheses.
Train at scale with hyperparameter tuning for accuracy and latency.
Test against holdout data and edge cases; validate fairness, bias, and explainability.
Containerize, build CI/CD, implement monitoring and automated retraining.
Track performance, detect drift, iterate on real-world feedback.
Most AI projects fail in month three, not week one. A model that looked great in evaluation starts drifting against real-world data, costs creep, and accuracy decays silently. Azati’s build-and-operate model exists for exactly that window.
The Enterprise AI Readiness Diagnostic scores your organisation across six pillars – with the 2026 data on why most aren’t agent-ready yet.
Clutch has named Azati among the top AI, machine learning, and NLP companies – independent validation of the same engineering team that designs, trains, and operates the systems on this page.
| Layer | Technologies |
|---|---|
| Frameworks & training | PyTorch · TensorFlow · Scikit-Learn · Hugging Face · ONNX · NVIDIA CUDA |
| LLM & GenAI | OpenAI · ChatGPT · LLaMA · BLOOM · Google T5 · BERT · RoBERTa · Stable Diffusion · Diffusers · Amazon Bedrock · Gemini API |
| RAG & orchestration | LangChain · LlamaIndex · Pinecone |
| Vision & OCR | OpenCV · Tesseract OCR |
Enterprise MLOps pipelines, automated retraining, and monitoring that keep AI performing under real traffic. We engineer systems that run 24/7 with 99.5% production model uptime – not prototypes that break in week three.
Senior ML engineers who’ve solved your class of problem before – embedded into your team in under two weeks, with low churn and deep domain continuity.
Cloud, on-premise, EU-private cloud, or on-device at the edge. When data residency, security, or connectivity rules out a public AI API, we build and run the AI inside your perimeter – or directly on the device.
Azati was founded in 2002, and over the years in business has maintained an impressive track record responsibly delivering value-added services. Global leaders including Shell, Petronas, ADNOC, Woodside Energy, Accuris, Clarivate, MedPRO use solutions built with Azati’s technology under the hood.
At Azati, we use advanced AI and machine learning not just to follow trends, but to transform raw data into actionable insights and smart solutions that drive real business value. Each project is an opportunity for our expert team to apply deep technical knowledge, from building predictive models to automating processes. It’s this blend of passion, unique expertise, and precision that helps our clients solve complex problems and achieve lasting success.
Yuri V.
AI/ML Team Lead
Bring a defined use case, a messy dataset, or just a goal – we’ll tell you what’s realistic and how we’d approach it. Senior engineers who stay accountable through production, in whatever engagement fits your stack and timeline.
Let’s talkIt is the design, training, and operation of custom machine-learning and generative-AI systems for enterprises – covering predictive models, computer vision, NLP, RAG, fine-tuned LLMs, document intelligence, and AI agents. Unlike a one-off model build, Azati stays responsible for the system in production: accuracy, drift, cost, and uptime.
Yes. When data residency, security, or regulation means data can’t leave your perimeter, Azati builds and runs generative AI and enterprise knowledge bases on-premise or in your private cloud, fully under your control – instead of relying on a public AI API.
Both. Azati’s build-and-operate model means we can keep running the AI after launch – continuous evaluation against golden datasets, drift monitoring, automated retraining, cost-per-query optimization, and audit-ready logging – including for teams with no internal MLOps function.
We run continuous evals against golden datasets to detect silent accuracy decay, monitor data drift with automated retraining triggers, and for generative systems add retrieval grounding, output validation, and execution traces so answers can be checked against source data.
Yes. The Managed AI engagement is designed precisely for this: Azati operates the AI layer as a service, with monitoring, performance reporting, and traceability, so you get production AI without building an MLOps team first.
Scope-dependent, but Azati typically moves from experimentation through validation and deployment in roughly 6–8 weeks for a focused use case, using proven frameworks rather than corner-cutting. Larger enterprise programs are phased to preserve operational continuity.
Engineering practices align with GDPR and EU AI Act requirements: documentation, decision traceability, audit trails, and human-in-the-loop checkpoints for high-stakes decisions. On-premise and private-cloud deployment options keep sensitive data inside your environment.
This page covers the full advanced AI & ML practice – classical ML, vision, NLP, and generative AI together. For deep LLM work (custom and domain-specific LLMs, fine-tuning, speech-to-text) see LLM Development Services; for autonomous, tool-using systems see Agentic AI Engineering. They share the same engineering team and production-ownership approach.
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