DevOps & Artificial Intelligence

Engineering reliable infrastructure and intelligent systems

Azerxlabs partners with engineering teams to automate delivery pipelines, modernize cloud infrastructure, and build production-grade AI products — so you ship faster, scale safely, and stay ahead.

120+
Projects delivered
99.95%
Avg. platform uptime
8+
Years in DevOps & AI
40%
Avg. cost reduction

Tools & platforms we work in every day

Kubernetes Terraform AWS Azure GCP GitHub Actions Docker Python PyTorch Anthropic / OpenAI APIs Prometheus & Grafana
What we do

Two disciplines. One integrated team.

We combine deep infrastructure expertise with applied AI engineering, so your automation and your intelligence layer are built to work together from day one.

DevOps Engineering

Ship faster, break less

End-to-end CI/CD, infrastructure as code, and cloud-native architecture that lets your team deploy with confidence, multiple times a day.

  • CI/CD pipeline design & automation
  • Kubernetes & container orchestration
  • Infrastructure as Code (Terraform, Ansible)
  • Observability, SRE & on-call maturity
AI Solutions

Build with intelligence, not hype

From LLM-powered products to predictive models and MLOps, we design AI systems that are grounded in your real data and shipped to production.

  • LLM & agentic application development
  • Custom ML models & predictive analytics
  • Data pipelines, RAG & MLOps
  • AI strategy, audits & team enablement
Why teams choose us

Senior engineers. No hand-offs. No hype.

You get a small, senior team that treats your infrastructure and your AI roadmap like its own — practical, security-conscious, and focused on measurable outcomes over trendy tooling.

01

Security & compliance built in

DevSecOps practices, least-privilege access, and audit-ready infrastructure from the first commit.

02

Vendor & model agnostic

We recommend the right cloud, tools, and AI models for your constraints — not the ones we're paid to push.

03

Built to hand off

Documentation, runbooks, and knowledge transfer so your team owns the system with confidence.

# deploy.yaml pipeline: build: docker buildx bake test: pytest -q --cov scan: trivy image --exit-code 1 deploy: strategy: blue-green target: eks/prod-us-east-1 approval: required # ai_service.py agent = Agent( model="claude-sonnet-5", tools=[retriever, sandbox], guardrails="strict" )
How we work

A clear path from idea to production

Every engagement follows the same disciplined process, whether we're migrating your infrastructure or shipping your first AI feature.

01

Discover

Audit systems, data, and goals

02

Design

Architecture & roadmap sign-off

03

Build

Iterative delivery in short sprints

04

Deploy

Automated, monitored rollout

05

Optimize

Tune cost, performance & models

Client results

Trusted by engineering & product teams

"MRM rebuilt our deployment pipeline in three weeks. Release time went from a day of manual steps to under ten minutes, fully automated."

JT
James T.
VP Engineering, fintech scale-up

"They shipped our RAG-based support assistant end-to-end — data pipeline, model evaluation, guardrails — and it's been running in production with zero major incidents."

SK
Sara K.
Head of Product, SaaS platform

"Our AWS bill dropped 38% after their infrastructure review, without touching performance. Clear communication throughout the whole engagement."

DL
Daniel L.
CTO, logistics tech company
Let's build

Ready to modernize your infrastructure
and put AI to work?

Tell us about your systems and goals — we'll come back with a scoped plan, not a sales pitch.