Cloud & DevOps Engineering That Keeps Your AI Systems and Apps Running at Scale.
We build the infrastructure, pipelines, and deployment workflows that let your team ship fast, stay reliable, and scale without surprise outages or manual operations.
What we deliver
From setting up cloud environments to building full MLOps pipelines for AI systems — we handle the infrastructure layer so your product team can focus on shipping features.
- Cloud setup and architecture on AWS, GCP, and Azure
- CI/CD pipelines with GitHub Actions and automated testing
- Containerization with Docker and Kubernetes orchestration
- MLOps pipelines for model training, versioning, and deployment
- Infrastructure as code with Terraform and automated provisioning
DevOps for AI teams
AI systems need infrastructure that goes beyond standard web apps — model versioning, evaluation pipelines, dataset management, and inference serving all require dedicated MLOps thinking.
- Model registry and versioning
- Automated retraining pipelines
- A/B testing and canary deployments for models
- LLM prompt versioning and evaluation hooks
- Observability for AI workflows and model performance
Security and compliance by default
We build with access control, secret management, encrypted storage, and audit logging built into every layer — not added as an afterthought.
Outcomes
- Deploy AI systems and web apps reliably without manual infrastructure work
- Eliminate deployment bottlenecks with automated CI/CD pipelines
- Scale cloud infrastructure predictably without surprise cost spikes
Pricing
- Starter — ₹20K – ₹60K: Cloud environment setup + basic CI/CD pipeline
- Growth — ₹60K – ₹2L: Full DevOps infrastructure with Kubernetes and observability
- Enterprise — Custom: MLOps + multi-region cloud architecture
Frequently asked questions
Can you set up CI/CD pipelines for our existing project?
Yes. We can implement automated build, test, and deployment pipelines for web apps, APIs, and AI systems on your current stack.
Do you handle cloud migration from one provider to another?
Yes. We can plan and execute migrations from on-premise or legacy environments to modern cloud platforms with minimal downtime.
What is MLOps and do we need it?
MLOps is the practice of deploying and maintaining machine learning models in production reliably. If you have AI systems in production or planning to deploy them, MLOps practices prevent model drift, deployment failures, and operational blind spots.