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We work primarily with Series A-C startups who are adopting AI but don't have ML infrastructure expertise, and AI/ML teams who can build models but struggle with production deployment and reliability. If you're hitting infrastructure walls while scaling AI features, we can help.

It depends on your needs. We offer 2-week sprints for focused projects (like setting up model serving or monitoring), ongoing retainers for continuous infrastructure management, and project-based engagements. Every engagement starts with a free 30-minute audit to understand your situation.

We work with AWS (SageMaker, EKS), GCP (Vertex AI, GKE), and Azure (Azure ML, AKS). Our approach is cloud-agnostic — we recommend the best fit for your specific workload, scale, and budget. We also work with hybrid and multi-cloud setups.

We're SREs first, not just ML engineers. Our team comes from Fortune 500 infrastructure — we've operated systems handling millions of requests daily. We don't just deploy your model, we make sure it stays up with proper monitoring, drift detection, and incident response.

Absolutely — that's one of our core services. We set up the entire pipeline: containerization, model serving infrastructure, CI/CD specifically for ML models, monitoring, and automated retraining. We go from Jupyter notebook to production Kubernetes cluster.

Every project is different, so we don't publish fixed prices. Book a free call and we'll scope it together — we provide transparent, detailed proposals with no hidden costs. We offer flexible engagement models to fit different budgets.

Yes. We build AI-powered SRE agents for incident detection and auto-remediation, RAG-based internal knowledge systems, custom LLM integrations, and AI cost optimization tooling. If it involves making AI work reliably in production, we can help.

We respond to all inquiries within 24 hours. For existing clients on retainer, we provide faster response times as part of the SLA. We're based in India and serve clients globally.