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GKE & Cloud Run
Kubernetes and serverless container platforms with autoscaling tuned to real traffic.
Cloud & DevOps
Google Cloud for data-first enterprises.
GKE and Cloud Run platforms, BigQuery analytics, and Vertex AI — Google Cloud engineering for organizations that treat data as the product.
What we build
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BigQuery analytics
Warehouse modernization and cost-governed analytics at petabyte scale.
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Vertex AI
Gemini, model training, and MLOps on Google's managed AI platform.
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Migration & modernization
Assessment-led moves from on-prem or other clouds with measured cutovers.
How we deliver
Step 01 — Assess
Use-case discovery, data and platform readiness, and a business case with measurable outcomes for Google Cloud.
Step 02 — Build
Senior-led delivery in weekly increments — architecture, security, and quality gates baked into every sprint.
Step 03 — Operate
Production monitoring, SLAs, and continuous improvement through our managed services team in Chennai.
Tools & platforms
GKECloud RunBigQueryVertex AIGeminiPub/SubTerraform
In depth
Google Cloud for data-heavy and AI-native workloads
Google Cloud shines where data volume and AI ambition meet. BigQuery remains the benchmark for serverless analytics, GKE set the standard for managed Kubernetes, and Vertex AI with Gemini gives enterprises a fast, governed route to generative AI. We engineer GCP estates that exploit those strengths without sprawl.
Typical engagements include BigQuery lakehouse builds fed by streaming pipelines, GKE platforms for microservices, and Vertex AI deployments for both classic machine learning and LLM applications. Infrastructure is Terraform-managed; identity and networking follow Google’s security foundations blueprint, hardened for your compliance needs.
For multi-cloud enterprises we make GCP a well-behaved citizen: federated identity, consistent CI/CD, and cost visibility that finance can reconcile. And when analytics is the driver, our business intelligence team turns BigQuery into dashboards leaders actually use.
BigQuery-centered analytics
Serverless lakehouse patterns with streaming ingest and governed access.
GKE done right
Production-grade Kubernetes with autoscaling, observability, and cost controls.
Vertex AI delivery
From AutoML to Gemini-powered applications, deployed with evaluation discipline.
Frequently asked questions
Have a different question? Talk to an engineer, not a salesperson.
Why Google Cloud over the alternatives?
BigQuery and Vertex AI are compelling for analytics-heavy and AI-first workloads; we help you decide with a workload-by-workload comparison, not ideology.
Can you run multi-cloud?
Yes — several clients run GCP for data and another cloud for applications; we design the interconnect and identity model to keep it manageable.
What about egress costs?
Architecture reviews include a data-gravity analysis so egress surprises are designed out, not discovered on the invoice.