What we build
The work the user never sees, and the absence of which the user notices first. We design and operate the infrastructure layer for AI workloads — agentic, generative, and traditional ML alike.
- Scalable architecture — horizontal scale by design, not retrofit. Stateless services, queue-isolated workers, regional failover.
- Cloud-native design — containers, Kubernetes, serverless where it fits. Infrastructure as code from day one.
- Performance optimization — latency budgets per tier, caching strategy, model routing, batched inference, GPU efficiency.
- Security hardening — least-privilege IAM, secrets management, network segmentation, key rotation, vulnerability scanning baked into CI/CD.
Where we deploy
The deployment target is a constraint, not a religion. We engineer for where your data and your compliance live.
AWS
Primary cloud. Bedrock, SageMaker, EKS, Lambda, S3, OpenSearch. Indian regions for DPDP-aligned workloads, US/EU for global.
On-prem GPUs
For sovereignty, latency, or cost reasons. NVIDIA H100/A100 clusters, vLLM/TGI inference servers, ray-serve and triton for orchestration.
Multi-cloud
Where redundancy or sovereignty demands it. Same orchestration layer across providers.
Edge
Where latency is the workload. Distilled models on edge GPUs, with central coordination for evaluation and updates.
How it connects
Infrastructure is the floor on which the rest of the engagement model stands. The other tiers depend on it.
- Data Platform — the lakehouse and pipelines run on this layer.
- Future of Work — the AI agents you direct execute on this layer.
- GenAI Delivery Factory — CI/CD, MLOps, deployment all live here.
- Responsible AI — audit trails, traces, and compliance hooks instrumented at this layer.
The discipline
- Infrastructure as code — Terraform, CDK, Pulumi. Reproducible from day one.
- CI/CD with policy gates — security scans, cost budgets, performance regressions caught before merge.
- Observability default-on — metrics, traces, logs, structured. SLOs, not vibes.
- Zero-downtime deploys — blue/green, canary, feature flags. Rollback is one click.
- Cost discipline — FinOps tagged at deploy time. Spot instances, reserved capacity, model routing tied to budget.