Cloud AI Infrastructure
We stand up the platform layer so your AI systems are secure, observable, scalable, and cost-governed from day one.
Production AI on AWS, Azure, and GCP
Most AI projects die in production, not in the notebook. We build the infrastructure that keeps them alive: VPC-isolated inference, private model endpoints, secrets and key management, observability, cost controls, and CI/CD for prompts, data, and models. IaC-first, cloud-native, auditable.
Outcomes
Our approach.
Landing zone
Account or subscription structure, VPC and private networking, KMS, IAM baselines, and audit logging — so every later AI workload lands in a secure-by-default environment.
Private model endpoints
Bedrock on AWS, Azure OpenAI on Azure, Vertex on GCP — deployed with private networking, customer-managed keys, and quota management.
Observability & cost
Traces, metrics, logs, and per-call token + dollar accounting wired into CloudWatch, App Insights, Datadog, or Langfuse. Budgets and alerts per tenant.
CI/CD for AI
Prompts, evals, embeddings, and model configs ship through pipelines like any other code. Rollback is a single commit.
What you get.
Production-shaped, from day one.
# Private Bedrock endpoint in a VPC subnet
resource "aws_vpc_endpoint" "bedrock" {
vpc_id = aws_vpc.app.id
service_name = "com.amazonaws.us-east-1.bedrock-runtime"
vpc_endpoint_type = "Interface"
subnet_ids = aws_subnet.private[*].id
security_group_ids = [aws_security_group.bedrock.id]
private_dns_enabled = true
tags = {
tier = "ai"
compliance = "hipaa"
}
}A proven shape for this solution.
We adapt it to your cloud, data, and compliance requirements. Nothing here is boilerplate — every layer is justified by the numbers.
Where this shows up.
- HIPAA-aligned healthcare AI stack with private endpoints
- Multi-tenant SaaS with per-customer cost isolation
- Regulated enterprise rollout with audit + DLP
- High-throughput embedding and ingestion pipelines
What we use.
We’re not religious about tools. We pick what fits your constraints and team.
Shipped examples.
Healthcare patient data mapping & health information chat
Mapped and normalized patient data to power a grounded chat experience where patients can ask questions about their own health information — safely.
What teams usually ask.
AWS, Azure, or GCP — which should we use?
+
The one you already run. If you're AWS-native, Bedrock. Azure shop, Azure OpenAI. GCP, Vertex. Cross-cloud is expensive and rarely worth it until scale demands it.
Can this be HIPAA- or SOC 2-aligned?
+
Yes. We deploy with private endpoints, customer-managed keys, audit logging, and documented controls. We provide scaffolding — your compliance team signs off on the final posture.
How do you control AI costs?
+
Model routing, prompt and retrieval caching, per-tenant budgets, and alerts on anomalous usage. We instrument token and dollar accounting at the request level so you can attribute every cent.
Related solutions.
MCP Servers
Custom Model Context Protocol servers that expose your systems to Claude, ChatGPT, Cursor, and any MCP-aware client.
Retrieval-Augmented Generation
End-to-end RAG pipelines from ingestion to retrieval to answer generation, built for accuracy and cost control.
Agents & Workflow Automation
Agentic workflows that read, write, and act across your existing tools — with human-in-the-loop where it matters.
Ready to accelerate your tech growth?
Schedule your free consultation today and let's discuss how we can help your business scale efficiently.
