Cloud Infrastructure Built for AI Scale
Cloud infrastructure is no longer just about compute and storage. It's the foundation on which your AI systems run, your teams collaborate, and your data flows securely across the organization. Getting it right — from architecture decisions to cost optimization — is the difference between a cloud investment that delivers and one that becomes technical debt.
We bring deep hands-on experience with AWS, Google Cloud Platform, Google Workspace, and HubSpot — designing and building infrastructure and revenue systems that are secure, scalable, and optimized for the AI workloads enterprises are deploying today.
The Reality of Enterprise Cloud
Most organizations are running a patchwork of cloud environments — some workloads on AWS, some on GCP, some on legacy infrastructure — with no unified governance, inconsistent security posture, and runaway costs. We clean that up and build something that actually runs.
Most organizations are running a patchwork of cloud environments — some workloads on AWS, some on GCP, some on legacy infrastructure — with no unified governance, inconsistent security posture, and runaway costs. We clean that up and build something that actually runs.
What We Build and Manage
Amazon Web Services
- Cloud architecture design and Well-Architected reviews
- Migration from on-premises and competing cloud providers
- AI/ML workload optimization on SageMaker and Bedrock
- Cost optimization — Reserved Instances, Savings Plans, rightsizing
- IAM policy design, SCPs, and permission boundary architecture
- VPC architecture, networking, and security group hardening
- EKS/ECS container orchestration and Fargate deployments
- Amazon Bedrock AgentCore for production AI agent deployment and management
- S3 Vectors for native vector storage and similarity search at scale
- AWS Glue and Athena for serverless ETL pipelines and data lake querying
- Amazon QuickSight for BI dashboards and embedded analytics
Google Cloud Platform
- GCP infrastructure design and Anthos hybrid deployments
- BigQuery data warehouse architecture and optimization
- Vertex AI infrastructure for ML training and serving
- GKE cluster design, node pool optimization, and autoscaling
- Cloud IAM, VPC Service Controls, and organization policy
- Data pipeline architecture with Dataflow and Pub/Sub
- Multi-cloud strategy and workload portability
- Cloud Run serverless container deployments and scaling
- Infrastructure as Code with Terraform and Cloud Deployment Manager
- Cloud Armor WAF configuration and DDoS protection
- Cost optimization — committed use discounts, rightsizing, and budget controls
Google Workspace
- Google Workspace deployment, migration, and administration
- Migration from legacy email platforms and other productivity suites
- Drive, Shared Drives, and data governance configuration
- Admin console security policies and endpoint management
- AppSheet and Apps Script workflow automation
- Gemini for Workspace AI features deployment and governance
- Google Meet and Chat enterprise configuration
- Chrome Enterprise deployment and MDM device management
HubSpot
- HubSpot CRM implementation, configuration, and data migration
- Marketing Hub — email, automation, landing pages, and lead scoring
- Sales Hub — pipelines, sequences, deal automation, and forecasting
- Service Hub — ticketing, knowledge base, and customer portal setup
- Operations Hub — data sync, workflow automation, and data quality
- HubSpot and Salesforce CRM integration and bidirectional sync
- Reporting, dashboards, and revenue attribution across hubs
AI Workload Optimization
Running AI at enterprise scale requires infrastructure decisions that most cloud teams haven't had to make before — GPU instance selection, inference cost optimization, model serving architecture, and data pipeline design for training workloads. We've built and optimized these systems across AWS and GCP and bring that experience directly to your infrastructure.
- GPU infrastructure: GPU instances right-sized for training vs. inference workloads across AWS and GCP
- Inference cost optimization: Spot instances, Savings Plans, and auto-scaling for production model serving
- Data pipeline architecture: High-throughput ingestion and preprocessing pipelines for training and RAG workloads
- Secure AI infrastructure: VPC isolation, private endpoints, and data residency controls for regulated AI workloads
Multi-Cloud and Hybrid Architecture
The Problem with Fragmented Cloud
- Inconsistent security posture across environments
- No unified visibility into cost and performance
- Duplicated tools and governance overhead
- Data gravity preventing workload portability
- Engineering teams siloed by cloud platform
Our Multi-Cloud Approach
- Unified identity and access management across providers
- Centralized cost management and tagging strategy
- Consistent security controls via policy-as-code
- Workload placement strategy based on cost, compliance, and capability
- Platform engineering that abstracts cloud complexity from app teams
Our Approach: Every cloud engagement starts with a Well-Architected review — assessing your current state against operational excellence, security, reliability, performance efficiency, cost optimization, and sustainability. We fix what's broken and build what's missing.