Our Services

From AI cost optimization to secure cloud platforms, we deliver infrastructure expertise that reduces spend, accelerates delivery, and meets the strictest compliance requirements.

AI Infrastructure & Platform Engineering

Design and build cloud platforms for modern application workloads and AI systems, with the automation, reliability, and scalability needed for production.

We design and build cloud platforms for both modern application workloads and AI systems. Our work spans core infrastructure, platform automation, observability, and production readiness, along with specialized support for GPU-based training and low-latency inference. From Kubernetes and infrastructure as code to workload scheduling and scaling strategies, we help teams build resilient platforms that perform reliably in production.

Capabilities

Cloud platform architecture and workload design
Kubernetes, IaC, and platform automation
Scalable networking, storage, and compute foundations
Autoscaling for application, training, and inference workloads
Observability, reliability, and capacity planning
Production readiness for data and AI platforms

Typical Engagement Flow

We typically begin with an assessment, move into implementation, and then provide ongoing support as needed.

1One-Time

Infrastructure Assessment

Review your current platform, identify bottlenecks and operational risks, and define a practical roadmap for infrastructure improvements across cloud, data, and AI workloads.

Starting at $5,000

Start Assessment

2Project-Based

Platform Build

Implement the infrastructure changes identified in the assessment, from core platform foundations and automation to production-ready environments for data and AI workloads.

Custom scoped

Scoped after assessment

3Recurring

Managed Platform Operations

Provide ongoing management, optimization, and operational support across your platform, improving reliability, performance, and scalability as workloads evolve.

Custom scoped

Available after delivery

Some clients start with an assessment only, but most continue into implementation and, where needed, ongoing support.

Optimize Your AI Platform

AI FinOps & Cost Optimization

Reduce cloud and AI infrastructure spend through better utilization, cost visibility, governance, and targeted optimization.

Most organizations overspend on AI infrastructure without realizing it. Idle GPUs, oversized instances, and inefficient serving pipelines can quietly drive up costs. We help teams optimize AI spend through GPU utilization analysis, training and inference cost profiling, model routing, serving efficiency, workload placement, and cluster right-sizing. We also establish FinOps governance with dashboards, alerts, and automated policies across AWS, Azure, and GCP.

Capabilities

GPU utilization optimization and cluster right-sizing
Training and inference cost profiling
Model routing, caching, and serving efficiency
Workload placement across cloud and GPU resources
FinOps dashboards, alerts, and spend visibility
Governance policies and automated cost controls

Typical Engagement Flow

We typically begin with an assessment, move into implementation, and then provide ongoing support as needed.

1One-Time

FinOps Assessment

Analyze cloud and AI infrastructure spend, identify waste and inefficiencies, and prioritize the highest-impact opportunities for cost optimization.

Starting at $3,000

Start Assessment

2Project-Based

Optimization Implementation

Implement the cost optimization changes identified in the assessment, including cluster right-sizing, governance policies, and automated cost controls.

Custom scoped

Scoped after assessment

3Recurring

Managed FinOps

Provide ongoing monitoring, optimization, and cost governance across your cloud and AI infrastructure, keeping spend aligned with usage as your environment evolves.

3–5% of cloud spend

Available after delivery

Some clients start with an assessment only, but most continue into implementation and, where needed, ongoing support.

Get a Free Cost Audit

MLOps & AI Model Operations

Operationalize ML and AI systems with the pipelines, deployment workflows, monitoring, and controls needed for reliable production delivery.

Getting models from development to production reliably is one of the hardest challenges in AI. We build the infrastructure, automation, and operational workflows needed to train, deploy, monitor, and improve models at scale. Our work includes CI/CD for ML, model registries, automated testing, production monitoring, and release management. For teams working with LLMs and agents, we also build evaluation pipelines, prompt and model version control, inference monitoring, and guardrails so systems stay reliable as teams iterate.

Capabilities

ML pipeline automation and CI/CD for models
Model versioning, registries, and artifact management
Automated testing and deployment workflows
Inference monitoring, drift detection, and release management
LLM and agent evaluation pipelines
Prompt and model version control with guardrails

Typical Engagement Flow

We typically begin with an assessment, move into implementation, and then provide ongoing support as needed.

1One-Time

MLOps Assessment

Evaluate your model delivery workflow, identify operational gaps, and define the next steps for production readiness and scale.

Starting at $5,000

Start Assessment

2Project-Based

MLOps Implementation

Implement the pipelines, automation, and tooling identified in the assessment, from CI/CD for models to monitoring, registries, and production deployment workflows.

Custom scoped

Scoped after assessment

3Recurring

Managed MLOps

Provide ongoing management of your ML infrastructure and model operations, including pipeline maintenance, performance monitoring, and operational support as models and teams scale.

Custom scoped

Available after delivery

Some clients start with an assessment only, but most continue into implementation and, where needed, ongoing support.

Operationalize Your ML

AI Security & Compliance

Strengthen cloud and AI environments with integrated security controls, compliance support, and secure deployment architectures.

Security cannot be an afterthought in modern AI infrastructure. We integrate security directly into CI/CD and ML pipelines, implement compliance-as-code for frameworks like SOC 2, ISO 27001, and FedRAMP, and help teams manage AI-specific risks across cloud and model operations. For defense and regulated environments, we also support zero-trust architectures and air-gapped or disconnected deployments so infrastructure meets strict security requirements without slowing delivery.

Capabilities

Compliance-as-code for SOC 2, ISO 27001, FedRAMP, and CMMC
Security controls for CI/CD and ML pipelines
AI risk assessment and governance
Zero-trust network architecture
Vulnerability management and security hardening
Air-gapped and disconnected environment deployments

Typical Engagement Flow

We typically begin with an assessment, move into implementation, and then provide ongoing support as needed.

1One-Time

Security Assessment

Review your security posture, identify control gaps, and prioritize the improvements needed to strengthen your cloud and AI environments.

Starting at $6,500

Start Assessment

2Project-Based

Security & Compliance Implementation

Implement the security controls, policy changes, and infrastructure hardening needed to close gaps and meet compliance requirements.

Custom scoped

Scoped after assessment

3Recurring

Managed Security Operations

Provide ongoing security monitoring, vulnerability management, and compliance support to keep your environment secure and audit-ready over time.

Custom scoped

Available after delivery

Some clients start with an assessment only, but most continue into implementation and, where needed, ongoing support.

Secure Your Infrastructure

Cloud Modernization for AI Workloads

Modernize cloud environments for AI readiness by upgrading infrastructure, refactoring platforms, and improving support for evolving workloads.

Legacy infrastructure is rarely built for the demands of modern AI workloads. We help organizations modernize cloud environments for AI readiness by containerizing applications, refactoring platforms for GPU-accelerated workloads, and building cloud-native architectures for training and inference at scale. Our approach combines automated migration tooling with hands-on engineering to improve performance and prepare infrastructure for long-term AI operations across AWS, Azure, and GCP.

Capabilities

AI-ready cloud modernization strategy and execution
Application containerization and Kubernetes adoption
Platform refactoring for GPU-accelerated environments
Cloud-native architecture for training and inference
Migration planning, tooling, and workload transition
Post-migration optimization and operational readiness

Typical Engagement Flow

We typically begin with an assessment, move into implementation, and then provide ongoing support as needed.

1One-Time

Modernization Assessment

Assess your current environment, identify modernization priorities, and define the right migration path for AI readiness.

Starting at $5,000

Start Assessment

2Project-Based

Migration & Modernization Implementation

Implement the modernization plan by refactoring platforms, migrating workloads, and building cloud-native infrastructure for AI readiness.

Custom scoped

Scoped after assessment

3Recurring

Post-Migration Support

Provide ongoing optimization and operational support after modernization, improving performance, reliability, and scalability as workloads grow.

Custom scoped

Available after delivery

Some clients start with an assessment only, but most continue into implementation and, where needed, ongoing support.

Modernize Your Infrastructure

Ready to scope an engagement?

Begin with an assessment, or start with a free AI infrastructure audit.

Start Assessment Get a Free AI Infra Audit