Secure AI Infrastructure & AI-Powered Applications
We design and deploy secure AI infrastructure—private LLM deployments, API-based integration, cloud model hosting, and logging and monitoring—so you can run AI-powered applications and features in production with confidence.
What we deliver
- • Private LLM deployments in your cloud
- • API-based AI integration with your apps
- • Cloud-based model hosting (AWS, Azure, GCP)
- • Logging and monitoring for AI usage and cost
- • AI chat interfaces, recommendations, and custom LLM integrations
Production AI systems, not AI demos
We focus on deployable, secure, and maintainable AI systems inside your cloud—with clear use cases, model choices, and operational controls.
AI use case design
- •Clear success criteria and scope
- •Data requirements and availability
- •Integration points with existing systems
- •Boundaries and guardrails for AI behavior
Model selection strategy
- •Off-the-shelf vs fine-tuned vs custom
- •Latency, cost, and accuracy trade-offs
- •Vendor and API choices (e.g. OpenAI, Azure OpenAI, Bedrock, Vertex)
- •Fallback and degradation behavior
Secure API integration
- •API keys and credentials in secrets management
- •Network isolation and private endpoints
- •Rate limiting and abuse prevention
- •API versioning and compatibility
Data access controls
- •Least-privilege access to data sources
- •No training on sensitive data unless agreed
- •Data residency and retention alignment
- •Audit of what data AI can access
Logging & monitoring of AI usage
- •Request/response logging for audit and debugging
- •Token and cost usage visibility
- •Error and latency metrics
- •Alerts for anomalies or policy breaches
Cost control for AI workloads
- •Model and tier selection for cost
- •Caching and batching where appropriate
- •Budget and quota guardrails
- •Ongoing cost review and optimization
Deployment inside existing cloud
- •Deploy in your AWS, Azure, or GCP account
- •Use your identity and networking
- •Integrate with your CI/CD and pipelines
- •Handover and runbooks for your team
Engineering principles
Infrastructure is code, not clicks — declarative, version-controlled, reviewable.
Automation over manual processes — repeatable pipelines and patterns.
Least-privilege by default — access scoped to what is required.
Observability as a first-class concern — metrics, logs, and alerts from day one.
Cost awareness at design time — right-sizing and lifecycle built into architecture.
Secure-by-design architecture — security and governance embedded, not bolted on.
Tooling & stack
We use tools we know and that fit your environment. No exaggeration; we list what we use.
Cloud platforms
- AWS
- Azure
- GCP
Automation
- GitHub
- Octopus Deploy
- CI/CD pipelines
Infrastructure
- IaC (Terraform, Bicep, CloudFormation)
- Containers (Docker, Kubernetes where used)
- Version control (Git)
Monitoring
- Metrics and dashboards
- Centralized logging
- Alerting and on-call tooling
AI (when applicable)
- Model integration and APIs
- Cloud-hosted inference
- API-driven AI systems
Implementation methodology
We follow a structured, outcome-focused approach: discovery and scope, design and review, implementation in iterations, and handover with documentation and knowledge transfer. Delivery is phased so you have visibility at each step.
Deliverables
Concrete outputs you receive so delivery is tangible and reviewable.
- Use-case and architecture document
- Model selection and API integration design
- Deployed AI endpoints or apps in your cloud
- Access control and data governance documentation
- Monitoring dashboards and alerting for AI usage
- Cost visibility and optimization recommendations
- Runbooks and handover session
Engagement model
We offer AI Readiness Assessment, Pilot Deployment, and full AI Platform Build. See engagement packages on the main AI Solutions page.
View AI engagement options →Ideal clients
- •Teams that need AI deployed inside their own cloud with clear security and governance.
- •Businesses with defined use cases (internal assistants, automation, apps) and data in place.
- •Organizations that want production AI systems, not one-off demos.
- •Engineering teams that need integration with existing tools and CI/CD.
Scope and boundaries
Clear scope builds credibility. We are explicit about what we do and what we do not do.
We focus on
- ✓Cloud platform engineering (AWS, Azure, GCP)
- ✓DevOps and CI/CD automation (e.g. GitHub, Octopus Deploy)
- ✓FinOps and cost engineering
- ✓Reliability, observability, and SRE practices
- ✓Security and governance (IAM, policy, audit)
- ✓AI systems integration and production AI deployment
We do not
- ✕Resell or bundle random SaaS tools
- ✕Build generic marketing or WordPress sites
- ✕Provide unmanaged outsourcing or body-shop staffing
- ✕Claim certifications or metrics we cannot substantiate
- ✕Deliver infrastructure as one-off clicks without code or documentation
FAQ
Ready to run AI in production?
Talk to us about your infrastructure and app needs. We'll help you deploy AI securely in your cloud.
One membership, full stack — View plans & membership