Cloud & AI Engineering Services
We design and implement AWS infrastructure, CI/CD with GitHub and Octopus Deploy, AI integration, cost optimization, reliability, and security—with clear deliverables and handover.
Common gaps we close
Companies often struggle with: scale-up beyond pilots, digital maturity, technical capacity, strategy focus, and skills/governance. We help address these so cloud and AI deliver tangible value.
See where companies need AI →Technical scope
Outcome-focused workstreams: infrastructure, CI/CD, FinOps, observability, security, and production AI integration. Each with defined deliverables and outcomes.
AWS Cloud Infrastructure
Design and evolution of a stable, scalable AWS foundation that supports your products and teams.
What we deliver
- Architecture guidance for core AWS services and landing zones
- Design, provisioning, and scaling of cloud infrastructure
- EC2 and EBS patterns aligned to workload needs
- Networking foundations with VPC patterns, routing, and security groups
Outcomes
- Stable, scalable cloud infrastructure
- Faster, more predictable deployments
- Fewer production incidents and surprises
CI/CD & Release Automation
Modern, GitHub‑centric delivery pipelines that make shipping changes routine instead of risky.
What we deliver
- CI workflows built around GitHub Actions and your branching model
- Octopus Deploy release pipelines and promotion strategies
- Environment consistency across dev, test, staging, and prod
- Release automation that fits regulatory and change‑management needs
Outcomes
- Safer, more controlled releases
- Repeatable deployments across environments
- Reduced manual effort and deployment friction
Cost Optimization (FinOps)
Practical cloud cost optimization that keeps performance high while bringing AWS spend under control.
What we deliver
- Assessment to identify waste and right‑size workloads
- Storage and compute optimization including EBS lifecycle and EC2 sizing
- Budgeting, guardrails, and reporting tuned to your finance cadence
Outcomes
- Lower and more efficient monthly AWS spend
- Predictable cloud costs for finance and leadership
- Optimized use of cloud resources over time
Reliability & Observability
Monitoring, logging, and operational practices that keep your services healthy and your teams informed.
What we deliver
- Monitoring and alerting strategy aligned to business impact
- Centralized logging and operational dashboards
- Incident reduction through SLA/SLO‑driven best practices
Outcomes
- Faster detection of issues in production
- Quicker recovery when incidents do occur
- Improved uptime and customer experience
Security & Governance
Security and governance patterns that scale with your organization without slowing teams down.
What we deliver
- High‑level IAM best practices and access patterns
- Policy guardrails and compliance‑ready configuration baselines
- Secure deployment practices embedded into CI/CD pipelines
Outcomes
- Reduced security and compliance risk
- Controlled, auditable access across teams and accounts
- Pipelines that ship securely by default
AI Engineering & LLM Systems
Production-grade AI systems deployed inside your cloud: architecture, integration, and operations—not research or hype.
What we deliver
- Architecture and strategy for LLM systems and AI-assisted workflows
- Integration of models with your data, APIs, and internal applications
- Deployment of AI services in your AWS, Azure, or GCP accounts with CI/CD and observability
Outcomes
- AI that fits your existing cloud, security, and delivery practices
- Clear ownership and runbooks for AI workloads
- Predictable, governed AI usage instead of one-off demos
AI engineering as part of your platform
We do not build new foundation models. We engineer AI systems for production environments: secure, observable, and cost-aware AI workloads that live alongside your existing services.
Cloud Architecture & Platform Engineering
Structured design of accounts, networking, compute, and infrastructure-as-code so platforms are repeatable and maintainable.
Account & Environment Strategy
- •Multi-environment isolation (dev, test, staging, production)
- •Naming conventions and resource tagging standards
- •Access control patterns and boundary policies
Networking Design
- •Segmentation principles (VPC/VNet, subnets, security groups)
- •Routing strategy and traffic flow
- •Secure connectivity patterns (VPN, private link, peering)
Compute & Storage Strategy
- •Workload classification and placement
- •Scaling patterns (horizontal, vertical, scheduled)
- •Lifecycle management and retention policies
Infrastructure as Code
- •Declarative provisioning (Terraform, Bicep, CloudFormation)
- •Version-controlled infrastructure and change review
- •Environment reproducibility and drift detection
DevOps & CI/CD Engineering
Pipeline design, release governance, and artifact management with GitHub and Octopus Deploy where applicable.
Pipeline Design
- •Build, test, and package stages
- •Environment-specific deployment flows
- •Quality gates and approval steps
Branching & Promotion Strategy
- •Branch strategy aligned to release model
- •Promotion from non-prod to production
- •Feature flags and safe rollouts
Release Governance
- •Controlled release process
- •Audit trail for deployments
- •Change approval where required
Artifact Management
- •Container and package registries
- •Versioning and retention
- •Supply-chain and vulnerability scanning
Environment Parity
- •Consistent configuration across environments
- •Secrets and config management
- •Database and dependency alignment
Rollback & Recovery Strategy
- •Rollback procedures and runbooks
- •Data and state considerations
- •Post-rollback verification
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.
How we work
A structured five-phase engagement so you know exactly how we operate and what to expect.
Discovery & Architecture Planning
- •Understand current environment and constraints
- •Review goals and success criteria
- •Define scope and success metrics
Secure Access Setup
- •Role-based access configuration
- •Time-bound permissions
- •Least-privilege model
- •Activity logging enabled
Architecture & Implementation
- •Infrastructure as Code
- •Pipeline-based deployments
- •Controlled environment promotion
Validation & Hardening
- •Security review
- •Cost review
- •Reliability validation
Handover & Ongoing Optimization
- •Documentation delivery
- •Knowledge transfer session
- •Continuous improvement model
Deliverables
Concrete outputs you receive so delivery is tangible and reviewable.
- Architecture diagrams (current and target state)
- Infrastructure repository (IaC: Terraform, Bicep, or CloudFormation as applicable)
- Pipeline configuration and deployment workflows
- Monitoring dashboard setup and alerting rules
- Security baseline and access model documentation
- Cost optimization report and prioritized action plan
- Operational runbooks and escalation paths
- Handover workshop and knowledge transfer session
Security & access model
We engage with client environments in a secure, professional, and enterprise-ready manner.
We do not
- ×We do not require root credentials.
- ×We do not use shared passwords.
We operate using
- •Role-based IAM access
- •Federated identity (SSO where available)
- •Auditable activity logging
- •Infrastructure-as-Code deployments
- •Pipeline-based execution
Access Control
- •Least privilege
- •Scoped permissions
- •Temporary elevation if required
Deployment Methodology
- •Version-controlled infrastructure
- •CI/CD-driven changes
- •Change visibility
Governance & Auditability
- •Logged access
- •Change traceability
- •Cost and usage monitoring
Engagement model
Project-based, retainer, or assessment and roadmap. We align to your timeline and team structure.
Cloud Assessment
1–2 weeksIncludes
- Current-state review of AWS, Azure, and/or GCP
- Risk and opportunity analysis
- Prioritized roadmap with quick wins and longer-term work
- Executive-friendly summary of key findings
Best for: Teams needing clarity on where to start.
Foundation Build
2–6 weeksIncludes
- Baseline AWS, Azure, and/or GCP landing zone
- Infrastructure as Code for core platform
- Initial CI/CD pipelines wired to environments
- Monitoring, alerting, and security guardrails
Best for: Teams building or standardizing a cloud platform.
Optimization & Operations
OngoingIncludes
- Regular cost optimization and FinOps reviews
- Reliability and incident reduction initiatives
- Support for platform changes and improvements
- Advisory support for roadmap and architecture decisions
Best for: Teams investing in continuous improvement.
Ideal clients
- •Teams with existing AWS, Azure, or GCP usage who want to standardize and optimize.
- •Engineering organizations ready to adopt or mature IaC and CI/CD.
- •Leaders who need cost visibility, governance, and reliability without hype.
- •Companies that want hands-on engineering delivery and knowledge transfer.