AI pilots without governance
Agents and copilots need secure data access, policy boundaries and audit trails before production use.
Agentic AI | Platform engineering
AI is only as good as the platform it runs on.
A model in a notebook is a demo. AI in production needs deployment pipelines, secrets management, scaling, monitoring and rollback — the same platform discipline as any critical system, plus a few AI-specific concerns like prompt/version control and cost per call. We build that layer so your AI doesn't fall over the first time real users hit it.
Plan your platform
Architecture
flowchart LR
DEV[Commit] --> BUILD[Build]
BUILD --> TEST[Automated Tests]
TEST --> SCAN[Security Scan]
SCAN --> GATE{Quality Gate}
GATE -->|pass| STG[Staging]
GATE -->|fail| DEV
STG --> APPR{Approval}
APPR -->|yes| PROD[Production]
PROD --> ROLL[Auto rollback on error]
Business challenges
AI and platform engineering challenges we solve.
Every recommendation starts with business pressure, technical risk and the operating model required after launch.
Slow delivery paths
Manual environments and inconsistent pipelines create drift, delays and reliability issues.
Disconnected operations
Teams need observability, runbooks and ownership models alongside automation.
Solution overview
Give teams a faster, safer path from AI idea to production.
We build reusable infrastructure, standardized pipelines, governed AI access and integrated observability so teams can ship with confidence. AI work is treated like production software: secured, monitored and accountable.
Agentic AI
AI strategy, readiness, governance, agents, multi-agent workflows and production guardrails.
Microsoft AI
Microsoft AI Foundry, Azure OpenAI, Copilot Studio, Teams AI and prompt engineering.
MCP development
MCP servers, secure tool integration, custom connectors and enterprise system access patterns.
Platform engineering
Terraform, Kubernetes, CI/CD, golden paths, observability and reusable deployment workflows.
Data and AI foundations
RAG patterns, vector search, knowledge bases, analytics and governed data access.
AI automation
Workflow automation that connects securely to business tools, cloud services and operational systems.
Architecture model
A practical delivery architecture before implementation begins.
We define the target operating model, controls, integration points and ownership path before building, so the solution can be supported after launch.
CloudevTech
Enterprise delivery model
01
Readiness and governance
02
Secure tool access
03
Build platform
04
Automate delivery
05
Monitor and improve
Our approach
Structured delivery from discovery to operational handover.
Every engagement is shaped around the service goal, current constraints and the operating model your team needs after launch.
Standardize the path
Create a clear golden path for environments, pipelines and releases.
Secure the platform
Build identity, secrets, policy and logging into the platform foundation.
Measure reliability
Define useful operational signals and dashboards before incidents happen.
Govern AI access
Expose tools through controlled APIs and auditable permissions.
Business benefits
Outcomes designed for decision makers and delivery teams.
Benefits are framed around measurable improvement, operating confidence and reduced delivery risk.
Production-safe AI
MCP, OAuth, logging and controlled tool access reduce AI-agent blast radius.
Repeatable releases
Golden paths, CI/CD and IaC reduce manual work and deployment risk.
Engineering enablement
Documentation and runbooks help teams operate and extend what is built.
Technology stack
Implemented with proven platforms and tools.
Technology choices are confirmed during discovery, with a preference for reliable, maintainable platforms your team can support.
Microsoft AI Foundry
Azure OpenAI
Copilot Studio
MCP
Terraform
Kubernetes
GitHub Actions
Azure DevOps
GKE
OAuth 2.0
Enterprise AI agents needed access to production tools. Security needed to sleep at night.
Controlled AI-agent tool access with least-privilege scoping, mediated APIs and BigQuery audit logging.
Read the story ->
FAQ
Common questions before engagement.
Short answers to common planning questions for AI & Platform Engineering.
Is this different from normal DevOps?
Mostly it's DevOps done well, with AI-specific additions: model/prompt versioning, token-cost monitoring, and guardrails on what the AI can trigger.
Do you need our data scientists?
We work alongside them. They own the model; we own making it run reliably and safely.
Enterprise consultation
Book a consultation