Consulting Service
GenAI Infrastructure
Build the infrastructure, deployment patterns, observability, and controls needed to run GenAI applications in production
We help teams turn GenAI prototypes into systems that can be deployed, monitored, secured, and maintained. The focus is infrastructure around the application: runtime architecture, API boundaries, secrets, retrieval components, evaluation hooks, observability, and cost guardrails.
This work is useful when an LLM or RAG prototype is valuable, but the surrounding platform is not ready for production traffic, team handoff, or ongoing operations.
When This Helps
Signs this service is worth prioritizing
Typical situations where external AI infrastructure, DevOps, and cloud support creates leverage quickly.
Teams with a GenAI prototype that needs a production deployment path
Product teams building LLM-backed applications, copilots, internal tools, or RAG systems
Organizations that need stronger governance, observability, and cost control around GenAI usage
Engineering leaders who want GenAI infrastructure to fit the existing cloud and DevOps model
Deliverables
What I would deliver
Clear consulting outputs instead of a vague capability list.
GenAI application infrastructure review and production-readiness assessment
Runtime architecture for LLM applications, RAG systems, workers, and supporting APIs
Secure exposure of OpenAI, Azure OpenAI, or other AI services through APIM or gateway layers
Infrastructure automation for environments, secrets, networking, and deployment boundaries
Vector store, document processing, and retrieval component integration patterns
Observability design for latency, errors, token usage, retrieval quality signals, and cost
Release workflow guidance for prompts, configuration, evaluation checks, and application changes
Engagement Model
How the work would run
Discover
Review your current architecture, delivery process, risks, and constraints before proposing changes.
Implement
Translate the plan into concrete architecture, automation, guardrails, and documentation.
Enable
Hand off the solution with operational context so your team can run it confidently.
Outcomes
What should improve
A clearer path from GenAI prototype to production system
Better reliability, security, and operational visibility around LLM-backed applications
Cost guardrails and usage signals before spend becomes difficult to explain
Infrastructure patterns that fit your existing cloud platform instead of creating a separate AI silo
Platforms
Tools and platforms
Technology is supporting evidence. The goal is a system your team can actually operate.
Adjacent Services
Related consulting areas
AI-Ready Cloud Architecture
Design cloud foundations that support AI workloads, scale cleanly, stay operable, and avoid expensive rework later
Learn moreMLOps Workflow
Create repeatable workflows for moving models, data checks, and inference services from development to production
Learn moreInfrastructure as Code
Automate and manage your cloud infrastructure using reusable, version-controlled code
Learn moreNext Step
Need help with GenAI Infrastructure?
If the constraints are already clear, the next useful step is a short technical conversation about scope, risks, and delivery approach.
Book a consultation