Enterprise AI platform engineering
From AI experimentationto enterprise production.
XpertiQ engineers the platform foundations that let enterprises run AI workloads securely, reliably and at a defensible cost.
AI moves fast.Enterprise systems have to keep up.
Building AI is getting easier. Running it across an enterprise is not.
As AI adoption grows, infrastructure, data, identity, security, governance and operations become part of the same architecture problem.
XpertiQ works where these systems meet.
What we do
Architecture and engineering across the enterprise AI landscape.
XpertiQ works across enterprise AI environments, from assessing existing architectures to designing, engineering and evolving the foundations required for production.
Engage XpertiQ where the problem is.
When AI initiatives have grown faster than the enterprise architecture around them.
01 / Assess
Understand what exists before deciding what comes next.
Assess existing AI workloads, architectures and platform foundations to identify structural risks, fragmentation and opportunities for rationalization.
Architecture · Infrastructure · AI workloads · Data & knowledge · Identity · Security · Operations · Cost
Also relevant when AI systems must be inventoried, classified and documented ahead of a regulatory deadline.
Where we work
Where enterprise AI meets enterprise systems.
Six domains that decide whether AI workloads reach production.
Model access, inference, AI gateways, agents, tool execution, private AI.
Azure AI Foundry · Amazon Bedrock · Google Vertex AI · vLLM · Ray Serve · NVIDIA Triton and NIM · LiteLLM · Ollama
Technology is part of the architecture, not the starting point.
Our view
Enterprise AI is a systems problem.
Individual AI workloads can succeed independently. At enterprise scale, their dependencies become interconnected. Production AI depends on more than models. These layers must work as one enterprise system.
Applications · Copilots · Agents · Automation
Models · Inference · Agents · Tool execution
Enterprise data · Search · Retrieval · Knowledge
Cloud · Kubernetes · Hybrid · Network · Identity
Security · Observability · Reliability · FinOps
Our architecture question
What should be shared, and what should remain workload owned?
This is an architecture decision, not a technology decision.
As AI adoption grows, enterprises must decide where shared foundations create value and where workload autonomy should be preserved. The right boundary depends on enterprise requirements, constraints and existing systems.
Typically in scope, shared side
- Identity, access and secrets
- Network paths and egress control
- Model access and gateway policy
- Observability, cost and audit trails
- Compliance and governance guardrails
Typically in scope, workload side
- Domain data and retrieval logic
- Prompt, agent and tool design
- Evaluation criteria and quality gates
- Release cadence and rollback rules
- Business logic and user experience
Where each capability belongs is decided per enterprise, not by a template.
Our independence
Technology follows architecture.
Enterprise AI rarely lives in one platform, one cloud or one technology ecosystem. XpertiQ evaluates technologies according to the architecture they need to serve, not the other way around.
Our engineering standards
What we will not compromise on.
01
Production boundaries before production access.
Identity, authorization and execution boundaries must exist before AI workloads interact with real enterprise systems.
02
Evidence before assumptions.
Architecture decisions are based on systems, code, pipelines and the teams that operate them, not on declarations alone.
03
Foundations, not business applications.
We engineer the enterprise foundations AI workloads run on. We do not build the business AI applications themselves.
Approach
Architecture decisions backed by engineering.
Understand
Context and constraints.
Challenge
Test assumptions.
Decide
Make trade-offs explicit.
Engineer
Build the foundations.
Validate
Prove production readiness.
Transfer
Hand over ownership.
About
Built for the infrastructure behind enterprise AI.
XpertiQ is an independent architecture and engineering company specializing in Enterprise AI Platform Engineering.
Our roots are in enterprise architecture, cloud, Kubernetes, platform engineering, security and observability. As AI moves from experimentation into core enterprise systems, these disciplines become fundamental.
Independent. Engineering-led. Enterprise-first.
Based in France. Engagements across France and Europe.
Build the foundations for production AI.
Whether the challenge is architecture, platform engineering or an existing production environment, start with the problem that needs to be solved.