Practical Thinking for Enterprise Technology Leaders
Architecture, integration, and AI insights from the Co Valere team - grounded in real delivery experience, not analyst reports.
The "Cost Per Capability" Framework for AI
Stop measuring AI by model cost. Start measuring it by capability cost.
FinOps Patterns for AI Agents, RAG, and Domain Models
AI cost doesn't explode because models are big. It explodes because patterns aren't controlled.
How to Govern Multi-Model AI Ecosystems
Twenty models is where EA becomes essential.
You Can't Automate What You Can't Understand
For years, enterprise data strategies focused on lakes, warehouses, and pipelines.
What is Enterprise MCP and Why It Matters for AI-Ready Integration
Model Context Protocol is rapidly becoming the standard for connecting AI agents to enterprise systems. We explain what it is, why it matters, and how to implement it securely.
AI Innovation is Loud. AI Risk is Quiet.
AI innovation is loud. But AI risk is quiet.
Digital Twins + AI Agents: From Observing Systems to Acting Within Them
Digital twins were designed to observe.
Agent-Level Unit Economics: Measuring Cost per Decision, Not per Workload
Traditional FinOps measures infrastructure:
Embedding FinOps into Autonomous Decision-Making
AI agents are increasingly trusted to act autonomously - approving transactions, triggering workflows, scaling infrastructure, and interacting with external services.
The "Cost Per Capability" Framework for AI
Most enterprises still measure AI the old way: cost per model, cost per token, cost per workload.
The "Cost Per Capability" Framework for AI: The New FinOps Mindset Enterprises Need
For years, enterprises have measured AI the wrong way.
Designing an Agentic AI Operating Model for the Enterprise
Deploying AI agents in an enterprise requires a governance model, an operating framework, and clear ownership. Here's how to design one.
FinOps for Multi-Model AI
AI isn't expensive. Unmanaged AI is.
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