Intelix is an AI operating efficiency firm helping organizations reduce AI waste, deploy private intelligence stacks, and bring cost discipline to enterprise AI adoption.
We work with leadership teams to answer a simple but expensive question: where should each AI workload actually run? We model cloud, owned hardware, colocation, private models, frontier APIs, and human review against real workload economics, then help companies implement the routing, governance, and telemetry needed to keep AI spend under control.
Our work focuses on AI infrastructure TCO, model routing, context compression, spend visibility, private AI deployment, least privilege access, and governed human approval gates for sensitive or high impact workflows.
Core practice areas include:
• AI infrastructure and inference TCO audits
• Cloud versus owned versus colocation workload modeling
• Private intelligence stack design and implementation
• Small model routing for routine enterprise workflows
• Token spend reduction and chargeback design
• Governance, audit readiness, and human in the loop controls
• Sovereign, on premise, and industry specific AI deployment strategy
Intelix exists for companies that know AI is no longer an experiment, but also know that unmanaged AI spend, shadow tools, model overkill, and weak governance can quietly become the next cloud cost problem.