About
Meru AI is built around one premise: AI should return authority to human judgment.
Meru AI is led by Jose Okabe, an AI implementation strategist and enterprise systems architect focused on the operating layer where tools, people, data, and decisions meet.
Operating background
The work sits between strategy and the systems that carry the business.
Jose has designed and supported AI, automation, attribution, telecom, reporting, and operations systems inside environments where the work had to survive daily use. The focus is not AI as novelty. The focus is the operating record: what the business knows, how teams act on it, and where human review must remain.
Meru uses that background to help professional services and mid-market organizations identify where AI belongs, connect it to existing systems, and measure whether it changed a real business behavior.
View Jose Okabe on LinkedInEnterprise architecture
Reduced 1,000+ automations into 7 governed workflows while moving brittle operations toward AWS-centered infrastructure.
Revenue operations
Rebuilt attribution and signed-contract feedback loops across ad platforms, CRM, and executive reporting systems.
Legal operations
Designed AI-supported workflows for intake, call analysis, churn risk, document processing, and human review.
Adoption
Built operational systems used across a 350+ employee environment, with implementation work tied to daily workflows rather than isolated demos.
Related work
Proof that shows how the practice thinks.
Enterprise Legal AI Architecture
A case study on architecture, attribution, telecom, intake, finance, and workforce adoption inside a high-volume legal operation.
Open resourceSales Intake Automation
A guide to using AI around lead context, call review, follow-up, and revenue feedback loops.
Open resourceAI Consulting Services
The commercial service model for strategy, implementation, system integration, workflow automation, and stewardship.
Open resourceNext step
Start with the workflow that carries the most consequence.
Meru begins by locating the operating center before recommending tools, models, automations, or agents.