AI Operations

Enterprise AI Analytics Platform

Replaced manual cross-platform reporting for 40+ users, cutting 300+ hours of work each week.

My role

I came up with the concept, designed the system, built the first version, and led the rollout.

Collaboration

The Head of Data Engineering and I scaled it and took it to production.

Claude CodeAI AgentsShared ContextAutomated ValidationMCP

Problem

Teams relied on manual, platform-by-platform report pulls to answer recurring business questions. The work was slow, duplicated across departments, and dependent on a small number of people who knew where the data lived and how to interpret it.

Solution

I built a self-service analytics platform with reusable agent workflows, shared company context, connected analysis tools, and automated checks. I built the first version, then worked with the Head of Data Engineering to scale it for production.

Outcome

Measured

Cut 300+ hours of manual reporting each week across 40+ users.

How it works

🧩

Encode

Packages repeatable analyses as agent workflows

🧠

Ground

Adds company definitions, metric logic, and operating context

🔗

Connect

Links the data and tools each analysis needs

Check

Tests outputs before users see them

Answer

Returns analysis in the team's existing tools