Mitchell MillerClarityPulse
Personal AI reporting prototype: GA4, GSC, ads data → source-backed narratives
Representative mockup — not client dataPartners
Internal Agency Teams
Tools & Data
Python, LLM APIs, GA4/GSC APIs
Proof
Converted GA4, GSC, ads, and SEO data into KPI cards, source-backed narratives, risk queues, and export-ready weekly briefs.
Interview Angle
Use this story when the interviewer cares about scale, execution ownership, cross-functional alignment, or turning messy data into a shipped system.
Problem
Client reporting was manual, time-consuming, inconsistent; no single surface connected GA4, GSC, ads, SEO data into a narrative
System & Approach
Built internal AI product with modules: header (generated time, client, source freshness, QA state), KPI cards with raw values and deltas, executive narrative with evidence tags, trend chart, risk/action queue table, source health, export lock / low-confidence review state
Execution Pattern
This work required translating search and analytics signals into concrete requirements, aligning stakeholders who owned different parts of the system, and keeping the implementation tied to measurable business outcomes instead of isolated SEO tasks.
Outcome
Converted GA4, GSC, ads, and SEO data into KPI cards, source-backed narratives, risk queues, and export-ready weekly briefs.
What this shows hiring managers
Ability to prototype AI tools that solve real operational bottlenecks.