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  • Joined Sep 8, 2026

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Jlopez-nava/README.md

Hi, I鈥檓 Juan 馃憢

I build AI-powered marketing systems that turn scattered data into clearer growth decisions.

My work sits at the intersection of growth strategy, lifecycle marketing, paid acquisition, and practical AI automation. I care less about adding AI for its own sake and more about building tools that help a team answer: What should we do next, why, and what evidence supports it?

Featured work

An evidence-backed audit system for B2B SaaS and technology companies. It reviews website positioning, conversion paths, SEO signals, and competitive context鈥攖hen turns the evidence into a prioritized executive brief. A fictional lifecycle concept shows how the framework can extend across the customer journey.

What it demonstrates: growth strategy, lifecycle thinking, competitive intelligence, structured AI analysis, and safety-conscious browser automation.

A read-only decision dashboard that turns a fictional Google Ads snapshot into prioritized opportunities across spend, search terms, budget, and visibility鈥攚hile keeping every next step human-owned.

What it demonstrates: paid-search strategy, marketing analytics, decision dashboards, and responsible automation design.

A Profound workflow that detects competitive movement in AI search visibility, traces changes to citation-earning pages, and converts the evidence into weekly analysis, alerts, and actionable content briefs.

What it demonstrates: AI-search strategy, competitive intelligence, content operations, evidence-grounded agent design, and cross-tool workflow automation.

A Profound workflow that watches a competitor's citation traction and positive validation, applies a decision threshold, and turns qualified signals into sourced content and sales-enablement drafts for human review.

What it demonstrates: signal-based automation, AI-search competitive intelligence, evidence-led content strategy, cross-functional workflow design, and responsible human-in-the-loop execution.

How I approach AI + marketing

  • Start with the business decision, not the model.
  • Separate observed evidence from hypotheses and recommendations.
  • Build guardrails around anything that can change spend or customer experiences.
  • Design the output for the person who needs to act on it.
  • Treat privacy, credentials, and customer data as product requirements.

Focus areas

Growth strategy 路 Lifecycle & CRM 路 Paid search 路 CRO 路 Competitive intelligence 路 AI workflows 路 Marketing analytics


I鈥檓 continuing to turn real marketing workflows into focused, explainable AI tools. The repositories below document both the product thinking and the implementation behind them.

Pinned Loading

  1. google-ads-growth-analyst google-ads-growth-analyst Public

    Read-only Google Ads decision dashboard built with fictional data for marketer-first analysis.

    TypeScript

  2. ai-growth-intelligence-auditor ai-growth-intelligence-auditor Public

    Evidence-first AI website and competitive growth auditor with a fictional lifecycle concept.

    TypeScript

  3. ai-visibility-competitor-monitor ai-visibility-competitor-monitor Public

    A Profound workflow that detects competitive AI-visibility shifts and turns citation evidence into content briefs, Google Docs, and Slack alerts.

  4. ai-competitive-content-response-agent ai-competitive-content-response-agent Public

    A Profound workflow that turns meaningful competitor AI-citation signals into sourced content and sales response drafts.