Content Strategy · IA · AI Systems · UX

Designing clarity across complex systems

Content & Information Architecture Lead with 8+ years designing AI-native operations, knowledge systems, and content workflows across enterprise, SaaS, fintech, and digital products. I help organizations build institutional memory that scales — turning messy, fragmented knowledge into governed, findable, AI-ready infrastructure.

Information Architecture AI Content Systems Content Governance Knowledge Management UX Writing Conversational Design
Languages Spanish (Native) English (C1) French (B2) Italian (B1) Portuguese (A2)
"I design clarity at scale."

I specialize in ambiguous environments where content challenges reveal deeper structural issues — aligning teams, simplifying decision-making, and building systems that scale across languages, platforms, and organizations.

Available for global remote engagements — enterprise transformation, product content strategy, AI governance, and knowledge systems.

Large-scale Enterprise content systems serving global workforces
60–80% Content volume reduction through modular design
50K+ Monthly search queries analyzed with AI
90% Error reduction via improved content workflows
8+ Years across enterprise, SaaS, fintech & AI
Core Capabilities
01

Information Architecture

Designing taxonomy, metadata, and navigation systems that scale across complex ecosystems.

02

AI-Enabled Content Ops

Building prompt libraries, evaluation frameworks, and AI workflows that improve quality and consistency.

03

Content Governance

Creating scalable standards that enable consistency without bottlenecks — across teams and time zones.

04

Organizational Alignment

Translating complex systems into clear frameworks that help leadership and teams make better decisions.

Featured Work

01
AI Operations · Knowledge Systems · Enterprise
Building AI-Native Operations & Institutional Memory at Enterprise Scale
AI Governance Taxonomy & Metadata Knowledge Systems +2 more
60–80% content reduction  ·  50K+ monthly queries analyzed
+
Context

A large enterprise organization needed to build institutional memory at scale — a governed, searchable, AI-ready knowledge layer across a complex digital workplace migration affecting tens of thousands of employees. The challenge wasn't the technology rollout. It was making sure the knowledge infrastructure behind it actually worked: findable, modular, consistently governed, and usable by both humans and AI systems.

Problem

Existing knowledge content ranged from 400 to 12,000 words per article — inconsistent, hard to navigate, and expensive to maintain. Support demand was high, content ownership was fragmented, and there were no shared standards to govern AI-assisted content production at scale.

The core problem wasn't adoption — it was missing operational infrastructure. Prompts, knowledge bases, and capture routines only work when the underlying content is modular, governed, and structured for AI consumption. Without that, even the best rollout generates confusion at scale.
My Role

UX Content Lead, AI & Product. Led end-to-end content strategy and adoption communications. Partnered with leadership, IT, and operational teams to align communication, governance, and employee experience strategy across the transformation program.

Strategy

Defined an operational Definition of Done for knowledge content — transforming 400–12,000-word articles into modular, AI-ready units. Designed taxonomy, metadata, and IA standards to enable automation and content reuse at scale. Analyzed 50K+ monthly search queries using AI-assisted clustering and intent modeling to surface gaps and fix findability. Built AI-native capture routines: prompt libraries, output evaluation frameworks, and review guardrails that caught wrong-voiced or thin content before it reached users.

60–80%Content volume reduction
EnterpriseScale taxonomy & metadata system
50K+Monthly queries analyzed
Organizational Impact ★
Reduced content volume by 60–80% while improving clarity, findability, and reuse
Established enterprise-wide taxonomy and metadata standards serving tens of thousands of employees
Built AI-enabled content ops model across research, design, and validation workflows
Improved search success and reduced support demand through intent-based IA redesign
Standardized responsible AI adoption with governance guardrails and prompt libraries
Enabled scalable content reuse and automation across knowledge systems

Enterprise transformation reveals that content is infrastructure. When structure, governance, and AI workflows align, organizations stop producing confusion at scale — and start delivering clarity instead.

02
AI Operations · Account Management · Product
AI Product Operations & Account Management for a Conversational Storytelling Platform
Account Management Product Operations Problem Diagnosis +1 more
70% engagement increase  ·  90% reduction in operational errors
+
Context

An early-stage AI platform guiding users through complex storytelling processes needed someone to own the client relationship and keep product operations running. Requests arrived messy and underspecified; AI-assisted delivery had to stay consistent and reliable as the product evolved.

Problem

There was no structured operational layer between clients and the product. Requests came in vague, delivery was inconsistent, and AI-assisted outputs drifted in quality. Without clear diagnosis, capture routines, and review cadences, problems repeated and operational errors compounded.

Most client requests don't arrive with clear specs. The real work was diagnosis — translating "the client said X" into what actually needed to happen, then making it real across the product, tools, and AI workflows.
My Role

Key Account Manager & Product Operations. Served as first point of contact for client needs — taking in requests, diagnosing what was actually needed, and translating that into operational fixes. Kept AI-assisted delivery running: monitored output quality, fed corrections back into workflows, and documented procedures so the team could operate consistently. Coordinated cross-functional work across product and editorial.

Strategy

Established a diagnosis-first intake: clarified underspecified requests before acting. Built capture routines and review cadences to keep AI-assisted output consistent. Created correction workflows that fed fixes back into the system. Documented operating procedures so quality didn't depend on any one person.

70%Increase in user engagement
90%Reduction in content & operational errors
Organizational Impact ★
Increased engagement by up to 70% by improving how client needs were diagnosed and delivered
Reduced operational errors by 90% through quality monitoring and correction workflows
Built the operational layer — intake, review cadences, and correction loops — that kept AI-assisted delivery reliable
Documented procedures and reference materials that let the team operate independently

This role proved that AI operations is mostly about diagnosis and follow-through. The model was fine. What clients needed was someone to translate messy requests into clear outcomes and keep the system running reliably.

03
Institutional Memory · Knowledge Stewardship · Ops
Stewarding Institutional Memory: Diagnosing & Stabilizing a High-Risk Knowledge System
Dependency Mapping Risk Prioritization Content Infrastructure
Reactive fixes → proactive stewardship  ·  Eliminated recurring failure patterns
+
Context

An organization's institutional memory layer — its knowledge base — had grown into a fragile, high-dependency system. Hidden relationships between articles meant one change could silently break multiple downstream resources. Fixes were reactive, underprioritized, and repeated. Nobody owned the drift.

Institutional memory doesn't maintain itself. Treating the knowledge base as operational infrastructure — with the same staleness, gap, and drift monitoring you'd apply to any critical system — was the reframe that changed everything.
Strategy

Mapped dependency chains across the knowledge base. Introduced risk-based remediation prioritization. Reframed content as operational infrastructure with lifecycle ownership responsibilities.

Organizational Impact ★
Identified and resolved hidden dependencies before they cascaded into user-facing failures
Improved institutional memory reliability — less drift, less staleness, fewer recurring gaps
Established ownership and monitoring cadences so drift was caught before it compounded
Shifted the team from reactive fixes to proactive knowledge stewardship
04
Fintech · Payments · Multi-market
Openpay / BBVA — Payment UX, Knowledge Systems & Conversational IA
KMS & Help Center IA Chatbot Flows Voice & Tone +2 more
3 years · multi-market  ·  KMS + chatbot + Design System integration
+
Context

A major payments platform operating across multiple international markets needed a unified content system spanning its Knowledge Management System, Help Center, onboarding flows, chatbot, and Design System — all in a multi-lingual, multi-regulatory environment.

Problem

Content was siloed across products and markets. The preauthorization flow caused payment disputes and abandonment. Chatbot resolution times were high. Voice and tone were inconsistently applied across the platform.

A payments product is only as trustworthy as the clarity of its content. Every ambiguous label, missing state, or inconsistent tone is a potential dispute, drop-off, or support ticket.
My Role

UX Content Specialist for 3 years across the full product ecosystem. Designed KMS and Help Center IA (taxonomy, navigation, content models). Structured onboarding IA and KYC flows. Created conversational IA and chatbot flows. Integrated voice and tone into the Design System. Led plain language and behavioral economics workshops across cross-functional teams.

Strategy

Built a tone-shift model by flow moment (preauthorization, incidentals, checkout). Structured each screen around the user's actual mental model. Designed chatbot flows to reduce resolution time. Embedded voice and tone as a Design System component — not a one-off guideline.

Organizational Impact ★
Reduced payment-related disputes and checkout abandonment
Improved self-service and internal alignment across multiple markets
Reduced chatbot resolution time and improved satisfaction scores
Embedded consistent voice & tone into the product Design System
Improved KYC completion rates through IA and content restructuring

Three years across a multi-market payments ecosystem proved that content systems — not individual copy — are what create trustworthy product experiences at scale.

Content challenges often reveal structural misalignment.
The work is never just the words.

Certifications & Skills

AI Product Management (IBM) Scrum Advanced (2025) Behavioral Economics (2024) NLP / Dialogflow UX Writing HTML / CSS / JavaScript Conversational Design Business Analytics Amazon Ads Diploma in Journalism Diploma in Poetry

Languages

Spanish Native
English C1 — Advanced
French B2 — Upper Intermediate
Italian B1 — Intermediate
Portuguese A2 — Elementary

Also a Writing Coach

For the past 10 years, I've guided people through the full writing process — from blank page to finished work. My core mission is to help everyone write better, clearer, more human content.

I bring the same systems-level thinking to individual writers that I apply to product ecosystems — helping you find structure, voice, and clarity at every scale.

What I bring to coaching

Whether you're navigating a blank page or a complex content system, the challenge is often the same: finding the structure that lets the right ideas come through.

Full writing process guidance
Clarity and structure at every stage
Voice development and authenticity
Human-centered content creation