CASE STUDY: Forma
Designed a scalable internal knowledge system using AI-assisted structuring to improve speed, clarity, and content reliability.
Role: Senior Content Strategist
Scope: Internal CMS, knowledge systems, IA, AI-assisted workflows
Timeframe: 4-mo contract
SNAPSHOT
My Approach
I treated this as a knowledge infrastructure problem.
Instead of organizing documents, I focused on:
how knowledge is structured for retrieval
how systems scale without duplication
how AI can support content organization and governance
The goal: create a usable, trustworthy knowledge system—not just storage.
The Problem
Forma’s internal knowledge ecosystem couldn’t keep up with company growth.
Teams struggled with:
scattered and duplicated information
lack of a reliable source of truth
slow access to critical knowledge
The system wasn’t designed for retrieval or scale—especially in a low-search environment.
What I Led
Designed and implemented a centralized internal CMS using Notion
Created structured information architecture and taxonomy for fast retrieval
Audited and de-duplicated content across teams
Built templates and governance systems for scalable content creation
Introduced AI-assisted workflows to support structuring and maintenance
Partnered cross-functionally to align system with real workflows
The Outcome
Faster access to internal knowledge across teams
Reduced duplication and content sprawl
Increased trust in internal documentation
Scalable knowledge system supporting growth and onboarding
Why It Matters
Most internal knowledge systems fail because they prioritize storage over usability. This work created a knowledge system optimized for retrieval and scale—laying the foundation for:
faster team execution
consistent decision-making
AI-assisted knowledge workflows
Core Skills Demonstrated
Knowledge Systems Design · Information Architecture · CMS Design · AI Content Workflows · Content Operations