Notion knowledge base with contextual AI retrieval
How we migrated Animalz's 10-year-old knowledge base from legacy tools to a clean Notion workspace with contextual AI retrieval. 50-60% faster retrieval and an information architecture built to stay that way.
By the numbers
- 01+200
Documents migrated including different text formats, media, and internal hyperlinks
- 02100%
Knowledge base adoption by the team
- 033,600 h/year
Average hours saved located for basic information search by team member
- Industry: Marketing (Content Creation)
- Country: USA
- Users: +30
- Client Status: Implemented Q1 2026
- Notion AI: N/A
Animalz
We redesigned the information architecture and strategically migrated Animalz's entire knowledge base to Notion. What began as a technical migration request became a deep restructuring: we went from +200 fragmented documents with inconsistent tagging and near-impossible search, to a clean, intentional knowledge system adopted 100% by the team. Animalz is a leading company in intelligent content creation, SEO-optimized and audience-focused, operating from New York since 2015.
Starting Point
The initial request was a technical migration from Tettra to Notion. However, after the discovery call, we identified that the real problem wasn't just technical: the knowledge base had been built over years by multiple contributors without unified criteria. The result was duplicated content, inconsistent tags, and a system the team avoided using because finding information took too long.
Diagnosis: they didn't just need to move data — they needed a knowledge base the team would want to, and could, use every day.
Services Implemented
- Strategic Documentation Audit: Full audit of Tettra documents, identifying duplicates, information gaps, and critical pain points. Design of a new intent-based tagging system built around the team's real workflows.
- Technical Drop-in — Migration: Full migration of +200 documents from Tettra to Notion, including historical records, multimedia, and internal hyperlinks. Workspace preparation and content verification under the new organizational system.
- Quality Control Automations: Implementation of automated flows for change tracking, document ownership management, and internal content approval, with automatic notifications to responsible profiles.
- Team Training: Training on Notion 101 and use of the new knowledge base: how to create, maintain, search, and connect documentation with project management.
Tech Stack
- Notion
- Tettra
- Claude
- N8N
Highlighted Workflows
Intent-Based Knowledge Base
One of the main pain points was the difficulty of finding internal information for day-to-day work. The root cause: duplicated and inconsistent tagging that made search nearly impossible. We designed a new tagging system based on the team's actual search intent — not arbitrary categories.
Result: Intentional tagging system, time savings in organizing and searching internal content, structure that scales with team growth.
Automated Quality Control
We implemented automation systems that maintain trust in the documentation: clear ownership for each document, defined approval flows for new content with real criteria, and automatic notifications to the profiles responsible for validating information.
Result: The team has 100% confidence that internal documentation is up-to-date and validated by the right people.
Intentional Training
We didn't just migrate data — we prepared the team to maintain, scale, and trust their own system. Training covered creating new documentation, maintenance criteria, efficient search, and connecting knowledge base with project management in Notion.
Result: Clear expectations, team agreements on how to use and maintain the system, real adoption from day one.