Stage 01
Map Vault Structure
Defined the vault hierarchy, source-of-truth rules, metadata expectations, protected areas, project states, and the boundaries between original notes and generated synthesis.
Output / Vault operating model
Back to portfolio
Draft case study2026
AI Knowledge System
NERO AI Second Brain.
Built NERO, an AI-assisted Obsidian second brain with 14 vault sections, five operating modes, protected notes, agent rules, and cross-note recall for real workflows.
ObsidianAI AgentsMarkdownKnowledge BaseAutomation
Explore the rebuild
Vault
14 major sections
Modes
5 operating modes
Core
Obsidian knowledge base
Rules
Agent instruction layer
The System Problem
A CRM rebuild starts by identifying the connected failures underneath the visible symptoms.
Root cause / 01
A note vault needed to behave like a system instead of storage
The vault already contained journals, project notes, work systems, people, health, prompt libraries, dashboards, and archives. The problem was retrieval and coordination: information existed, but finding the current source, connecting related notes, and distinguishing stale context still required manual effort.
NERO was designed as an operating layer over that vault. It needed to recall narrowly, protect original journal entries, respect chronology, detect contradictions, propose controlled writes, synchronize project context, and avoid turning personal notes into an AI-generated rewrite of the user.
Audit signals
What the system was telling us.
01
Fourteen vault sections required coordination
02
Five operating modes guided agent behavior
03
Journal originals required write protection
04
Temporal conflicts needed explicit handling
05
Project context crossed many note types
Rebuild Sequence
The work moved from diagnosis to architecture, implementation, and governance—in that order.
Stage 02
Define Agent Behavior
Created root agent instructions covering identity, retrieval, chronology, privacy, prompt injection, rollback, controlled writes, and reliable task-completion reporting.
Output / Agent instruction layer
Stage 03
Add Working Modes
Structured recall, organization, execution, analysis, and planning modes so the same vault could support retrieval, synthesis, project work, and controlled updates.
Output / Five operating modes
Stage 04
Test System Safety
Designed tests for cross-note retrieval, contradictions, journal protection, current-focus synthesis, project execution, and safe write behavior before broader use.
Output / System QA framework
Target System Architecture
Four connected layers turned the portal from a collection of tools into an operating system.
System spine
Target-state sequence
Good automation and reporting sit on top of a governed data and process model—not the other way around.
Layer 01
Knowledge Layer
Fourteen vault sections separate journals, projects, systems, people, prompts, archives, templates, and operational knowledge into clearly navigable working domains.
Layer 02
Retrieval Layer
Agent rules use narrow search, links, chronology, freshness, and contradiction checks to recover the most defensible context first.
Layer 03
Execution Layer
Operating modes turn retrieved context into planning, analysis, project execution, organization, and proposed updates without bypassing source rules.
Layer 04
Safety Layer
Protected journals, controlled writes, rollback expectations, privacy rules, and prompt-injection defenses preserve the vault as a trusted source system.
Qualitative Outcomes
The engagement focused on structural improvement, so the strongest results are clearer operations—not invented vanity percentages.
SystemBeforeAfter rebuild
Recall
Useful context was scattered across notes
Cross-note retrieval became structured
Chronology
Old and current context could conflict
Freshness rules became explicit
Journals
Source writing risked accidental rewriting
Original entries stayed protected
Execution
Notes mostly stored information passively
Vault supported active project work
What Was Delivered
A reusable case study should make the work tangible without exposing confidential client data.
Deliverable 01
Vault architecture map
Deliverable 02
Root agent instruction file
Deliverable 03
Five operating modes
Deliverable 04
Journal protection rules
Deliverable 05
Recall and contradiction workflow
Deliverable 06
System safety test plan
System note / final
Architecture before automation.
NERO reinforced that an AI second brain is mostly an information-governance problem wearing a cool name. Retrieval quality depends on source discipline, chronology, and write boundaries. The useful part is not having an agent that says more; it is having one that knows what not to overwrite.
Private journal content, personal records, connected files, and sensitive memory context are excluded entirely.