Intelligent Extraction

How ATLAS transforms raw content into structured knowledge

Beyond Simple Storage

Most tools just store your content. ATLAS understands it.

When you add content to ATLAS, AI models analyze every piece to extract structured knowledge that's searchable, connectable, and actionable.

---

What Gets Extracted

📌 Concepts

Key ideas with importance scores

Every piece of content yields concepts — the fundamental ideas it contains. Each concept includes:

Field	Description
Name	Clear, descriptive identifier
Description	What this concept means
Importance	Score from 0.0 to 1.0
Category	Domain classification
Source	Where it came from

Example:

Name: "Antifragility"
Description: "Systems that gain from disorder and volatility"
Importance: 0.92
Category: Systems Thinking
Source: Twitter bookmark on Taleb thread

💡 Insights

Actionable learnings and observations

Not just facts, but implications. What should you remember? What matters?

Example:

"Most productivity systems fail because they optimize 
for capture rather than retrieval. Design for finding, 
not filing."

✅ Actions

Things to do, research, or explore

Your content often implies next steps. ATLAS captures them:

Example:

- Research: "Look into Zettelkasten methodology"
- Build: "Create a personal knowledge API"  
- Connect: "Reach out to author for interview"

📝 Quotes

Memorable passages worth preserving

The exact words that captured an idea perfectly.

🧩 Frameworks

Mental models and thinking tools

Structured approaches to problems. Decision matrices. Evaluation criteria. Reusable thinking patterns.

---

The Extraction Process

Step 1: Content Analysis

LLM receives the full content with extraction prompts optimized for each content type (tweets vs. long articles vs. conversations).

Step 2: Structured Output

AI returns JSON-structured extractions that map to ATLAS's schema. No fuzzy outputs — clean, queryable data.

Step 3: Importance Scoring

Each concept gets an importance score based on:

• Relevance to your existing knowledge graph

• Novelty of the idea

• Actionability

• Connection potential

Step 4: Deduplication

If a concept already exists in your graph, ATLAS merges the new reference rather than creating duplicates.

Step 5: Relationship Mapping

New concepts automatically link to related existing concepts based on semantic similarity and co-occurrence.

---

Importance Scoring

The 0.0 to 1.0 importance score is crucial for surfacing what matters.

Scoring Factors

Factor	Weight	Description
Novelty	25%	Is this new to your graph?
Density	25%	How connected to other concepts?
Actionability	20%	Does it suggest concrete actions?
Source Quality	15%	Where did this come from?
Recency	15%	Is this timely?

What Scores Mean

• 0.9 - 1.0: Core concepts, foundational ideas

• 0.7 - 0.9: Important concepts, frequent references

• 0.5 - 0.7: Useful supporting concepts

• 0.3 - 0.5: Contextual, situational concepts

• 0.0 - 0.3: Peripheral, low-signal concepts

---

Current Stats

Extracted Item	Count
Concepts	12,768+
Insights	5,691+
Actions	4,694+
Relationships	9,807+
Content Items	5,731+

---

The Compounding Effect

Here's what makes intelligent extraction powerful:

Month 1

You add your Twitter bookmarks. ATLAS extracts 500 concepts.

Month 3

You add Apple Notes and GitHub stars. ATLAS extracts 1,000 more concepts — but 300 of them connect to existing ones, enriching both.

Month 6

Your graph has 3,000 concepts with 8,000 relationships. New content immediately slots into context.

Month 12

ATLAS knows your intellectual landscape. It can answer questions about your specific interests, connect disparate ideas, and surface forgotten insights at the right moment.

Knowledge compounds. Storage doesn't.