Documentation/Learning Marketplace

Learning Marketplace

The Cograph Learning Marketplace offers structured technical modules to help your team close the knowledge gaps the graph identifies. 36+ modules across engineering, infrastructure, data, and business skills.

What's included

Written Modules

Structured text content with code examples, diagrams, and reference sections. Read at your own pace.

Interactive Quizzes

Knowledge checks throughout each chapter with instant feedback and explanations.

Coding Challenges

Hands-on exercises with an in-browser editor and AI-powered feedback on your submissions.

Progress Tracking

Chapter completion, quiz scores, and time spent — visible to both the learner and their manager.

Module Categories

Browse over 36 modules across 6 categories. All modules are written by practitioners and updated regularly.

Backend8 modules
  • Node.js Deep Dive
  • GraphQL APIs
  • PostgreSQL Optimization
  • and more...
Infrastructure7 modules
  • AWS Cloud Practitioner
  • Docker & Containers
  • Kubernetes
  • and more...
Frontend5 modules
  • React 19
  • Next.js App Router
  • TypeScript for Production
  • and more...
Data & ML6 modules
  • SQL for Data Analysis
  • Machine Learning
  • Python Fundamentals
  • and more...
Security3 modules
  • Cybersecurity Foundations
  • GDPR Compliance
  • OAuth & Auth Patterns
  • and more...
Business7 modules
  • Technical Writing
  • Strategic Account Management
  • Brand & SEO
  • and more...

Module Preview

Every module follows the same structure — chapters with objectives, learning content, knowledge checks, and coding challenges. Here's an interactive sample from our Getting Started with Cograph module:

Interactive Preview
25 min read

Chapter 1: The Knowledge Graph Mental Model

  • Understand what a knowledge graph is and why it matters for retention
  • Identify the three types of knowledge Cograph captures
  • Recognize your own expertise surface area in the graph

Why Knowledge Gets Lost

Every organization has the same invisible problem: the knowledge that makes your company work lives inside people's heads, not in your systems.

When someone leaves — whether it's a planned retirement, a surprise resignation, or a role change — a piece of your organizational memory walks out the door. Documentation helps, but documentation captures what people remember to write down, not what they actually know.

Cograph solves this differently. Instead of asking people to document everything upfront (which nobody does), it continuously maps expertise from signals they're already producing: code commits, Slack conversations, document edits, and meeting patterns.

cypher
// Each employee node has relationships to topics they know
MATCH (e:Employee {email: "sarah@company.com"})
  -[r:HAS_EXPERTISE]->(t:Topic)
RETURN t.name as topic, r.score as score
ORDER BY r.score DESC
LIMIT 5

// Result:
// "API Authentication"     → 0.94
// "PostgreSQL Optimization" → 0.87
// "CI/CD Pipelines"        → 0.81
// "React Patterns"         → 0.76
// "Infrastructure as Code" → 0.71

A simplified view of how Cograph models expertise in the graph

Three Types of Knowledge

Cograph captures: (1) Technical expertise — code ownership, architectural decisions; (2) Process knowledge — how things actually get done day-to-day; (3) Relationship capital — who knows who, who to call for what.

How Expertise Scores Work

Each employee–topic relationship has a score between 0 and 1, built from multiple signals:

- Code ownership (40%) — proportion of commits, files last touched - Document authorship (30%) — Notion pages, Google Docs created or heavily edited - Communication patterns (20%) — Slack threads where the person is the go-to responder - Peer recognition (10%) — mentions, direct reports' engagement

The score is continuously updated as new signals arrive. It's not a one-time assessment.

Knowledge Check

What is the primary advantage of Cograph's signal-based approach over traditional documentation?

Check Your Own Profile

Once your organization connects its integrations, you can view your own knowledge profile from Settings → My Profile. Most people are surprised by what Cograph already knows about them.

Key Takeaways

  • The knowledge graph maps expertise continuously from real work signals
  • Expertise scores are weighted composites across code, docs, communication, and recognition
  • Three knowledge types: technical, process, and relationship capital

How to Purchase

  1. Navigate to Learning → Marketplace in the sidebar
  2. Browse or search for modules by category, difficulty, or topic
  3. Click a module card to view the full curriculum and preview
  4. Click Purchase Module — charged to your organization's billing
  5. The module appears immediately in Learning → My Modules
Team pricing: Purchasing a module gives access to your entire organization — anyone in the account can access purchased modules. Modules are priced $29–$79 per module, purchased once.

Progress Tracking

Cograph tracks progress at the chapter level. Each chapter has a "Mark Complete" button — completion is self-reported and not gated on quiz scores.

Not Started

Module purchased but no chapters opened

In Progress

At least one chapter marked complete

Completed

All chapters and required quizzes finished

Manager Visibility

Managers can see the learning progress for their direct reports from Team → Learning Progress. Visible data includes:

  • Which modules are purchased and assigned
  • Chapter completion status (complete / not started)
  • Time spent per module
  • Quiz scores (not individual answers)

Knowledge Graph Integration

Completed modules feed back into the knowledge graph as lightweight expertise signals. Completing the GraphQL APIs module adds a small positive signal to your GraphQL expertise score — weighted far lower than direct work experience but useful for employees building new skills.

Signal weight
Completed module+5% to expertise score
Completed module + passed all quizzes+10% to expertise score
Direct work signals (commits, docs)Primary score driver

This means learning modules are useful for closing gaps, but won't create false expertise signals that distort successor matching — real work experience always dominates.

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