Revolutionizing AI Memory – Join the Waitlist

Give Your AI True Memory
Beyond Simple RAG

From static retrieval to evolving intelligence. Experience AI that grows smarter with every interaction, discovering connections and compounding knowledge over time.

The AI Memory Crisis

Current AI systems struggle with truly understanding and retaining information, severely limiting their potential for deep learning and complex problem-solving.

Vector Similarity Misses Real Relationships

Current RAG systems reduce rich knowledge to flat vector distances, missing crucial relationships and context. Complex connections are lost in simple similarity scores.

Manual Knowledge Engineering Bottlenecks

Teams waste countless hours manually structuring and updating knowledge bases. Without autonomous integration, valuable information stays trapped in documents and silos.

Static Knowledge Structures Break Down

Traditional knowledge bases can't adapt their structure as domains evolve. Fixed schemas and ontologies quickly become outdated, requiring constant manual maintenance.

Shallow Context Windows Limit Understanding

When answers require connecting information across documents and time periods, limited context windows and single-hop retrieval fail to deliver meaningful insights.

No Learning from Experience

Current systems reset with every query, unable to learn from past interactions. Each piece of knowledge stays isolated, missing the compound value of accumulated insights.

Privacy & Security Vulnerabilities

Traditional AI memory systems store sensitive data in plain text, creating security risks. Without proper encryption and access controls, confidential information remains exposed.

TypeCommand: The Solution to AI Memory Crisis

TypeCommand revolutionizes AI cognition with three key components: Dynamic Knowledge Graph, Advanced Reasoning, and Intelligent Agents. Together, they enable genuine understanding and adaptive learning.

Dynamic Knowledge Graph

The foundation of TypeCommand's true AI memory. It creates a living network of information that grows and adapts with each interaction.

  • Interconnected data representation
  • Real-time knowledge integration
  • Semantic relationship mapping

Advanced Reasoning

Enables complex problem-solving and inference, allowing AI to think beyond simple pattern matching.

  • Multi-hop logical inference
  • Analogical reasoning
  • Hypothesis generation and testing

Intelligent Agents

Autonomous AI entities that work tirelessly to enhance and utilize the knowledge graph.

  • Automated document parsing
  • Continuous ontology refinement
  • Complex query processing

Supporting Technologies

Advanced Document Parsing

AI agents automatically extract and structure knowledge from diverse document types, seamlessly integrating new information.

Adaptive Ontology

The knowledge schema evolves automatically, ensuring optimal representation of complex relationships over time.

Graph-Enhanced Queries

Combine semantic search and graph traversal to answer complex, multi-faceted questions with unprecedented accuracy.

Contextual Retrieval

Go beyond keyword matching with retrieval that understands the semantic connections and context in your data.

True AI Memory in Action

Watch how TypeCommand transforms raw data into a living, evolving knowledge base that powers genuinely intelligent AI interactions.

1

Ingesting Unstructured Text

// Raw document input with extracted entities
The AI agent market is projected to reach$25B by 2025, with autonomous agents processing over500M tasks daily. Studies show that agents withadvanced memory systems like TypeCommand's are4x more efficient at complex tasks. Currententerprise solutions manage an average of10,000 agent interactions per day, highlighting the growing demand for scalable AI memory solutions.
2

Dynamic Knowledge Structuring

// Automated Ontology
Market: AI Agents ($25B by 2025)
Scale: 500M daily tasks
Performance: 4x efficiency gain
// Knowledge Graph
AI Agents--process-->500M tasks/day
Memory Systems--enable-->4x efficiency
Enterprise--manages-->10k interactions/day
3

Continuous Learning & Integration

// Knowledge evolution timeline
Initial
Base Knowledge:
AI Agents → process → 500M daily tasks
Update
New Data Integrated:
+ Market Size: $25B projected value
+ Efficiency: 4x improvement with memory
Synthesis
Generated Insights:
High task volume correlates with market growth
Memory systems drive efficiency improvements
Projected compound effect: Rapid market expansion
// Real-time confidence scoring
85% confidence
4

Intelligent Interaction

// User query
"What's driving the growth in the AI agent market?"
// Graph traversal and reasoning
Entity: AI Agent Market
Source: "The AI agent market is projected to reach $25B by 2025..."
↓ has_metric
Entity: Market Size
Value: $25B by 2025
↓ supported_by
Entity: Daily Processing
Source: "...autonomous agents processing over 500M tasks daily."
↓ leads_to
Entity: Efficiency Gain
Source: "...agents with advanced memory systems are 4x more efficient..."
// LLM synthesizes response using graph path
Reasoning chain:
1. Market growth confirmed by $25B projection
2. Growth supported by high task volume (500M daily)
3. Efficiency gains (4x) validate market potential
4. Connected evidence suggests sustainable growth trajectory
// Generated response
The AI agent market is experiencing strong growth driven by proven efficiency gains (4x improvement) and massive scale (500M daily tasks), leading to a projected market size of $25B by 2025. This growth is supported by clear operational metrics and measurable performance improvements.

The Compounding Value of TypeCommand

Experience exponential growth in AI capabilities as TypeCommand continuously learns and evolves, creating a flywheel effect of increasing value over time.

Exponential Knowledge Growth

As TypeCommand processes more data, its knowledge graph expands exponentially, uncovering new connections and insights that compound over time.

Adaptive Learning

The system continuously refines its understanding, learning from each interaction to improve accuracy and relevance of future insights.

Accelerated Problem Solving

With a growing knowledge base, TypeCommand solves complex problems faster and more efficiently, reducing time-to-insight for your team.

Cross-Domain Insights

As the knowledge graph spans more domains, TypeCommand uncovers valuable cross-disciplinary connections, driving innovation and new discoveries.

The TypeCommand Flywheel Effect

  1. More data ingested leads to a richer knowledge graph
  2. Enhanced knowledge graph enables more accurate reasoning
  3. Improved reasoning capabilities attract more users and data
  4. Increased usage generates more interactions and learning opportunities
  5. Continuous learning further enhances the system's capabilities

This self-reinforcing cycle creates a powerful flywheel effect, where each revolution amplifies the value and capabilities of TypeCommand, providing ever-increasing returns on your investment.

Build Smarter AI Applications

Join innovative teams using TypeCommand to create AI that truly understands and learns from your data.