Use Cases

Where Ix is used.

Ix Infrastructure is a general-purpose system for representing, querying, and evolving structured knowledge about complex systems. The same core primitives apply across industries, while domain layers tailor semantics, metrics, and queries.

Core Infrastructure Layer

These use cases apply to every deployment. They describe what Ix does regardless of industry.

Persistent System-of-Record for Knowledge

Technical knowledge is fragmented across documents, codebases, tickets, diagrams, and people. Over time, context is lost, decisions are repeated, and systems become harder to reason about.

Ix represents knowledge as a persistent scene graph where systems, components, concepts, and decisions are first-class entities with explicit relationships. The graph evolves as the system evolves, preserving structure and context across time.

  • Versioned entities and relationships
  • Evidence links back to source artifacts
  • Queryable structure instead of static files

Queryable Organizational Memory

Finding answers requires knowing where information lives and how to interpret it. This does not scale with team size or system complexity.

Ix exposes a unified query interface over the knowledge graph. Queries operate on entities and relationships rather than filenames or keywords, enabling precise retrieval and reasoning.

  • Graph and semantic queries
  • Deterministic traversal and filtering
  • Consistent answers across tools and sessions

AI-Readable World Models

LLMs operate on unstructured text and lack persistent memory or system awareness. This limits reliability and makes outputs difficult to trust.

Ix provides a structured world model that AI systems can query directly. Instead of inferring structure from text, models operate on explicit entities, constraints, and relationships.

  • Graph-native retrieval for agents
  • Persistent context across sessions
  • Supports local, private, or cloud LLMs

File System & Internal Knowledge Layer

Structured Documentation Systems

Documents encode structure implicitly, making it difficult to extract relationships, dependencies, and historical changes.

Ix ingests documents into content-addressed subgraphs. Structural elements (sections, figures, references) become nodes, while semantic and logical relationships are explicit edges.

  • Document-as-subgraph representation
  • Incremental versioning without duplication
  • Cross-document linkage and reuse

Engineering Knowledge Bases

Design rationale and constraints are rarely preserved in a usable form, leading to repeated mistakes and slow onboarding.

Ix models decisions, assumptions, and constraints as first-class entities connected to systems and components, preserving institutional knowledge.

  • Decision and constraint modeling
  • Traceability from implementation to rationale
  • Long-term knowledge retention

Robotics & Autonomous Systems

World Modeling for Robots

Robots require a coherent, up-to-date representation of their environment, but most systems rely on ad-hoc maps and brittle data pipelines.

Ix represents environments, objects, and spatial relationships as dynamic scene graphs that update with perception and state estimation.

  • Persistent world representations
  • Multi-resolution spatial graphs
  • Integration with perception and SLAM

Semantic Reasoning for Autonomy

Autonomous systems struggle to reason beyond geometry, limiting planning and decision-making.

Ix augments geometric world models with semantic and relational structure, enabling higher-level reasoning about tasks, goals, and constraints.

  • Task and object semantics
  • Constraint-aware planning
  • Agent-accessible world state

Embodied AI & Agentic Systems

Persistent Agent Memory

Most agents are stateless between runs and rely on context windows or logs for memory.

Ix provides a persistent memory layer where agents read and write structured state, enabling long-term learning and continuity.

  • Read/write agent memory
  • Structured state persistence
  • Multi-agent shared context

Tool and Environment Modeling

Agents lack an explicit understanding of tools, interfaces, and system affordances.

Ix models tools and environments as entities with capabilities and constraints, enabling agents to reason about actions rather than guess.

  • Explicit affordance modeling
  • Safer tool invocation
  • Deterministic agent behavior

Genomics & Neuroscience

Experimental Knowledge Graphs

Experimental data, metadata, and interpretation are stored separately, making reuse and synthesis difficult.

Ix models experiments, datasets, parameters, and results as connected graph entities, preserving structure and provenance.

  • Provenance-aware data modeling
  • Cross-experiment querying
  • Reproducible analysis context

Multi-Scale Biological Models

Biological systems span many scales, but tools often operate at only one level.

Ix supports hierarchical graphs that connect molecular, cellular, and system-level entities.

  • Hierarchical representations
  • Cross-scale reasoning
  • Flexible domain schemas

Simulation & Digital Twins

Digital Twin State Management

Simulations generate large volumes of state that are difficult to relate back to system structure.

Ix ties simulation state directly to system entities, enabling introspection, comparison, and historical analysis.

  • Stateful simulation graphs
  • Time-indexed system snapshots
  • Model comparison and validation

Complex System Analysis

Understanding emergent behavior requires reasoning across many interacting components.

Ix enables graph-based analysis of interactions, dependencies, and feedback loops.

  • Interaction modeling
  • Constraint propagation
  • System-level insight