Developers

Build systems that remember.

Ix is infrastructure for representing complex systems as a persistent, queryable model. You can treat it like a “system of record” for technical state and relationships across tools, teams, and time.

DSG-JIT Documentation Platform Architecture
Persistent graph memory Versioned / time-travel Deterministic ingestion LLM-optional runtime

Where Ix fits in real systems

Most products already have data (files, schemas, logs, tickets), but the relationships live in people’s heads or ad-hoc glue. Ix provides a shared model that applications can query directly.

Ingestion

Connectors pull from your sources. A deterministic pipeline extracts entities/refs, resolves links, and writes versioned updates to the model.

  • files + docs + repos + tickets + chat
  • structured or unstructured inputs
  • auditability + provenance

Model

Systems, components, concepts, and constraints become nodes/edges with typed attributes. Documents can be modeled as versioned subgraphs instead of static blobs.

  • explicit relationships
  • versioning + diffs
  • domain-specific schemas

Query & Runtime

Applications query the graph to retrieve structure and state. LLMs can sit on top, but aren’t required to build or maintain the model.

  • graph traversal + constraints
  • hybrid retrieval (optional vectors)
  • tooling for agents

Developer use cases

These are the cases that tend to produce immediate ROI: fewer rediscoveries, faster onboarding, more reliable automation, and less “tribal knowledge.”

Robotics & Autonomous Systems
Persistent world-model memory across runs, missions, and deployments. Queryable state, objects, places, events.
Example: “what changed in the environment since the last run?”
Embodied / Agentic AI
Give agents a structured environment + durable memory. The agent queries state, relationships, constraints; not raw text.
Example: “plan actions given system topology + constraints.”
Neuroscience & Genomics
Model experiments, samples, pipelines, assumptions, and results as connected entities. Enable reproducibility and provenance.
Example: “find datasets produced by pipeline X with parameter Y.”
Digital Twins & Simulation
Represent system structure + state that updates during simulation. Query causal structure and scenario deltas.
Example: “what dependencies broke after component change?”
Internal Knowledge Systems
Turn scattered docs into a versioned model. Deterministic link extraction makes knowledge reusable and queryable.
Example: “which design decision depends on this constraint?”

What developers get

Reliable structure

Stop asking an LLM to “guess” structure. Ix stores structure explicitly: entities, edges, types, provenance, versions.

Persistent memory

State survives restarts. You can query historical versions and compute deltas over time.

Composable APIs

Apps query the model directly. Your workflows can treat the model as a shared dependency, not a one-off index.

Works with your AI stack

LLMs become consumers of structured state. You can keep deployments private/on-prem without changing the model layer.

How teams deploy it

A typical rollout starts small, proves value, then expands.

  1. Pick one narrow slice (one product, one repo, one dataset pipeline, or one subsystem).
  2. Model the schema (types, attributes, relationship rules).
  3. Wire ingestion (connectors + deterministic extraction + resolution).
  4. Ship one query surface (UI search, internal API, or agent tool).
  5. Expand once it’s reliable and measurable.
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