Why coherence beats more context
An LLM call is stateless and a vector search is lossy: you get retrieval, not coherence. Spread your meaning across a vector database, a schema registry, prompt strings, and a graph you patch by hand, and the same concept ends up meaning three different things in three systems. Your agents inherit the drift. Penumbra holds one high-fidelity representation of your domain, governed and provenanced, so every agent and every call reasons over the same meaning. Coherence and fidelity are what make agent behavior trustworthy. Not more tokens.How Penumbra works
Six capabilities cover the lifecycle, from defining your domain to acting on it.- Define your domain — shapes. Typed frameworks that make your domain explicit, authored in the Shapes Workbench. Everything else reads from them.
- Ingest and extract — sources. Bring documents and data in, and coerce them into typed structure through a shape. The same source read through different shapes surfaces different meaning.
- Govern every change — semantic git. Every write stages, plans, applies, and reverts. Version control for meaning, with lineage preserved.
- Retrieve and reason — search. Hybrid, semantic, and lexical retrieval over entities and the sources behind them.
- Check before acting — decision quality. Ask whether a region of the graph is fit to act on, for a specific purpose. A verdict with findings, never a confidence score.
- Interop in and out — import and export. Import OWL and RDF, and compile your domain back out to OWL, RDF, SHACL, JSON, YAML, and Markdown. Define once, project everywhere.
@penumbra-systems/platform), typed
TypeScript access most builders use, and the API, the REST endpoints the SDK
calls at https://pnbr.io/v1.
Penumbra is in preview for design partners and developer testers. Request a key
at shep@getpenumbra.ai.
Start building
Design your shapes
Author your domain model in the Shapes Workbench, the design-time MCP.
Install the SDK
Add the client, set your key, and connect in about two minutes.
Quickstart
Read your ontology, search, capture, recall memory, and check decision quality.
Ingest a document
Upload, extract through a shape, and coerce material into the graph.