About
SILKLEARN
A knowledge graph platform. It reads across your documents and builds a structured graph of how everything connects.
What we build
One engine, one workspace. Private spaces for your team. Decompose sources, surface contradictions, arrange the structure on a canvas. Consumable by humans and AI.
SILKLEARN starts where documents stop. You upload PDFs, Markdown, DOCX, or plain text, and the engine compiles them into a graph: claims with provenance, dependencies mapped, contradictions preserved, gaps named. Nothing is silently averaged away — when two sources disagree, both readings stay visible and branched, and nothing is served as truth until a human approves it.
From the compiled structure you build a canvas — your curated arrangement of the knowledge, the way you understand it. Canvases are shareable privately, with your team, or through a public read-only link. The graph stays yours: content-addressed, versioned, auditable.
Why structure matters
Language models predict tokens. They do not build world models. Fluency is not understanding. Retrieval is not reasoning.
The cure for a disease is not twenty isolated papers away. It is one structural insight away — if someone can see how those papers connect. SILKLEARN is built for that gap.
This is why the engine is deterministic where it matters. Identity is content-addressed, history is immutable and bitemporal, and the trust layer is fail-closed: raw or unapproved claims never enter what gets served. The structure, provenance, versioning, and trust are deterministic and auditable; the content extraction itself is a reviewed draft. That combination — a compiled graph you can audit and a human gate on what becomes truth — is the difference between a retrieval tool and a knowledge platform.
Team
