Scale without verification is a liability.
Building for machines means maintaining structured, high-signal knowledge that can be verified and revisited. Without it, scale amplifies error.
In practice, "building a library" looks like data contracts: versioning, lineage, refresh schedules, backfills, quality gates, and observability. If you can't trace a claim, reproduce it, and monitor drift, you don't have a knowledge layer—you have a risk surface.
One Ontology: People, Organizations, Data
Most real-world knowledge reduces to people, organizations, data, and their relationships.
Synorb uses a single ontology with shared taxonomies across these primitives. Filings, research papers, earnings calls, blog posts, and structured feeds are normalized into the same backbone.
This lets machines traverse the world—from people to organizations to data and back again—across domains and over time.
Discovery: Durable, Verifiable Knowledge
Discovery Streams prioritize durability and verification. Instead of ranking information by visibility or engagement, they organize knowledge around what holds up: traceable sources, clear attribution, and stable tag resolution.
Human-oriented search rewards attention. Discovery Streams reward provenance.
Narrative: Data Made Legible
Modern systems generate data continuously. Machines can't reason over it until it becomes legible.
Narrative Streams translate structured and time-series data into explicit, citable statements linked back to underlying measurements. They're built for continuous ingestion: new measurements arrive, narratives update, and downstream systems receive attributable deltas instead of re-reading static documents.
Research: Analysis With Provenance
Traditional research is written for human readers at limited cadence. Machines require the same depth at higher frequency.
Research Streams assemble citation-ready analysis from trusted inputs. Sources are preserved, assumptions are stated, and refresh is explicit.
These streams share a single ontology, so models can reason across them as one unified corpus rather than disconnected documents.