Search that understands who's asking and why.

PageRank is 28 years old

Every search engine, every recommendation system, and every AI retrieval pipeline ranks content the same way: compute a global relevance score from the link structure or embedding similarity of the corpus. The score is the same for every query, every user, every context. A medical researcher and a curious teenager receive the same ranking for the same query.

This isn't a quality problem. It's a structural one. Global ranking treats relevance as a property of the content. But relevance is a property of the relationship between the content and the query — and the query carries context that global ranking cannot see.

Semantic discovery replaces global ranking with contextual semantic evaluation at every traversal step. A discovery object — a persistent, stateful agent carrying its own intent, context, memory, policy constraints, and cognitive state — traverses an adaptive index through successive anchor evaluations. At each anchor boundary, search, inference, and governance execute as a single atomic step. Only when all three evaluations pass does the traversal advance.

Three modes, one mechanism

Human search: A user's query initializes a discovery object that traverses the index and returns ranked results — but ranking is contextual, computed from the discovery object's accumulated semantic state at each step, not from a global link graph.

Agent reasoning: An autonomous agent initializes a discovery object to gather information for a specific operational objective, with each traversal step governed by the agent's full cognitive state including affect, confidence, and integrity.

Answer synthesis: The discovery object traverses the index to construct a composite answer from multiple anchors, with each synthesis step evaluated for semantic coherence, source reliability, and governance compliance.

The modes are parametric configurations of the same traversal mechanism. The difference is in the discovery object's initialization — what intent, what governance constraints, what cognitive state modulates the traversal.

Quality that improves with governed use

As more agents traverse the index, accumulated traversal history improves anchor relevance scoring, anchor self-organization, and semantic neighborhood quality. Anchors that consistently satisfy governed traversal objectives accumulate higher trust. Anchors that produce semantic drift or governance violations are deprioritized. The index improves with use — but the quality signal is richer than links because it encodes why content was relevant, not just that it was linked.