Reduce semantic drift
Use one published ConceptCode instead of accumulating local labels that diverge across tools, teams, and languages.
Shared semantic identity for people, agents, and software
Make important meaning portable and inspectable.
Embedded Semantics helps people, local AI agents, and applications carry one reviewed meaning across languages and system boundaries. It returns a stable ConceptCode when governed evidence supports one—and preserves unknown or ambiguity when it does not.
- Align
- One shared semantic reference
- Preserve
- Original language plus stable identity
- Abstain
- No forced guess when evidence is absent
Keep meaning inspectable
Carry the original expression, exact code, definition, provenance, and registry version as separate, reviewable evidence.
Fail safely
The current production path resolves reviewed exact expressions and explicitly abstains when authoritative evidence is absent.
Where the value appears
Use it when meaning must survive a handoff.
A shared reference across humans and software
Different applications and agents can point to the same published ConceptCode instead of relying on locally invented labels.
The code is usable only after an explicit resolved response.Meaning that survives language changes
Reviewed expressions in multiple languages can attach to one stable identity while each original expression remains available for display and evidence.
The production resolver covers reviewed exact evidence; it does not promise unseen-paraphrase resolution.Safer automation through explicit uncertainty
Unknown and ambiguous results remain first-class outcomes, allowing an agent to ask for context or continue without assigning a false identity.
An abstention must never be converted into a best-effort ConceptCode.Choose your path
Start with the outcome you need.
I need to understand the value
See which problems governed semantic identity can solve, where it belongs in a workflow, how to adopt it gradually, and how to measure benefit.
See benefits and use cases →I want to evaluate the interaction
Submit one expression, inspect the raw JSON, and see a plain-language explanation of resolved, unknown, ambiguous, and error outcomes.
Open the resolver walkthrough →I need to prove the client is safe
Select one recipe and run six local fixtures to prove that only valid resolved evidence receives code and version while authorization remains separate.
Run the readiness self-test →I am connecting a desktop or local agent
Start with one stateless HTTPS bootstrap, then send only the expression that needs governed identity. No account, session, hosted execution provider, or model dependency is required.
Open the connection quickstart →Current architecture
Separate the probability from the meaning.
A vector is a coordinate produced by a model version. A concept identity should survive model upgrades, language changes, and retraining. Embedded Semantics keeps those layers separate and makes the registry authoritative.
- ExpressionReviewed language evidenceUnicode-preserving expression plus optional language constraint
- LookupGoverned exact resolutionProduction path: reviewed exact-equivalence evidence only
- IdentityConcept registryStable ConceptCode, definition, evidence, and registry version
- OutputInspectable recordHuman-readable concept page plus stable JSON API representation
- ResearchSemantic candidate retrievalExperimental lane for unseen queries; not production-active
Starter registry
Concepts are first-class records.
Direct answer
What is registry-backed semantic resolution?
It is a semantic system where persistent concept records define meaning and language expressions provide governed evidence for those records. The practical benefit is a shared reference that can travel through memory, APIs, events, routing, and audits without turning one model or one language into authority. The current production resolver handles reviewed exact multilingual expressions and explicitly abstains on unseen phrases with unknown_expression when exact authority is absent. Experimental embeddings are evaluated as a future candidate-retrieval layer, never as semantic identity.