01
Read in place
Declarative connectors reach documents, SaaS content, messages, records, SQL systems and vector stores.
Context Graph
Turn enterprise metadata into governed context for AI.
Kynexa connects enterprise sources, extracts asset- and element-level metadata, enriches it with semantics and taxonomy, and builds a governed context graph that RAG applications and agents can use with policy, provenance, and lineage intact.
Reads at source · single-tenant · every policy decision logged.
Policy governs every graph operation
Access, purpose, sensitivity and lineage are checked before context moves.
DiscoverTraverseRetrieveAssemble
Connected sources
DocumentsFiles, pages, chunks
SaaS systemsTickets, wikis, CRM
Data storesTables, vectors, records
Governed context graph
Metadata, relationships, sensitivity and lineage evaluated before context is used.
Discover
Traverse
Retrieve
Assemble
AI consumers
RAGPermitted retrieval
CopilotsScoped answers
AgentsGoverned action
Evidence captured with every answer
Provenance
Lineage
Policy decision
Audit trail
Connected sources, governed context graph, inline policy, provenance, lineage, audit trail, governed RAG, copilots and agents.
A four-stage flow. Enterprise sources are connected and read at source. Processing extracts metadata only. A governed context graph tags every node Allowed, Denied or Redacted by policy. RAG apps and agents receive only permitted context, carrying lineage.
The problem you own
Enterprise AI cannot rely on vector similarity alone. Knowledge lives across documents, records, tables, tickets, messages, SaaS systems and existing vector stores, each with different ownership, freshness, sensitivity and business meaning. Without a governed graph of that context, AI may retrieve related text while missing source authority, relationships, access purpose, policy restrictions, or the lineage needed to explain an answer.
How it works
Kynexa starts with governed ingestion. Declarative connectors read enterprise systems in place, file processing creates stable chunks and unified previews, semantic services extract entities, classifications, sensitivity, and summaries, and taxonomy services attach business meaning. Metadata Manager stores this as searchable asset-, element-, embedding-, provenance-, and graph metadata, while Policy Manager evaluates who or what can discover, traverse, retrieve, combine, or use each part of the graph.
How it works
From source metadata to governed context
From source metadata to governed context
Kynexa builds graph-ready metadata first, then applies policy before AI can discover or use it.
02
Process elements
File processing extracts text, creates stable chunks, preserves page and section context, and generates previews.
03
Enrich semantics
Semantic services add entities, relationships, classifications, sensitivity labels and summaries.
04
Attach taxonomy
Taxonomy services apply business meaning so graph nodes can be searched, governed and explained.
05
Evaluate policy
Policy Manager controls discovery, traversal, retrieval, combination and downstream use.
Metadata Manager stores graph-ready context
Source systems stay authoritative; Kynexa stores governed metadata for retrieval and audit.
AssetsElementsEmbeddingsProvenanceGraph edgesPolicy state
Context graph assembly
Assets, elements, owners, entities and lineage become connected graph metadata.
Policy Manager evaluates
Discover
Traverse
Retrieve
Combine
Use
Kynexa reads enterprise sources in place, processes stable elements, enriches metadata with semantics, attaches taxonomy, stores graph-ready metadata and evaluates policy for discovery, traversal, retrieval, combination and use.
A five-step process: read enterprise sources in place, process stable elements, enrich metadata with semantics, attach taxonomy, store graph-ready metadata, and evaluate policy before discovery, traversal, retrieval, combination or use.
Control matrix
Where governance applies
Policy is not bolted on after retrieval. It shapes discovery, traversal, assembly and use of context.
| Control surface | Policy input | Enforcement point | Audit evidence |
|---|---|---|---|
| Source connection | Tenant · provider · scope | Connector configuration | Connection and scan record |
| Metadata discovery | Role · purpose · sensitivity | Metadata Manager search and preview | Discovery decision |
| Element retrieval | Subject · action · semantic labels | Policy Manager chunk evaluation | Retrieval trace |
| Context assembly | Purpose · taxonomy · lineage | RAG and agent context window | Allowed, denied, or redacted context |
What you get
Connector-backed graph foundations
Bring documents, SaaS content, messages, records, SQL systems and vector stores into a common metadata pipeline without moving enterprise source systems.
Asset and element intelligence
Represent files, tables, chunks, pages, sections, embeddings, summaries, classifications, sensitivity, ownership and custom schema fields as graph-ready context.
Business taxonomy and semantic labels
Attach enterprise meaning through taxonomies, categories, entities, relationships and semantic labels that policy and retrieval can both understand.
Policy-aware graph traversal
Evaluate access at asset, element, metadata and purpose level before context is surfaced to a user, copilot, RAG workflow, or agent.
Hybrid discovery and governed retrieval
Combine keyword, metadata, facet, semantic and vector search so AI gets relevant context with least-privilege controls preserved.
Provenance, lineage, and audit evidence
Trace each answer back to the source asset, element span, metadata decision, policy evaluation, retrieval step and downstream AI interaction.
What changes for you
What changes for you
- What you can control
- Which sources, assets, elements, labels, relationships and purposes each user or agent can discover and use.
- What you can prove
- Which connectors, chunks, graph nodes, policies and source spans shaped each AI answer or action.
- What changes operationally
- Teams get connected, current AI context without replacing source systems or weakening least privilege.
- Primary stakeholders
- Data governance, security, AI platform.
Technical FAQ
No. Kynexa reads and indexes at source, then stores governed metadata such as chunks, embeddings, entities, relationships, sensitivity, taxonomy, policy state, provenance and lineage. Source systems remain authoritative.
Similarity finds related text. Kynexa adds connector context, asset and element metadata, taxonomy, semantic labels, policy evaluation and lineage, so retrieved context is relevant, permitted and explainable.
Yes. Teams can evolve schemas, prompts, taxonomy nodes, semantic labels, purpose vocabulary and policies, then regenerate or re-evaluate metadata so the graph reflects changing business and regulatory context.
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See Kynexa govern your AI — in 30 minutes.
Bring a real use case. We'll set up governed retrieval, reasoning and audit on your stack.