Platform · Syncratic
A Knowledge Assurance System
Syncratic reveals the relationships across data entities, their dependencies, and changes over time. This interweaves them into a governed living knowledge structure that people and other knowledge-dependent entities and workflows can reason over.
Platform surfaces
One workspace, every knowledge motion
Each surface exists to make one kind of knowledge work inspectable: discovery, reasoning, relationship review, change review, and operations. Documents and repositories are governed today; the same lifecycle is designed to extend across the information continuum: structured systems, verbose unstructured sources, and continuous signals.
Search / Ask
A unified discovery-to-reasoning surface. Search finds evidence across accessible scopes; Ask reasons over it with citations, provenance, and memory threads.
Insights
Graph-derived relationship narratives: live streams, multi-source signals, and document deltas clustered into readable, subject-driven insights.
Explorer
Document inspection with metadata, graph context, and change history: see the evidence behind every answer at the document level.
Document Delta Engine
Version lineage and deterministic deltas. When a contract, policy, or filing changes, see what moved, when, and why it matters.
Connectors
Governed source synchronization with schedules, sync state, per-file visibility, and administrator-controlled configuration.
Privacy Engine
Runtime context governance that masks, reduces, or withholds sensitive evidence before it reaches a model; canonical artifacts stay intact.
Audit & Admin
Boundary diagnostics, event inspection, and operational traceability. RBAC-controlled administration of users, roles, licensing, and models.
AI Gateway
Applications call Syncratic Ask through a governed API, with tenant policy, privacy checks, spend limits, and audit evidence on every call.
Semantic search and retrieval
Retrieval that follows meaning
Every query is decomposed into its semantic parts, so retrieval follows meaning across the enterprise corpus through relationship structure.
- Hybrid retrieval: lexical, vector, and graph evidence, ranked together
- Document-family-aware enrichment for contracts, filings, cases, and datasets
- Direct, reverse, and follow-up questions all retrieve the right evidence
Governance
RBAC and data boundaries, enforced at query time
Access control is enforced at query time across every store: documents, graph, and insights. Tenant and user scopes define what can be retrieved; object-level access defines what can be seen; role-based access governs who administers what.
- Identity through Keycloak/OIDC with role and identity context on every request
- Tenant/user scope and object-level access enforced across all data stores
- RBAC-controlled administrative surfaces: users, roles, licensing, models, privacy
- Privacy Engine gates sensitive evidence at the model context: mask, reduce, suppress, or deny
- Boundary diagnostics in Audit: see exactly when and why a boundary was applied
Deployment posture
Sovereign by architecture, model-agnostic by design
How the layers, boundaries, and dataflows behind these postures fit together: see the architecture overview.
Sovereign deployment
Offline-safe licensing and entitlement enforcement keep the deployment self-contained while premium capabilities stay governed and auditable. Run air-gapped, own your infrastructure, and keep operational control, with admin bootstrap, SMTP-backed invitations, license update, and readiness evidence included.
Model-agnostic by design
The model layer is intentionally replaceable. SLMs and LLMs are replaceable execution components: model roles can be bound to private, deployment-local LLMs or external public endpoints, with a trust zone available to Privacy Engine policy. You choose the deployment posture per role, across model locations and providers.
Governed knowledge assurance
See the platform on your own data.
A governed loop from ingestion to operational insight: sources in, evidence up, answers out, everything auditable.