Platform · Syncratic
A Knowledge Assurance System
Syncratic represents enterprise data as a network of relationships, not isolated documents or fragments. Entities, roles, dependencies, and changes over time are interwoven into a living knowledge structure that can be reasoned over, not just retrieved.
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.
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, without mutating canonical artifacts.
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
Beyond file titles, into understanding
Every query is decomposed into its semantic parts, so retrieval follows meaning across the enterprise corpus instead of stopping at keywords in a filename.
- 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: not suggested
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 rather than being locked to one model location or provider.
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.