Architecture
The Three-Repo Trident
KynticAI is not a monolith. It is three purpose-built repositories that work together: an open-source core, a SaaS control plane, and an enterprise fortress.
Scout
Open-Source Core
The public data-plane engine. Domain entities, selector execution, context snapshots, GraphQL/REST API, React admin console. MIT-licenced.
universalcontextlayer
Cloud
SaaS Control Plane
Account management, billing (Stripe), licensing (RSA-signed), data-plane registration, downloads, support cases, and the mini CRM. Never touches raw customer data.
universalcontextlayer-cloud
Fortress
Enterprise On-Prem
Private connectors (SQL Server, PostgreSQL, REST/CRM, email), governance, compliance exports, identity (OIDC/SCIM/SAML), credential vaults, Helm charts, air-gapped deployment.
universalcontextlayer-enterprise
Data Flow: Source to Consumer
Data never moves. KynticAI reads metadata where it lives and builds semantic context in place.
Sources
SQL databases, CRM, ERP, flat files, APIs — data stays where it lives
DataSource Registration
Connectors register metadata without moving raw data
Selector Execution
5 mapping kinds: DirectField, StringToEnum, Threshold, WeightedScoring, Formula
Context Facts
Semantic units with confidence scores, provenance chains, and freshness
Context Snapshots
Point-in-time assemblies for any entity, consumed by AI agents
Consumers
AI models, dashboards, REST/GraphQL APIs, webhooks, SDK clients
Two Enterprise Sub-Tiers
Without-AI Tier
Semantic context layer only. Connects to existing databases, builds context facts with provenance, serves via API. No AI model required — pure data infrastructure.
- Connector marketplace
- Selector engine
- Context snapshots
- GraphQL + REST API
- Provenance audit trail
- Role-based access
With-AI Tier
Everything in Without-AI, plus embedded AI model support. Llama, Mistral, or customer’s own model runs on-prem. The context layer feeds the model; the model enriches the context.
- Embedded Llama/Mistral
- On-prem inference
- Discovery Agent
- Self-improving flywheel
- Synthetic industry agents
- AI-safe packaging
Rust Engine Modules
The performance-critical path is written in Rust for memory safety, zero-cost abstractions, and sub-millisecond latency.
ucl-ingest
High-throughput metadata ingestion from connectors
ucl-selector
Selector execution engine with 5 mapping kinds
ucl-context
Context fact assembly with confidence scoring
ucl-provenance
Full audit trail from fact back to source record
ucl-synthesis
Markdown dossier generation with token budgeting
ucl-gateway
API gateway with rate limiting and auth
Architecture Intention
| Metric | Legacy | KynticAI |
|---|---|---|
| Selector execution | Batch reports and manual ETL | Low-latency selector path |
| Context snapshot assembly | Static dashboard export | On-demand governed context |
| Integration model | Centralised copy-first pipeline | Read-only customer data-plane access |
| Operational footprint | Large platform dependency | Scoped services per deployment |
| Change control | Slow project release cycle | Pilot-first validation path |
| Deployment posture | Generic cloud dependency | Customer-owned data plane |