05 / Guardrails
Enrich
Apply hallucination guardrails, repair references, enrich context, and inject entity anchors before retrieval.
Hallucination GuardrailsEntity Anchors
Enterprise AI Foundation
Build an EnterpriseAI Foundation
ZGI ingests, cleans, and structures enterprise data and documents, then uses knowledge graphs, multi-recall, and workflow orchestration to build runnable agent systems.
Low-Code Data Foundation
LLM Foundation
Knowledge & Recall
Agent Runtime
Graph & Recall
Graph x Multi-Recall
Drive retrieval and execution.
Data Foundation
Graph & Recall
LLM Foundation
Agent Runtime
Graph & Recall
Document ETL, vector stores, knowledge graphs, and multi-recall operate as one retrieval core.
Document ETLVector DBKnowledge Graph
Runtime Signals
TraceableTrustedGoverned
Data
Stage
Graph & Recall
Stage
LLM
Stage
Agent
Stage
Document Intelligence Pipeline
Turn Raw Documents IntoAI-Ready Knowledge
Through multimodal parsing, semantic chunking, four-dimensional scoring, and hallucination guardrails, complex documents become knowledge ready for retrieval and execution.
Not direct chunking and indexing.
Parse, segment, score, repair, then write into vector and graph-ready knowledge.
Mainline
Documents → Knowledge → Recall → Execution
Human Review LoopRules & Learning Loop
Workflow Capabilities
Workflow Nodes, Tools, and Agent Orchestration
Build complex execution paths with nodes, plugins, retrieval, code, and runtime controls on one workflow canvas.
This is not just a canvas of blocks. It is a governed execution system for complex tasks.
Node GraphPlugin SurfaceRuntime Control
Capability Surface
Compose complex tasks with nodes, variables, and branching
Build execution graphs with prompts, retrieval, variables, branching, loops, and answer nodes on one canvas.
Drag, connect, and configure nodes with conditional branches, loops, and structured variables
Keep knowledge retrieval, tool calls, code transforms, and LLM inference inside one execution graph
Visual execution graphNode-level debugging
Canvas
One workflow surface
Node Graph
Model, tool, code, answer
Node Graph
Start
Knowledge
GraphRecall
Answer
Tool Call
Review
LLM Gateway
LLM Gatewayfor Governed Model Runtime
ZGI connects global and private models, then brings routing, budgets, permissions, logs, and traceability into one governed runtime.
One Gateway. Governed Runtime.
Not just model access, but routing, budgets, permissions, and traceability brought into one governed runtime.
LLM Gateway
Unified model control surface
Governed Runtime
01
Model Access Layer
Expose one OpenAI-compatible interface while adapting global and private model providers behind the gateway.
OpenAI CompatibleGlobal Models
02
Routing & Fallback
Route by model, tag, or latency target, then fail over automatically when a primary endpoint is degraded.
RoutingFallback
03
Token & Budget Control
Track token usage, cost, quotas, and spend thresholds across organizations, workspaces, and keys.
Token StatsBudget
04
Permissions & Audit
Apply model access policies, preserve request logs, and keep every admin change and runtime call traceable.
IAMAudit Trail
Access
Global model access
Governance
Budgets, permissions, and logs
Traceability
Auditable runtime trail
Enterprise Controls
Built for Enterprise Control, Not Just AI Demos
From private deployment and workspace isolation to model routing and runtime records, ZGI provides the control surface required for production.
Deployment
Private deployment, isolated environments, and enterprise network access.
Access Control
Workspace permissions, datasource visibility, and tenant-level governance.
Model Governance
Global model access, channel routing, billing, and quotas.
Observability
Workflow runs, node execution logs, usage metrics, and operational visibility.
ZGI
Production Ready
Enterprise AI should not stop at the point where the demo works.
ZGI brings private deployment, workspace isolation, SQL audit trails, model quotas, and runtime records into the production layer together.
Build with ZGI
Trusted Knowledge. Controlled Execution.
For teams that need private deployment, governance, model control, and auditable execution paths.