No LLM in the runtime path
Zebric applications render from blueprints. Forms, tables, workflows, and permissions do not depend on an LLM call to function.
Principles
The blueprint is the contract.
Zebric separates AI-assisted creation from application runtime behavior, so teams can move quickly without putting critical operations behind probabilistic execution.
Blueprints are built for generation
The schema gives LLMs a clear target: entities, relationships, workflows, views, policies, and journeys in a structured format.
Prototype fast, then own the result
Use an LLM to draft a solution quickly, then review, version, run, and govern the resulting blueprint like any other operational asset.
AI-assisted workflow Draft, review, run, iterate
Describe the operation
Start with the real process: actors, data, handoffs, approvals, exceptions, and outcomes.
Generate a blueprint
Use an LLM or agent to draft the Zebric blueprint from that operational description.
Review the model
Inspect the generated entities, permissions, workflows, views, and journeys before it ships.
Run and iterate
The Zebric runtime renders the application, and future changes happen through the blueprint.
Common Skills
Useful jobs for AI agents.
Because the application model is explicit, AI can help with more than writing code. It can work directly with the operational design.
Blueprint drafting
Turn a plain-language workflow description into a first-pass Zebric blueprint.
Spreadsheet migration
Infer entities, fields, states, and relationships from existing operational spreadsheets.
Permission review
Check roles, access rules, approval paths, and escalation behavior for gaps.
Journey mapping
Create end-to-end user journeys and diagrams that match the underlying workflow.
Integration planning
Draft integration specs for APIs, events, imports, exports, and external systems.
Test data generation
Create realistic records, edge cases, and workflow scenarios for demos and validation.
Change explanation
Summarize blueprint diffs in operational language before a change is approved.
Documentation
Generate operator docs, admin notes, onboarding material, and system references from the blueprint.
What else this unlocks.
Explicit blueprints give AI a stable surface area for governance, audits, simulation, and change management. An agent can reason about what the system is supposed to do before touching production behavior, and reviewers can approve the model rather than reverse-engineering a pile of one-off UI and application code.