Production outcomes
Start with the outcome your team needs.
Move from a business requirement to a domain-owned, testable AI capability without assembling a separate infrastructure stack for every use case.
Solution paths
Solve the production problem, not only the demo.
Each solution uses the same core operating model, then emphasizes the controls and evidence needed for that outcome.
The operating model
Clear boundaries from idea to production.
Keep business ownership, application contracts, runtime controls, and operating evidence connected.
Domains define responsibility.
Organize intents, linked knowledge, tools, and shared behavior around the team or product area that owns them.
Intents define the contract.
Give application teams a named, schema-bound capability instead of a provider-specific prompt integration.
Evidence supports iteration.
Use testing, traces, policy decisions, and runtime context to improve behavior after the first release.
Built for more than one team
One platform, many domain boundaries.
Product, operations, support, education, healthcare, financial services, and compliance teams can each own their domains. These are examples, not a fixed catalog of supported use cases.
Create custom domains around your organization
LiyaEngine supplies the operating layer while your tenant supplies the business context and production configuration.
Define intents for the capabilities each domain exposes
LiyaEngine supplies the operating layer while your tenant supplies the business context and production configuration.
Manage knowledge, agents, workflows, and controls in the owning context
LiyaEngine supplies the operating layer while your tenant supplies the business context and production configuration.
Turn the next AI requirement into an owned capability.
Create a workspace, define the domain, and keep the contract, controls, and execution evidence together.