The model doesn't know your data
A general-purpose model has never seen your policies, your product catalog, or your support history — so it guesses, and guesses read as confident lies.
SolutionsGround answers in
Ship AI Features
Ground answers in
your own data.
Index your documents, policies, and knowledge base once — every intent call retrieves the right context automatically. No vector-store plumbing, no prompt-window juggling.
Start with your domainKeep ownership and runtime context with the team that operates the capability.
Expose a clear contractIntegrate through a named intent instead of coupling product code to prompts.
Improve from evidenceTest and observe the same execution path your application calls.
Where teams get stuck
The prototype is easy. The operating model is not.
Production problems appear when prompts, provider logic, policy checks, and debugging are split across application services with no shared owner.
Naive chunking wrecks retrieval quality
Splitting documents at fixed character counts cuts sentences in half and buries the right answer in noise — the model gets the wrong passage, every time.
Vector stores are their own project
Standing up embeddings, an index, a re-ranker, and a memory layer is weeks of infrastructure before you've answered a single real question.
Production pattern
A direct path from business outcome to callable AI.
LiyaEngine keeps the capability surface simple while the infrastructure behind it remains governed and observable.
01
Own the capability
Create a tenant-owned domain and define the intent contract your product needs.
02
Connect the context
Link knowledge, tools, model configuration, and policies without changing the application interface.
03
Test the behavior
Run representative requests, review structured output, and inspect the full execution path.
04
Operate with evidence
Use traces, evaluations, usage, and cost context to improve the capability after launch.
How LiyaEngine helps
Move infrastructure out of the product code path.
Retrieval runs automatically inside the engine on every intent call — you never invoke it directly. Documents are chunked at natural content boundaries, indexed, and retrieved with hybrid dense + keyword search.
A cross-encoder re-ranks the top candidates before they reach the model, and multi-tier memory keeps session context alive across turns — all without you managing a vector store.
See the technical specTechnical foundation
Read the full Retrieval & Memory reference
Semantic chunking, hybrid search, cross-encoder re-ranking, and bring-your-own vector store on Enterprise.
Ready to ground your first answer?
Create your workspace, define a domain, and build the first intent around the outcome your team owns.