Data lookup goes through traditional, 100% reliable database queries — SQL, JSONB, NoSQL, or a Rule Engine. The LLM only ever reasons over that verified data. No hallucinated facts, no vector database required.
Data lookup and reasoning are two different jobs. TotalApp never lets an LLM do both at once — Tier 1 finds the exact data, Tier 2 reasons over what Tier 1 already verified.
This architecture is built for data with a defined schema and clear rules — an invoice, a tax calculation, a permission check. For unstructured documents and open-ended text, Semantic Search is the right tool instead.
If a user's machine has no local model (Ollama or similar) and no GPU available, no screen locks up, crashes, or becomes unusable. Three architectural guarantees make that true.
Traditional queries own the facts. The LLM owns the reasoning. Neither one does the other's job.
Tier 1 uses exact-match queries against your real schema — there is no approximate or "close enough" result to get wrong.
The LLM only ever reasons over data already verified correct by Tier 1 — it cannot introduce facts that aren't there.
No embedding pipeline, similarity index, or vector store to build, tune, or keep in sync for this class of query.
Tier 1 works with whichever deterministic store fits the data — no lock-in to a single database technology.
Tier 2 can interpret data in plain language, audit it like a linter, or support a decision — always from verified facts.
Invoice and tax calculations, RBAC permission checks — any operational workflow with a fixed schema and firm rules.
Use this architecture for structured records, Semantic Search for free-text discovery — combine both in one workflow.
Core CRUD operations never depend on AI. Missing a local model just routes Tier 2 to a cloud service, or disables only the AI buttons.