We think retrieval should show its work.
Most AI search treats an answer as a finished product, you get a sentence and have to trust it. LumIntelligence treats an answer as a claim with a receipt: every result traces back through a graph to the exact source text that produced it.
The three-tier network
Content is ingested as immutable Sources. We discover Entities mentioned inside them (a process we call NoD, "network of discovery"), and tag each entity into an atomic Concept. Every edge points upward, so tracing from any tier back to its Sources is one uniform graph operation, never a guess.
"Querying matches entry points in the vector store, entity or concept, traces each one to its Sources, and dedupes. The answering policy is the caller's choice; the lineage never is."
Bring the model you trust
LumIntelligence is provider-agnostic. Connect your favorite online LLM provider, or run entirely offline with a local model, consolidation thresholds and retrieval cutoffs are tuned per embedder, so switching providers doesn't mean re-tuning your whole pipeline.
Online providers
Connect a hosted LLM and embedding API of your choice for fast, sharp retrieval.
Local providers
Run entirely offline with a local model when full data control matters most.
Where it's proven
We built LumiContacts, a personal contacts app, entirely on top of LumIntelligence, to make sure the platform holds up under a real, opinionated product, not just a demo. Its LumiSearch conceptual search, traceable result cards, and trend dashboards all run on the same graph described above.
See how LumiContacts uses it