Flora runs an entirely local, data-sovereign pipeline. Ingestion, embeddings, retrieval, and generation stay inside the perimeter you control, so sensitive knowledge never leaves your network.
Documents are parsed within your network boundary.
Vectors and metadata remain in your Qdrant deployment.
Only authorized context reaches the model.
Each stage is optimized for throughput, latency, and strict control of the data path.
Doctor connects securely to enterprise knowledge bases and parses complex documents inside the local network, so source content never leaves the perimeter during ingestion.
All parsing happens on-premise, with no document egress to external services.
Hugging Face Text Embeddings Inference (TEI) turns cleaned content into highly accurate vector embeddings with near-zero latency, keeping the embedding stage fast enough for high-volume pipelines.
Local embeddings are generated immediately after parsing, without a network hop.
Qdrant stores the vectors at massive scale and supports sub-millisecond search, while retrieval-time RBAC filters narrow the candidate set to only the documents a user is allowed to see.
Access filters are applied before results are ranked or returned.
vLLM produces the final answer on local hardware, using PagedAttention to sustain high-throughput, low-latency inference even as request volume climbs.
PagedAttention keeps GPU memory usage efficient under load.
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