Vector storage and search built for RAG-scale retrieval - fast, filterable, and deployable where your data has to live.
Retrieval quality is half of every serious AI system. VectorStore is our line on embedding storage, hierarchical and inverted indexing, and low-latency similarity search for enterprise RAG, recommendations, and long-context workflows - including self-hosted and dedicated deployments when residency requires it.
Managed vector services get expensive and residency-awkward as corpora grow.
Naive indexes lose the latency/recall tradeoff under high-dimensional enterprise embeddings.
RAG without fast, filterable vector search falls back to weak keyword search.
→Index research: hierarchical clustering and inverted structures tuned for high-dimensional embeddings.
→Horizontal scale and elastic resources for bursty query loads.
→APIs/SDKs for RAG pipelines; hybrid paths with lexical retrieval (as used in Ask Symbiosis and Knowledge).
→Deploy cloud or on-prem-shaped dedicated instances.
How the pieces fit
EMBEDDING STORE
Compact storage for high-dimensional vectors with metadata filters.
ANN INDEX LAYER
Approximate nearest-neighbor structures tuned for recall/latency targets.
QUERY PLANNER
Combines vector similarity with metadata predicates and hybrid lexical signals.
OPS PLANE
Snapshots, re-index jobs, and capacity controls for production corpora.
- How do we keep indexes honest as embedding models change under the corpus?
- What observability makes retrieval failures as debuggable as application errors?
→Backing store for Knowledge, Ask Symbiosis, and document-intelligence pipelines.
→A path to residency-friendly retrieval without giving up performance.
CRAFT
Fine-tune with retrieval in the loop - models that learn from your documents, not just generic next-token habit.
LLM FUSION
Route work across models like a specialist team - pick by task, merge outputs, keep guardrails in the path.
EXTENDEDREACH LLMS
Stretch useful context windows and industry-tuned models past default chat limits - for estates that do not fit in a prompt.