Vector Memory
Memory your agents can actually rely on.
A persistent context layer that unifies user profiles, knowledge, and history into one graph — queryable in under 300ms. Bring your data; our engineers build the structure that makes it useful.
Vector Memory · context layer
Sources
Retrieved · top 4
p95 recall
248ms
target < 300ms
Isolation
<300ms
p95 recall
1
graph, all your data
Multi-tenant
isolation by default
Owned
in your VPC
Why a memory layer
Vector DBs aren’t enough.
Most agents stitch together a vector DB, a graph, a cache, and a few RAG scripts — then watch quality drift in production. Vector Memory is the operated layer above all of that.
01
Hybrid retrieval, not just vector
Graph relationships, vector similarity, and lexical match — merged into one ranked result. Agents get the right context, not the closest cosine match.
02
Updates and deletes are first-class
Memory evolves. Update facts, deprecate old context, merge entities, honour right-to-erasure — without rebuilding the index.
03
Multi-tenant isolation, by default
Per-tenant namespaces, row-level access, scoped retrieval. Built for B2B from day one.
Plain RAG vs. Vector Memory
Same query · same dataPlain RAG
- One vector store per data type
- Top-k from cosine similarity
- No relationship awareness
- Deletes & updates require re-index
Vector Memory
- One unified graph
- Hybrid graph + vector + lexical
- Entity relationships preserved
- Updates & deletes, no rebuild
Capabilities
What you get out of the box.
Built and operated by engineers — not a SaaS console you have to configure for a year.
Unified memory graph
Profiles, conversations, knowledge bases, and tool outputs live in one connected graph — not five disconnected vector stores.
Sub-300ms recall
Hybrid retrieval (vector + graph + lexical) tuned for agent loops, where every millisecond of latency compounds.
Structured + unstructured
Native support for typed entities, relationships, and free-form documents — without forcing your data into a single shape.
Multi-tenant isolation
Per-customer namespaces, row-level access controls, and tenant-scoped retrieval baked in — not a configuration afterthought.
Updateable, not append-only
Memories evolve. Update facts, deprecate old context, and merge entities without rebuilding the whole index.
Runs in your VPC
Deploy on your cloud account so sensitive data never leaves your perimeter. We bring the engineering, you keep the keys.
Where it runs
Inside your VPC. Your keys. Your data.
No copy of your data on our infrastructure. Vector Memory deploys in your cloud account; our engineers operate it inside your perimeter.
01
Your cloud account
AWS / GCP / Azure. We deploy via your IaC, with the auth and audit your team already trusts.
02
Encrypted everywhere
In transit and at rest. Your KMS keys; our engineers never see plaintext customer data.
03
Engineers on-call
A named team operates it. Not a portal that says "open a ticket" when something breaks.
How it works
From source data to live recall.
Ingest
Connect your sources — CRM, support tickets, docs, transcripts. We model the entities and relationships that matter to your agents.
Embed & link
Content is embedded, entities resolved, and the graph stitched together. Hybrid indexes are tuned to your retrieval patterns.
Query
Agents call a single API; the layer routes between vector, graph, and lexical retrieval to return the right context fast.
Improve
Every interaction is logged, evaluated, and fed back. Engineers tune retrieval and resolve drift before it affects users.
Use cases
Where teams put it to work.
01
Customer-aware support agents
Voice and chat agents that know the customer’s history, plan, and last three tickets — without a 15-second lookup.
02
Sales & RevOps copilots
Agents that pull live account context across CRM, email threads, and product usage to draft outreach grounded in reality.
03
Internal knowledge agents
Long-lived agents that learn your company’s vocabulary, processes, and decisions — and stay current as those change.
Frequently asked
Honest answers.
No certification claims, no marketing fluff. If we don’t do something, we say so.
No. Vector Memory combines vector search with a typed entity graph and lexical retrieval, plus the engineering to keep it accurate. A vector DB is one component; the layer above it is what actually makes agents reliable.
Get started
Stop stitching memory together. Start using one layer.
A 30-minute call with a engineer. We’ll look at your sources and sketch a graph that fits your retrieval patterns.