Product
An engine, not a wrapper
Most "enterprise AI" is a chat window in front of someone else's API. VectorBrain is the layer underneath: memory, orchestration, and governance that turn models into a system your organization can actually build on.
Inputs
Your Data & Tools
Docs · Email · CRM · APIs
The engine
VectorBrain
Vector memory · Director · Policies
Outputs
Governed Agent Work
Research · Drafts · Code · Automations
Everything inside the box runs in your environment. Nothing inside it leaves.
Pillar one
Persistent vector memory
The defining difference between a tool your teams tolerate and a brain they rely on: it remembers.
Embeddings that persist
Every document, conversation, and decision is embedded into a governed vector store. Retrieval is semantic, not keyword roulette.
Context that compounds
The brain remembers how your business works: projects, terminology, owners, past decisions. Week one knowledge is still there in year two.
Scoped by design
Memory is partitioned by team, project, and permission. Agents only recall what their caller is allowed to see.
Pillar two
Multi-agent orchestration
One agent answering questions is a toy. A coordinated fleet executing work under policy is infrastructure.
The Director
A coordinating engine that decomposes a goal into a plan, assigns specialist agents, sequences their work, and assembles the result.
Specialist agents
Research, writing, code, design, and automation specialists, each tuned for its job, all sharing one memory and one set of policies.
Tools & integrations
Agents act through governed connectors: email, calendars, document stores, CRMs, internal APIs, and MCP-compatible tools.
Pillar three
Any model. Your rules.
The model landscape changes quarterly; your architecture shouldn't. VectorBrain routes each task to the right model (Claude for reasoning, a fast model for triage, your fine-tuned model for domain work) under policies you set centrally.
- Per-task and per-team routing policies
- API models, private endpoints, or fully local models
- Swap providers without touching a single workflow
FAQ
Product questions
Is VectorBrain a RAG system?
RAG is one capability inside VectorBrain, not the whole story. Beyond retrieval-augmented generation, VectorBrain maintains persistent agent memory that accumulates across sessions, and an orchestration layer that turns that memory into coordinated multi-agent work.
What is a vector memory engine?
A vector memory engine stores information as embeddings (numerical representations of meaning) so AI systems can recall relevant context semantically. VectorBrain combines this with persistence and governance: memory survives across sessions, and access is scoped by role.
Do we need ML engineers to run it?
No. VectorBrain ships as a deployable platform with the embedding pipeline, vector store, orchestration engine, and admin tooling included. Your platform team deploys it like any other internal service.
Can VectorBrain use our existing models?
Yes. The engine is model-agnostic: route tasks to Claude, GPT, or Gemini via API, or point it at models you host internally. Routing policies are configurable per task type and per team.
See the engine on your data.
A 30-minute technical walkthrough: architecture, deployment options, and an honest fit assessment.