The Persistent Runtime For
retrieval, memory, and agent
execution
Vector platform unifies native vector search, graph memory, hybrid retrieval, workflows, and deployment control in one system — built for teams running production AI.
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BVector
Most AI systems are assembled from disconnected tools.
A vector database for retrieval. A graph for relationships. A workflow engine. A separate layer for memory. The result is predictable — more glue code, more latency, less coherence.
The hidden cost of integration.
Every additional system adds setup, synchronization, and operational overhead. Teams end up maintaining glue code instead of building intelligence.
Context fades too fast.
Most agents retrieve context for the moment, then lose structure, plans, and continuity. Long-term reasoning becomes impossible.
Latency kills coherence.
Knowledge moves across service boundaries, slowing responses and creating fragmented state. Every hop adds delay and drift.
We collapsed the stack into one persistent runtime.
Instead of bolting AI onto databases and orchestration layers, BVector makes retrieval, memory, graph structure, and execution native to the same system.
Less glue code. Lower latency. Full control.
Everything agents need, in one runtime.
BVector gives you the primitives required to build production AI systems without composing them from disconnected infrastructure.
Native RAG
Ingest, index, retrieve, and serve grounded responses from a single system designed for production use.
Hybrid retrieval
Combine semantic, keyword, and structured retrieval without routing requests across separate services.
Graph memory
Store entities, relationships, plans, and context as first-class knowledge inside the runtime.
Persistent state
Retain memory and execution context across sessions, workflows, and time.
Workflow execution
Run planners, validators, and executors in the same runtime that stores and retrieves the knowledge they use.
Audit and governance
Inspect retrieval paths, runtime decisions, and actions with access control built in from day one.
From knowledge to action in one loop.
BVector is designed for AI systems that need to retrieve, reason, execute, and remember — not just answer once and forget.
Ingest()
Upload documents, stream events, connect data sources, and organize knowledge for runtime use.
Index()
Store information natively across vector, graph, and searchable forms inside the same substrate.
Retrieve()
Use hybrid retrieval to fetch the right context with speed, precision, and structure.
Reason()
Plan against current context, persistent memory, and graph relationships.
Execute()
Run validators, workflows, and actions from within the runtime.
Remember()
Persist outcomes, state, and evolving knowledge for future tasks and decisions.
Not a chain of disconnected calls. A continuous runtime for intelligent systems.
Hundreds of templates by the world's top designers
Choose from templates from word-class storytellers at Apple, McKinsey, BCG, IDEO, and beyond. Or upload your own to generate tailored proposals, updates and reports.
Quarterly Business Review
Proposals, pitch decks, and pipeline updates.
Control the runtime,
not just the chat window.
A serious AI system needs more than a prompt box. BVector includes the interfaces teams need to ingest knowledge, inspect behavior, govern access, and deploy useful experiences.
Built for teams running
real AI systems.
BVector is designed for organizations that need more than a hosted chatbot or a stitched-together retrieval stack.
Enterprise knowledge agents
Build grounded assistants over internal knowledge with auditability, access control, and private deployment options.
Agentic workflows
Run systems that retrieve, plan, validate, and execute against enterprise data and tools.
Operational copilots
Support internal operations, support teams, and productivity workflows with fast retrieval and durable memory.
Private AI deployments
For organizations that cannot rely on cloud-only AI infrastructure or external data movement.
Memory-rich domain systems
Create applications that need persistent context across entities, events, sessions, and time.
Retrieval-heavy products
Power customer-facing experiences where latency, ownership, and retrieval quality matter.
Designed as a system, not assembled as a stack.
When retrieval, memory, graph structure, and workflow execution live in separate systems, coherence and responsiveness suffer. BVector treats intelligent behavior as a property of the runtime itself — making AI systems simpler to operate, easier to inspect, and better suited for real workloads.
Co-located intelligence
Retrieval, memory, and execution live where the knowledge already resides.
Persistent semantic structure
Entities, relationships, plans, and runtime state remain queryable over time.
Operational simplicity
Fewer moving parts means fewer sync paths, fewer failure modes, and less glue code to maintain.
Deployment ownership
Run the system on-prem, in private cloud, or in managed environments according to your requirements.
Deploy with the control enterprise AI actually requires.
AI infrastructure should adapt to your constraints — not the other way around. BVector supports deployment models for teams that care about security, control, and ownership.
On-prem
Run entirely inside your infrastructure boundary for maximum control and data ownership.
Private cloud
Deploy in controlled cloud environments without relying on a cloud-only AI stack.
Managed deployment
Adopt faster with a simpler operating model while preserving a production-grade architecture.
Available now. Expanding toward a full agent runtime.
We are launching in deliberate phases so each layer of the platform can stand on its own and mature in production.
Foundation
RAG pipeline, native vector index, hybrid retrieval, MCP integration, ingestion UI, audit, access control, chat interface, and on-prem or cloud deployment.
Each phase adds capability. The architecture remains one system.
Enterprise-Grade Security & Compliance
Our AI platform combines intelligent search, vector indexing, and enterprise-grade security to deliver reliable, scalable, and compliant AI experiences.
SOC 2 Type II
Independent verification of our security controls for data protection, infrastructure reliability, and secure AI workflow operations.
ISO 27001
Internationally recognized information security framework ensuring structured risk management, access control, and operational security practices.
Google CASA Tier 2
Validated secure handling of Google user data for AI-powered indexing, vector search, and retrieval-augmented generation workflows.
OAuth 2.0
Delegate access across services with fine-grained scopes. Supports PKCE flow, refresh token rotation, and per-resource permission models.
Multi-Factor Authentication
Enforce MFA across all user accounts with support for authenticator apps, hardware security keys, and SMS fallback for compliance-driven environments.
Full operational visibility.
From audit trails to usage attribution — every tool your platform team needs to operate, observe, and optimize.
Built for
Audit Logs
Every action, timestamped and tamper-evident.
A complete, immutable record of every API call, configuration change, and data access event across your organization. Filter by actor, resource, time range, or outcome — and export directly to your SIEM for compliance audits.
- Immutable log stream with cryptographic integrity checks
- Filter by user, resource type, IP address, and outcome
- Configurable retention periods per compliance tier
- Native integrations: Splunk, Datadog, and Elastic
Transparent pricing that scales
Choose the perfect plan for your needs. Always flexible to grow with you.
Want the architecture first? Start there.
If you're evaluating systems for production AI, begin with the runtime model, deployment approach, and core primitives — then see how the product fits your stack.
Quick install
Own your AI runtime.
Run retrieval, persistent memory, graph reasoning, and agent execution inside one production system — with the control, auditability, and deployment flexibility your team needs.