Product & architecture

The Enterprise AI Brain stack.

A distributed AI operating system with intelligence in orchestration, not in centralized storage. Six layers — from the channels people talk to, down to safe execution against your live systems.

L1

Universal Interaction Layer

One unified conversational interface, everywhere people already work.

SlackTeamsEmailWebMobileVoiceIDEsBrowser ext.Terminal
L2

Reasoning & Planning Engine

The "OS scheduler" for enterprise intelligence — decides what to do and how.

Multi-step reasoningTool selectionDynamic planningGoal decompositionMulti-agentMemory mgmtSafety verificationCost optimization
L3

Dynamic Retrieval Engine

Decides which systems to query, which permissions apply, and whether a cache is valid.

Live APIFederated searchSelective semanticEvent-drivenTemp. indexingRetrieval planningContext compression
L4

Universal Tool Runtime

A standardized execution framework that turns every system into a composable capability.

RESTGraphQLgRPCMCPSQLSOAPSDKsBrowser automationRPA fallbackWorkflow engines
L5

Enterprise Memory Fabric

NOT a giant vector DB — a hybrid model where most memory stays transient.

EphemeralSessionOrganizationalSemantic cacheWorkflowAgent
L6

Security & Governance

Enterprise-grade controls woven through every layer of execution.

Real-time ACLCustomer keysAudit logsPolicy engineData residencyPII controlsSOC2 / GDPR / HIPAAZero-trust
Layer 3 · Dynamic Retrieval

Retrieval without heavy ingestion.

Instead of indexing everything up front, the engine decides — per request — how to get exactly the context the task requires.

Live API retrieval

Fetch the current record straight from the system of record — no index in between.

Federated search

Query many systems' native search in parallel and merge results at query time.

Selective semantic

Embed only what the task needs, when it needs it — query-time embeddings, not a global corpus.

Event-driven

Kafka / CDC / webhooks / EventBridge invalidate hot cache and trigger proactive workflows.

Temporary indexing

Build an ephemeral index scoped to a single task, then discard it.

Layer 4 · Universal Tool Runtime

Every system, normalized into a composable capability.

The tool abstraction

Each enterprise system is described once, then orchestrated safely:

Toolinput schemaauth schemaexecution policyACL policyretry semanticssafety constraints

Runtime capabilities

Execution is transactional and policy-aware by default.

Tool callingRetriesRollbacksTransaction trackingApproval gatesPolicy enforcementHuman-in-loop

Protocols

RESTGraphQLgRPCMCPSQLSOAPSDKsBrowser automationRPA fallbackWorkflow engines
Layer 5 · Enterprise Memory Fabric

A hybrid memory model — mostly transient.

Not a petabyte vector store. Memory is scoped to its job and discarded when the job is done.

Ephemeral memoryCurrent task reasoning
Session memoryConversation continuity
Organizational memoryStructured enterprise knowledge
Semantic cacheHot retrieval acceleration
Workflow memoryCross-step orchestration
Agent memoryLong-running tasks
AI infrastructure strategy

Model-agnostic. You choose the model — we own the orchestration.

The platform abstracts the model layer so you can route per task, per tenant, or per policy — including your own hosted weights.

OpenAIAnthropicGeminiAzure OpenAIMistralLlamaDeepSeekCustomer-hosted

See it reason over your systems.

We'll deploy a scoped pilot inside your cloud and connect a handful of your real APIs.