How it works

Context-driven
AI orchestration.
Production-grade.

MCP-native. Policy-gated. Zero training on your data. Hive connects to your stack, builds a knowledge graph of how your business runs, and executes workflows end-to-end — with a structured receipt for every action.

MCP-native SOC 2 in progress EU & UK data residency Zero training on customer data AES-256 · TLS 1.3 90+ connectors

01 — Security & trust

Enterprise-grade security.
Non-negotiable.

Trust is the precondition for handing over operations. Every design decision — data model, agent architecture, approval flow — is built around the assumption that your business is sensitive and consequential.

SOC 2 Type II in progress ISO 27001 Q4 2026 GDPR compliant UK ICO registered

Encryption (rest)	AES-256. Customer keys available on Enterprise.
Encryption (transit)	TLS 1.3 enforced. Certificate pinning on mobile.
Data residency	EU (Frankfurt) and UK (London). Choose at org level.
Model training	Zero. Your operating context is never used to train any third-party model.
Access control	Role-based. Per-tool read/write scopes. Audit on every permission change.
Approval gates	Configurable thresholds per action class. Irreversible actions always require human confirmation.
Audit log	Immutable. Every agent action, every decision, every receipt — structured and queryable.
Data deletion	Instant on request. Operating context model: exportable and deletable.

02 — Architecture

Five production layers.
One coherent system.

Every task Hive executes passes through all five layers in sequence. Each layer is independently scalable, testable, and auditable. No black boxes.

Layer 01

MCP Integration Layer

Model Context Protocol-native connections to 90+ tools. Bidirectional read/write. OAuth 2.0 + scoped API keys. Connection health monitoring with automatic retry and alerting on credential drift.

MCP · OAuth 2.0

Layer 02

Operating Context Model

A knowledge graph updated on every task execution. Entities: workflows, decisions, exceptions, actors, thresholds. Relationships: causal chains, approval histories, escalation patterns. Queried by all agents — shared context, no re-explanation. Graph depth compounds continuously.

Graph DB · Event-sourced

Layer 03

Agent Runtime

Multi-agent execution with structured context handoffs. Each agent is scoped to a single function — reconciliation, classification, generation, routing. Agents query the shared context model. Outputs are typed, validated, and passed downstream as structured objects, not free text.

Multi-agent · Typed I/O

Layer 04

Orchestration Engine

DAG-based workflow scheduling. Tasks execute async, sync, or on trigger (webhook, schedule, event). Built-in retry with exponential backoff. Dead-letter queue for failed tasks. Model router selects the optimal inference tier per task — reducing token spend 40–70% vs. naive single-model approaches.

DAG · Async · Model router

Layer 05

Policy & Receipt Engine

Configurable approval thresholds per action class and amount. Runtime policy evaluation before every write action. On approval: downstream execution is automatic. On completion: a structured receipt is emitted — agent ID, source data refs, output values, timestamp — and written to the immutable audit log.

Policy-gated · Immutable log

03 — The operating context model

The moat is the context.
Not the model.

Any system can call an LLM. Only Hive owns a continuously updated knowledge graph of how your business actually operates — built from real task executions, not documentation.

This graph is the reason the 90th day is fundamentally different from the first. It is also what cannot be migrated to a competitor.

// graph schema (simplified)
Node types: Workflow · Decision · Exception · Actor · Tool · Policy
Edge types: triggers · approves · escalates_to · owned_by · precedes
Updated: on every task execution + human approval event
Access: all agents · read-only by default · write via approved mutations

Context compounds over time

M1 M2 M3 M4 M5 M6

Workflows mapped

Discovered from execution patterns, not documentation

Exception patterns

312

Captured from human approval decisions over 6 months

Auto-approved rules

95%+ consistency threshold. Human confirmed.

The direction

The moat is the context.
Not the model.

Today, Hive orchestrates the best available frontier models — grounded in your operating context. But a context model that accumulates decisions, exceptions, approval patterns, and institutional knowledge over 18 months isn’t just memory for inference. It’s the training signal for something new.

Today

Orchestration

Frontier models grounded in your operating context. Workflows run. Receipts issued. Context compounds.

Specialisation

Context model becomes a fine-tuning dataset. Business-specific agents replace generic ones. Inference cost drops. Accuracy compounds.

Frontier

AGBI

Artificial General Business Intelligence. A frontier model trained specifically on how companies operate — not language, not code, but the operational mechanics of business at scale.

The companies that connect their stack to Hive today are building the context layer that will define what’s possible when AGBI arrives.

Enterprise-grade security.Non-negotiable.

Five production layers.One coherent system.

The moat is the context.Not the model.

The moat is the context.Not the model.

Enterprise-grade security.
Non-negotiable.

Five production layers.
One coherent system.

The moat is the context.
Not the model.

The moat is the context.
Not the model.