Common inference path
- 01Prompt
- 02Model
- 03Response
Technology · Pre-inference architecture
AEA™ — Agentic Enrichment Architecture™
Infrastructure before inference. Trustworthy AI execution begins with identity, policy, context, knowledge, evidence, runtime placement, and human authority established before a model acts.
Bound before executionGW Slate™ control boundary
- 01IdentityWho is acting
- 02AuthorizationUnder whose authority
- 03PolicyWhat may happen
- 04ContextWhat the work may know
- 05EvidenceWhat must be proven
- 06Human authorityWhere judgment remains
Answer
What is AEA™?
AEA™ is GlobalizeWe’s patent-pending architecture for making the conditions around AI execution computable before inference. It prepares the authorized identity, policy, context, knowledge, provenance, evidence obligation, runtime boundary, and human-at-the-control authority that GW Slate™ enforces during execution.
01 · Architecture before inference
The model performs inside an accountable system.
AEA™ turns the conditions surrounding execution into explicit inputs. The result is an operating path that can be governed, inspected, and improved.
AEA™ governed execution path
- 01Identity
- 02Authorization
- 03Policy
- 04Context
- 05Knowledge
- 06Enrichment
- 07Execution
- 08Verification
- 09Receipt
- 10Human authority
02 · GlobalizeWe intelligence architecture
Map → prepare → govern → prove.
Each layer owns a defined responsibility. The relationship among the layers is the entity graph that keeps discovery, execution, and evidence coherent.
01
Manta Graph™
Map the operating reality
Reveals objectives, relationships, systems, dependencies, constraints, evidence requirements, and measurable outcome paths.
02AEA™
Prepare intelligence before inference
Makes identity, policy, context, knowledge, evidence, runtime conditions, and human authority computable inputs to execution.
03GW Slate™
Govern approved execution
Binds, routes, enforces, approves, verifies, and receipts work across approved local, hybrid, sovereign, and frontier environments.
04Receipt Rail™
Preserve proof
Carries decision lineage, policy state, actor identity, runtime claims, verification artifacts, and replayable evidence.
03 · Governed runtime profiles
One architecture. Three approved execution modes.
GW Slate™ applies the runtime profile workflow by workflow, using role, purpose, jurisdiction, data class, risk posture, and policy as decision inputs.
Frontier Cloud
Approved frontier capability routed through GW Slate™ with policy-scoped context and explicit tool boundaries.
Best fit
- Creative workflows
- Public information
- Rapid experimentation
- Low-risk automation
Hybrid Controlled
Resident knowledge remains controlled while approved tasks reach selected model and cloud capabilities.
Best fit
- Enterprise knowledge
- Internal operations
- Mixed-sensitivity work
- Regulated-adjacent use
Sovereign Private
Data, models, identity, policy, logs, and receipts remain inside the client-controlled boundary.
Best fit
- Financial services
- Healthcare
- Government
- Confidential operations
Deployment doctrine
Every consequential workflow runs in the approved place, under the approved identity, with the approved evidence obligation and explicit human authority.
04 · Interoperable by architecture
Standards connect systems. AEA™ carries control across them.
AEA™ is designed for heterogeneous models, agents, tools, identity systems, data boundaries, and proof requirements operating through brand-aligned intelligence rails.
MCP
Model Context Protocol
Connects AI applications to approved context, tools, and workflows through a standardized interface.
A2AAgent2Agent Protocol
Supports communication and interoperability among independent agent systems.
C2PAContent provenance
Provides a technical foundation for certifying the source and history of digital content.
VCVerifiable Credentials
Expresses machine-verifiable claims about identity, role, authority, and status.
DIDDecentralized Identifiers
Supports portable identifiers and cryptographic verification material where a workflow requires them.
05 · Evidence before trust
Trust becomes an inspectable execution chain.
Receipt obligations are defined before work begins. GW Slate™ records each material decision, and Receipt Rail™ preserves the resulting proof object for review, audit, dispute, and operational learning.
- 01Intent
- 02Policy decision
- 03Model selection
- 04Execution
- 05Human approval
- 06Output
- 07Receipt
The resulting evidence answers
- What happened?
- Why did it happen?
- Which policy applied?
- Which model executed?
- Who approved?
- What evidence exists?
Direct answers
Agentic Enrichment Architecture™ — what enterprise teams need to know.
Canonical definitions for enterprise architects, AI leaders, operators, security teams, human decision-makers, and discovery engines.
01What is Agentic Enrichment Architecture™?
Agentic Enrichment Architecture™ (AEA™) is GlobalizeWe’s patent-pending pre-inference architecture for binding identity, authorization, policy, context, knowledge, provenance, evidence, runtime placement, and human authority before an AI system executes work.
02How are AEA™ and GW Slate™ different?
AEA™ defines the computational conditions for accountable execution. GW Slate™ is the modular controller and governed execution runtime that operationalizes those conditions across frontier cloud, hybrid controlled, and sovereign private environments.
03Where does Manta Graph™ fit?
Manta Graph™ maps the objective, systems, relationships, context, constraints, dependencies, evidence requirements, and measurable outcome path. That structured discovery informs AEA™ and the governed workflow GW Slate™ will enforce.
04What does pre-inference mean?
Pre-inference describes the computable decisions made before a model receives an authorized task: who may act, which data may be used, which policy applies, where execution may run, what must escalate, and what evidence the run must produce.
05Can AEA™ support cloud, hybrid, and private AI?
Yes. The architecture remains consistent while GW Slate™ applies the approved runtime profile to each workflow according to role, purpose, jurisdiction, data class, risk posture, and policy.
06How does AEA™ produce evidence?
AEA™ makes evidence an execution condition. GW Slate™ records the policy decision, runtime path, model selection, human approval, material actions, outputs, and verification artifacts, then routes the resulting proof through Receipt Rail™.
Choose one workflow. Define the conditions. Prove the outcome.
A Discovery Dive maps the identity, context, policy boundary, runtime profile, human decisions, and receipt obligations around one consequential workflow.