Manta Graph™ · Relational Knowledge Graph

See complexity become legible.

Manta Graph™ is a relational knowledge graph for the dimensionality of siloed enterprise data. It maps how people, systems, policies, and evidence actually connect — then proposes typed routes a small local model can run against, before any frontier model is chosen.

Deterministic sample data on GlobalizeWe.com · Authenticated execution on GlobalizeWe.io

Diagnose the estateDIAGNOSE THE ESTATESourceSystemArchiveManta Graph™Token wasteData leakBottleneckOptimize firstMAPDIAGNOSEOPTIMIZE
Diagnose the estate. Sources connect to a Manta Graph hub. From the hub, three leak nodes surface: token waste, data leak, and bottleneck. The path ends at optimize first. Phases: map, diagnose, optimize. Decorative illustration.

Living map: sources surface, diagnosis names the leaks, then what to optimize first.

Four-stage Manta Graph journey connecting enterprise intake, diagnosis, proposed routes, human approval, GW Slate, and Receipt Rail

Direct answer

What is Manta Graph?

A Manta Graph™ diagnostic returns three artifacts you keep: a visual topology of how people, systems, policy, and evidence actually connect; an executable schema engineers can run; and a compute-and-token economics view finance can size. It never grants authority and never mints proof — those belong to GW Slate™ and Receipt Rail™ after the map is legible.

The relational knowledge-graph journey

From fragmented intake to governed proof.

Every stage produces something you can inspect — not a black box.

Stage 01

System Ingestion & Topology Mapping

Connect your systems, content, policy, and people into one map instead of scattered point solutions.

Output: connected knowledge candidates
Stage 02

Diagnostic Pipeline Deployment

See what's duplicated, missing, or stuck — and where the real opportunities sit.

Output: evidence-linked diagnostic
Stage 03

Executable Architecture & Routing Logic

Compare the simplest reliable paths, explain the tradeoffs, and keep named human-at-the-control at the boundary where consequence matters.

Output: governed route proposal
Stage 04

Govern & Prove

Run the approved path through AEA, GW Slate, and Receipt Rail — then feed what you learned back into the graph.

Output: proof-backed learning loop

Interactive explainer · sample data

Run a simulated diagnostic.

Pick a sample, inspect the relationships, compare two routes, and see what happens after approval.

This demonstration is deterministic and educational. Authenticated product workflows, enterprise data, and live execution belong on GlobalizeWe.io.
SELECTED INTAKE

Global program release

A multilingual market program moves from canonical claim to approved, accessible market expressions.

AFFECTED GEO-LOCALES
ES-MXFR-CAJA-JPDE-DE
7sources
34entities
62relationships
11dependencies
2boundaries
2conflicts

Illustrative scoring for the selected deterministic sample.

DETERMINISTIC GRAPH · INTAKE VIEW
SourceContextAuthorityProof
Manta Graph simulated diagnosticA deterministic graph shows sources moving through context, diagnosis, route comparison, human authority, GW Slate, and Receipt Rail.Brand policyDAM mediaTMS memoryMarket evidencePolicy + contextAEA preparationEvidence setSource eligibleDiagnostic mapMeaning + materialityDirect route5 stepsGoverned route8 typed stepsHuman authorityApproval requiredGW SlateGoverned executionReceipt RailProof trail
Select any node to inspect its context, dependencies, review condition, affected geo-locales, supporting evidence, and proposed next action.
SIMULATED ROUTE COMPARISON

Two possible paths. One explicit decision.

Sample measures reflect this deterministic demonstration only.

PATH 01

Direct model sequence

One prepared prompt, one model route, one final manual review.

Workflow steps
5
Evidence-bound
3 / 5
Human gates
1
Open conflicts
2
Disposition: comparison path
PATH 02 · PROPOSED

Governed hybrid route

Structured context, hybrid retrieval, specialist routing, and named approvals.

Typed steps
8
Evidence-bound
8 / 8
Human gates
2
Open conflicts
0
RECEIPT RAIL™ · SIMULATED

View the proof trail.

HUMAN APPROVAL REQUIRED
  1. 01
    Context packet assembledMG-SIM-CP-0042 · 7 eligible sources
  2. 02
    AEA pre-inference gatePolicy v2.2 · context and authority prepared
  3. 03
    Human authorityPENDING · named approval boundary
  4. 04
    GW Slate dispositionHELD · awaiting human authority
  5. 05
    Receipt Rail recordReceipt mints after approval and execution

Global program release sample selected. Intake view active.

Interconnected responsibilities

One responsibility per layer. One governed loop.

Manta Graph proposes. AEA prepares. GW Slate governs. Receipt Rail records.

01
Map + propose

Manta Graph™

Finds the relationships, dependencies, and fit that actually matter.

02
Prepare

AEA™

Identity, policy, and context — ready before a model ever runs.

03
Govern + coordinate

GW Slate™

Coordinates approved models and tools across the stack you already run.

04
Record + verify

Receipt Rail™

A clear record of what happened and why.

Product applications

Relationship intelligence for work that carries consequence.

Multilingual publishing

Keep every localized version tied to the approved claim, terms, and market context.

Locale relationship map

Governed document intelligence

Connect source documents, content, and policy into one searchable, trustworthy asset.

Evidence-bound knowledge path

System interoperability

Map what each system can do, who owns it, and what approval it needs.

Integration relationship map

Geo-local activation

Spot where language, culture, or local rules change what's allowed to ship.

Market activation proposal

Accessibility remediation

Find affected assets, gaps, and the fastest sequence to fix them.

Remediation dependency graph

Workflow prioritization

Weigh delay, rework, cost, and risk before picking how work gets done.

Measured implementation backlog

The Manta Graph diagnostic

Bring one workflow that matters.

01Current-State Intelligence Map
02Exposure & Efficiency Register
03Source-Mode Recommendation
04GW Slate Integration Blueprint
05Diagnostic Receipt Pack
06Implementation Backlog + ROM

Control matched to consequence

Four assurance profiles. One rail.

L0

Assisted

Human-led, logged inputs and outputs, named owner, manual approval.

L1

Governed

Identity, eligible sources, policy checks, scoped tools, decision trace, receipt.

L2

Regulated

Jurisdiction rules, resident data handling, mandatory approval, retention controls.

L3

High-assurance

Isolated runtime, fail-closed controls, signed policy, cryptographic provenance, anchored evidence.

Answer engine reference

Manta Graph questions, answered directly.

01What is Manta Graph?

Manta Graph™ is GlobalizeWe's relational knowledge graph. It maps the dimensionality of siloed enterprise data — people, systems, policies, evidence, decisions, and outcomes — then proposes typed routes a small local model can run against.

02How are Manta Graph and GW Slate different?

Manta Graph maps the work and proposes a route. GW Slate™ decides whether that route can run.

03What does AEA do before inference?

AEA™ — Agentic Enrichment Architecture — prepares identity, policy, and context before a model ever runs.

04What does Receipt Rail preserve?

A record of what sources were used, what was decided, and what happened — tamper-evident by default.

05Does the GlobalizeWe.com experience execute live enterprise work?

No. This experience uses sample data to explain the method. Live work happens on authenticated GlobalizeWe.io deployments.

06How does Manta Graph connect to existing enterprise systems?

It maps relationships across the systems you already use — CMS, DAM, CRM, ERP, identity, models, APIs. Nothing gets replaced.

07How do vectors and Manta Graph work together?

Vector search finds what's similar. Manta Graph resolves how things connect. A hybrid route uses both when a workflow needs each.

08What are the three deliverable tiers of a Manta Graph™ topology mapping?

Tier 1 is a Visual Topology — the workflow map at exec and board depth. Tier 2 is an Executable Schema in JSON, GraphQL, Terraform, and LangGraph-ready form with air-gapped boundaries and API hooks. Tier 3 is the Token Economy Audit — the compute cost of the workflow at scale, broken down by node and model, so finance can size the program.

09How does the Manta Graph™ topology mapping fee apply to GW Slate™ deployment?

The topology-mapping fee credits in full toward GW Slate™ implementation if you proceed within 90 days. The Manta Graph™ topology mapping is the evidence; the GW Slate™ value calculator shows what that evidence is worth across the full program.

10What is a relational knowledge graph?

A relational knowledge graph maps how entities actually connect — people, systems, data, policies, evidence, decisions, and outcomes — rather than storing each silo as a separate list. Manta Graph™ is GlobalizeWe's relational knowledge graph: it makes the dimensionality of siloed enterprise data legible, then proposes typed routes a small local model can run against.

11How does Manta Graph™ help small, local models?

Small local models perform when the context is already shaped. Manta Graph™ maps the data topology first, then proposes typed, evidence-linked routes so a small local model runs against the right subgraph — before a frontier model is ever chosen.

Start your Manta Graph™ today.

Map one consequential workflow, govern it, and scale what works — with proof at every stage.

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