---
title: TopBraid vs mindBrain
date: 2026-05-12
tags:
  - ghostcrab
  - mindbrain
  - topbraid
  - data-governance
  - ontology
---

## The Structural Difference in One Sentence

TopBraid EDG is an **enterprise semantic data governance platform**: its intelligence lives in governed knowledge graphs, asset collections, W3C standards, workflows, lineage, metadata, and enterprise collaboration. [TopBraid EDG](https://topquadrant.github.io/edg-documentation/introduction/index.html)

MindBrain is an **agentic ontology runtime**: its intelligence lives in typed operational state, faceted retrieval, directed dependency graphs, and compact projections exposed to agents through GhostCrab MCP. [GhostCrab](https://www.ghostcrab.be/architecture.html)

![Comparison TopBraid vs mindBrain](ressources/topbraid-vs-mindbrain.png)

***

## What TopBraid Is

TopBraid is TopQuadrant's semantic technology product family. The current comparison point is mostly [TopBraid Enterprise Data Governance](https://topquadrant.github.io/edg-documentation/introduction/index.html), or EDG, not only the older desktop Composer product.

TopBraid EDG is documented as a flexible, web-based solution for data governance across heterogeneous data stores, data processing, and applications. It manages business glossaries, data sources, conceptual models or ontologies, reference data, business applications, policies, and relationships across those assets. [TopBraid EDG](https://topquadrant.github.io/edg-documentation/introduction/index.html)

Its foundation is semantic. EDG is based on interconnected knowledge graphs that express how data is used and managed in an enterprise ecosystem. The documentation lists built-in support for W3C standards including RDF, OWL, SPARQL, and SHACL. [TopBraid EDG](https://topquadrant.github.io/edg-documentation/introduction/index.html)

TopBraid Composer remains relevant historically and technically: its documentation describes it as a complete editor for RDF(S) and OWL models, and a platform for RDF-based components and services. [TopBraid Composer](https://topbraidcomposer.org/html/What_is_TopBraid_Composer.htm)

***

## The Core Problem It Solves

TopBraid solves enterprise semantic governance. Large organizations need a controlled way to define terms, metadata, ontologies, reference data, policies, ownership, lineage, and data quality rules across many systems.

EDG's own feature list emphasizes graphical editing, flexible data and relationship modeling, auditability, version control, collaboration, shared semantics, onboarding repeatability, distribution, data quality, integration, standards, SSO, and enterprise readiness. [TopBraid EDG](https://topquadrant.github.io/edg-documentation/introduction/index.html)

That is a governance problem before it is an agent problem. TopBraid is strong when the question is:

```text
Which business term defines this column?
Which data source feeds this downstream application?
Which policy governs this asset collection?
Who can approve changes to this reference dataset?
How do we publish governed semantic metadata across the enterprise?
```

MindBrain starts from a narrower runtime failure: an agent is trying to work inside a domain and needs durable state, typed dependencies, constraints, and a compact task context. GhostCrab frames the need as a structured project model with entities, stages, dependencies, rules, and evolving state. [GhostCrab](https://www.ghostcrab.be/architecture.html)

***

## Architecture

TopBraid EDG is a governance platform:

```text
Data stewards / SMEs / governance teams
     |
     v
TopBraid EDG web platform
  |-- Asset collections
  |-- Business glossaries
  |-- Taxonomies and ontologies
  |-- Reference datasets
  |-- Policies and governance model
  |-- Workflows, roles, approvals
  |-- Search, lineage, impact, APIs
     |
     v
Enterprise knowledge graph
  RDF / OWL / SPARQL / SHACL
```

EDG organizes assets into collections, and each collection has a type that determines the metadata, functions, imports, exports, reports, and editing applications available for it. [TopBraid EDG](https://topquadrant.github.io/edg-documentation/introduction/index.html)

GhostCrab / MindBrain is an agentic runtime layer:

```text
AI agent
     |
     v
GhostCrab MCP
  |-- Find: facet filtering
  |-- Follow: directed graph traversal
  |-- Pack: compact working context
     |
     v
Structured PostgreSQL project model
```

The TopBraid stack is built for governance teams and enterprise semantic assets. The GhostCrab stack is built for agents that need to navigate project reality in the moment.

***

## Data Schema

TopBraid EDG models governed enterprise assets:

```text
Asset collections
  |-- Business glossaries
  |-- Taxonomies
  |-- Ontologies
  |-- Reference datasets
  |-- Data assets
  |-- Policies
  |-- Governance model
```

EDG's terminology defines assets as technical, business, or operational resources governed by an organization. Collections are technically stored as named graphs, and most asset collections are based on an ontology that defines the schema for the assets they hold. [TopBraid EDG](https://topquadrant.github.io/edg-documentation/introduction/index.html)

The standards matter here. RDF is a graph-based data model for resources and triples. [W3C RDF](https://www.w3.org/TR/rdf11-concepts/) OWL is designed to represent rich and complex knowledge about things and their relations. [W3C OWL](https://www.w3.org/OWL/) SPARQL is the W3C query language for RDF graphs. [W3C SPARQL](https://www.w3.org/TR/sparql11-query/) SHACL describes and validates RDF graphs. [W3C SHACL](https://www.w3.org/TR/shacl/)

MindBrain models operational agent state:

```text
Records
  customer | employee | task | document | policy | experiment

Facets
  status | owner | geography | phase | priority | risk

Directed relations
  REQUIRES | BLOCKS | VALIDATES | DEPENDS_ON | OWNED_BY

Projections
  FACT | GOAL | STEP | CONSTRAINT
```

TopBraid's schema is enterprise semantic governance. MindBrain's schema is agent-usable operational structure.

***

## Signature Mechanism

TopBraid EDG's signature mechanism is governed semantic asset management. Its workflow system lets users make changes in isolated working copies, route those changes through review and approval, and commit approved changes back to production copies. [TopBraid EDG](https://topquadrant.github.io/edg-documentation/user_guide/workflows/index.html)

Its business glossary workflow lets teams add terms, descriptions, business rules, and relationships to other enterprise information such as business activities. [TopBraid EDG](https://www.topquadrant.com/doc/8.1/quick_start_guides/edg_business_glossaries/index.html)

TopBraid Explorer separates curation from consumption: EDG publishes asset collections to Explorer so broader stakeholders and APIs can search, query, and view published information with scalable read access. [TopBraid Explorer](https://topquadrant.github.io/edg-documentation/quick_start_guides/topbraid_explorer/index.html)

MindBrain's signature mechanism is the agent loop:

```text
User asks operational question
     |
     v
Find relevant records with facets
     |
     v
Follow typed dependencies and blockers
     |
     v
Pack FACT / GOAL / STEP / CONSTRAINT context
     |
     v
Agent answers or acts
```

GhostCrab's own onboarding example follows exactly that pattern: filter relevant hires, tasks, documents, statuses, and owners; traverse dependency edges; then pack working context for the agent. [GhostCrab](https://www.ghostcrab.be/architecture.html)

***

## Search / Retrieval / Reasoning Path

TopBraid retrieval is semantic-governance retrieval:

```text
Asset collection
  -> RDF graph
  -> SPARQL / search / APIs / Explorer
  -> lineage, impact, glossary, metadata views
```

EDG's documentation highlights search and filtering, web services and APIs, GraphQL support, Linked Data integration, and standards-based representation. [TopBraid EDG](https://topquadrant.github.io/edg-documentation/introduction/index.html)

MindBrain retrieval is agent-task retrieval:

```text
Facet subset
  -> directed graph traversal
  -> compact projection
  -> agent response
```

The difference is practical. TopBraid can tell an enterprise how terms, systems, policies, and datasets relate. MindBrain is designed to tell an agent what matters right now, what is blocked, what must happen next, and which compact evidence pack should be in context.

***

## Agent Integration

TopBraid EDG provides enterprise integration points. Its documentation covers APIs, GraphQL, web services, imports, exports, remote data sources, and application access patterns. [TopBraid EDG](https://topquadrant.github.io/edg-documentation/introduction/index.html)

That makes TopBraid connectable to agent systems, but its public architecture is not primarily an MCP runtime. It is a governed semantic platform that agents could query through integration work.

GhostCrab is explicitly an MCP server layer. It gives compatible agents a working surface over structured PostgreSQL state and keeps the division of labor clear: GhostCrab provides records, states, relations, dependencies, constraints, and context packs; the agent handles reasoning, conversation, intent, and next-action selection. [GhostCrab](https://www.ghostcrab.be/architecture.html)

***

## Key Design Insight

TopBraid assumes the enterprise's hardest problem is **semantic governance at organizational scale**. It gives humans and systems a governed graph of terms, data assets, policies, models, lineage, workflows, and approvals.

MindBrain assumes the agent's hardest problem is **structured action at task time**. It gives the agent a typed operational map with fast filters, explicit dependency edges, and pre-packed working context.

The tradeoff is not "old ontology tool versus new AI tool." It is governance surface versus runtime surface.

***

## What MindBrain Is

MindBrain is a structured agentic database that makes any domain navigable in real time; its intelligence lives in schema enforcement, typed ontologies, and pre-computed projections that cost zero inference at query time.

GhostCrab exposes that model as an MCP working layer over PostgreSQL. Its three capabilities are find the right slice, follow what connects, and pack the right working surface. [GhostCrab](https://www.ghostcrab.be/architecture.html)

***

## Why Ontologies Beat Flat Data Models

Traditional databases store data in tables — rows and columns with fixed types. That works well for transactional data, but it breaks down fast when your domain is complex, evolving, or needs to be understood by an AI agent. Tables answer *"what is stored here?"* but not *"what does this mean?"*

An ontology answers both.

***

### The Three Building Blocks

**Concepts** are the things that exist in your domain — a `Person`, a `Project`, a `Decision`. Not a table row: a meaningful entity with an identity and a type in a shared vocabulary.

**Semantic Relations** connect concepts with named, directional meaning — `Person` *manages* `Project`, `Decision` *depends_on* `Constraint`. Unlike a foreign key, a relation carries intent: an agent reading the graph knows *why* two things are linked, not just *that* they are.

**Properties** describe the attributes of a concept — a `Person` has a `name`, a `role`, an `expertise_level`. Unlike a column, a property can be typed, optional, multivalued, or inherited from a parent concept.

***

### Axioms — The Rules Layer

Axioms are constraints that make the model self-enforcing:
- A `Decision` *must* have at least one `rationale`
- A `Person` *cannot* manage more than one `active Project` at a time
- A `Skill` *is a subtype of* `Capability`

This is where ontologies go beyond schemas. A SQL table can't express that two concepts are *subtypes* of a shared abstraction, or that a relation is *transitive*. An ontology can — and an AI agent can reason over those rules without being told explicitly.

***

### What This Solves for Developers

| Problem with tables | Ontology solution |
|---|---|
| Schema changes break existing queries | Concepts extend without breaking existing relations |
| Foreign keys carry no semantic meaning | Named relations express *why* things connect |
| No native support for inheritance | Concept hierarchies are first-class |
| Business rules live in application code | Axioms are declared in the model itself |
| AI agents see raw data, not meaning | Agents traverse a graph of typed, named, meaningful nodes |

The practical result: your data model becomes something an AI agent can navigate, query, and reason over — not just a flat surface it has to be told how to interpret every time.

***

## Concrete MindBrain / GhostCrab Workflow

TopBraid governs enterprise semantic assets. MindBrain has a narrower runtime question: once a team has a domain model, how does an agent use imported operational data safely and quickly?

```text
1. Model the runtime domain
   ghostcrab_modeling_guidance or ghostcrab_loadout_suggest
   -> asset, policy, task, owner, obligation, incident, evidence, and blocker types

2. Register or verify the model
   ghostcrab_schema_list / ghostcrab_schema_inspect,
   ontology registration tools, ghostcrab_ddl_propose,
   ghostcrab_workspace_export_model
   -> facets, graph edges, lifecycle states, and projections are explicit

3. Qualify imported data
   MindBrain Studio or an import path maps governed assets, glossaries,
   policies, tickets, documents, ownership records, and source chunks into
   records, entities, relations, facets, evidence links, and projection signals

4. Query after import
   ghostcrab_count / ghostcrab_search / ghostcrab_collection_facet_search for facets
   ghostcrab_graph_search / ghostcrab_traverse for dependency and evidence paths
   ghostcrab_coverage for missing ontology coverage
   ghostcrab_projection_get / ghostcrab_pack for compact working context
```

This does not replace enterprise governance. It describes the handoff from governed meaning to agent work. Once Studio or an import pipeline qualifies the data into a MindBrain workspace, the agent can ask deterministic questions such as which policy obligation blocks a project, which owner controls the next action, which evidence is missing, and which projection should feed a dashboard or work queue. [GhostCrab](https://www.ghostcrab.be/architecture.html)

***

## Taxonomy Cost and Expected Gain

The taxonomy cost is justified when semantic governance needs to become operational execution: repeated workflows, evidence trails, owners, approvals, blockers, lifecycle states, cross-domain joins, and dashboards that agents will query more than once. The gain is not "more ontology." The gain is that imported data becomes a reusable operating surface: counted, filtered, traversed, checked for gaps, and packed into task context.

It is not justified for every semantic project. If the priority is enterprise glossary governance, approval workflows, lineage, standards publication, or broad data-steward collaboration, TopBraid remains the stronger center of gravity. If the job is only a small one-off search over documents, a lighter memory/search tool will be faster. MindBrain fits when governed meaning must drive real-time agent action.

***

## Why Try MindBrain First

TopBraid is a serious enterprise governance platform. MindBrain should be tested first when the question is not "how do we govern semantic assets?" but "how do AI agents use semantic structure while doing work?" Governance defines the map; MindBrain turns maps into deterministic operating surfaces.

The key difference is multi-ontology execution inside one workspace. MindBrain can keep ERP, CRM, HR, project, legal, finance, and knowledge ontologies distinct, then connect them through meta-ontologies. That creates access paths across application silos that are usually invisible to a single tool: an invoice status can meet a CRM warmth score, a project blocker, a policy constraint, and the owner who can resolve it.

MindBrain DDL is **Domain Definition Language**. It defines what things mean, how they relate, which facets can filter them, which graph edges connect them, and which projections should be materialized. Those projections can power dashboards, kanban boards, agent queues, and graph views of live project state, not just governance reports.

For agent systems, this determinism is the product. Instead of sending a model across a large catalog of records, APIs, and documents, facets narrow the candidate set in milliseconds and graph traversal follows typed relations. The Professional tier's bitmap-scale design, roughly 4.3 billion addressable objects per table, matters because semantic governance becomes directly usable at operational scale. [GhostCrab](https://www.ghostcrab.be/architecture.html)

***

## Head-to-Head: MindBrain vs TopBraid

| Dimension | TopBraid EDG | MindBrain |
|---|---|---|
| Core abstraction | Enterprise semantic governance platform | Structured agentic database |
| Primary use case | Govern data assets, glossaries, taxonomies, ontologies, policies | Help agents navigate live domain state |
| Main user | Data governance teams, stewards, enterprise architects | Operators, builders, practitioners using agents |
| Data model | RDF graphs, OWL, SHACL, SPARQL, asset collections | Typed records, facets, directed relations, projections |
| Workflow model | Review, approval, working copies, production copies | Task-time state, blockers, dependencies, next actions |
| Search path | Semantic search, SPARQL, APIs, Explorer, lineage, impact | Facet filtering, graph traversal, context packing |
| Agent interface | Integratable through APIs and semantic services | MCP-first GhostCrab surface |
| Governance strength | Strong enterprise controls and collaboration | Lightweight compared with full EDG governance |
| Runtime strength | Strong as governed semantic backbone | Strong as real-time agent working memory |
| Best fit | Enterprise metadata, data catalog, glossary, ontology governance | Project coordination, compliance work, CRM, onboarding, research operations |
| Weak fit | Minimal local agent runtime | Full enterprise data-governance program |

***

## The Structural Difference in One Sentence

TopBraid EDG governs enterprise semantics so organizations can control meaning across systems; MindBrain operationalizes typed semantics so agents can find, follow, and act inside a live domain without reconstructing structure from prose.
