The first AI that
builds knowledge
,
not just answers questions.
Ontology Generative Engine (OGE) turns any topic or dataset into a structured map of concepts and relationships — powered by a proprietary Ontological, Epistemological & Praxeological Engine that understands what exists, what is true and what you can do next.
Ideal for anyone who needs to see the bigger picture: markets, customers, risks, systems, knowledge.
Not a second brain. Not a mind map. A generative ontology engine that stays simple on the surface and deep under the hood.
Suggested focus: "Mid-size SaaS / APAC" with targeted retention play.
How the Ontology Generative Engine works
In plain language: OGE turns “a lot of information” into “a clear map of how things relate”. Under the hood, an AI-powered Ontological (what exists), Epistemological (what is true), and Praxeological (what to do) Engine keeps your model coherent, useful and actionable.
Define a universe
Start with a simple statement: a domain, a question, a problem. The engine generates an initial structure of key entities and links for that universe.
Benefit: You start with a clear map instead of a blank page.
Ingest data
Import PDFs, transcripts, reports, CSVs. OGE detects concepts, actors, events, metrics and how they connect across everything.
Benefit: Information stops being “a pile” and becomes a connected system.
Organic expansion
As more data flows in, the ontology grows: new classes, refined hierarchies, emerging clusters and structural patterns appear on their own.
Benefit: The more you use it, the smarter and more precise your model becomes.
Interrogate & simulate
Ask questions, surface hidden relations, run “what-if” scenarios and receive narrative answers grounded in the actual structure of your data.
Benefit: You don’t just see data — you understand how it behaves and where to act.
Most tools answer questions by reading documents one by one. OGE builds a reusable map of your domain (entities + relationships) and reuses that map every time you ask something. This makes answers more consistent, more explainable and more powerful as the ontology grows — because the engine is always checking: “does this fit the structure?”, “does this match the evidence?”, “what does this enable?”.
The Engine — Ontological, Epistemological, Praxeological
At the core of OGE there is more than “just an LLM with a graph”. The system is driven by a three-layer Engine that makes the tool simple to use, but extremely deep in how it reasons.
Ontological Engine
Decides what exists in your universe: entities, types, relationships, constraints. It keeps your graph consistent as you add more data, creating a stable “map of the territory”.
Effect: your domain becomes something the AI can navigate, not just read.
Epistemological Engine
Deals with what is true, plausible or conflicting. It cross-checks evidence, highlights gaps and tension points, and can show where information is weak or contradictory.
Effect: you get grounded answers and see where the model is unsure.
Praxeological Engine
Focuses on action: given the current structure and truths, which moves are possible and which have more leverage? It links nodes and patterns to concrete recommendations, playbooks and scenarios.
Effect: the graph doesn’t just describe the world — it suggests where to push and why.
These three layers run on top of modern LLMs and graph technology, but keep the logic of the system independent of any single model. The result is an engine that can evolve, change models, plug in new tools — while preserving your structure and intelligence as long-term assets.
Real-world style cases
OGE is domain-agnostic: it can structure a business problem, a criminal network, or an abstract model of reality with the same engine. Below are three illustrative universes where it changes the game.
Case 1 — 200K Lucrative Problems for entrepreneurs & impact
Together with the Lucrative Problems Framework and the work behind LucrativeProblems.com, OGE can host a global ontology of the top 200K problems worth solving — around 1K per country — scored by profitability and real human pain.
Each problem becomes a node connected to: affected groups, economic context, possible solutions, competitive landscape and UN SDG tags. For example, the UAE layer highlights problems linked to SDG 8 (Decent Work), SDG 9 (Innovation) and SDG 11 (Sustainable Cities), showing where entrepreneurial action can generate both strong revenue and measurable social benefit.
Game-changer: instead of starting from “ideas”, founders start from a live atlas of problems that are already validated as lucrative and aligned with global impact. Entrepreneurship becomes a concrete way of solving one problem at a time — with a clear view of how that compounds into a better world.
Case 2 — Mapping a transnational criminal network
Public data and reports about the Sinaloa Cartel describe leaders, factions, alliances, routes and confrontations — but usually in a linear way. OGE turns this into a structural model: who connects whom, which territories depend on which actors, and where institutional pressure has more leverage.
Instead of reading hundreds of pages, an analyst can interrogate the ontology: “What happens if we remove this broker?”, “Which regions are vulnerable if this alliance breaks?”, “Where do state actors overlap with key corridors?”.
Game-changer: you move from “isolated events and names” to a living network representation, where risk, influence and impact can be explored structurally — without requiring the analyst to design the model manually.
Case 3 — A living map of “how reality is structured”
Instead of treating philosophy, psychology, economics, technology and spirituality as separate topics, OGE lets you encode them as layers of a single ontology. The result is a practical, navigable schema of “how reality behaves from multiple angles.
You can attach concrete examples, models and experiments to each node and relation, then ask: “Which parts of this system am I over-optimizing?”, Where are the neglected leverage points?”, “What is the true cross-layer cost of this path?”.
Game-changer: personal development, strategic thinking and decision-making stop being a set of disconnected techniques and become a single coherent map you can refine over time.
Core capabilities
Not a mind map. Not a notes app. Not “GPT with memory”. OGE is an engine for auto-generated ontologies and graph-native reasoning, built to be simple on day one and increasingly powerful as you feed it.
Auto-Ontology Generation
Turn an objective or a data dump into an initial ontology: classes, relations, properties and a navigable graph — without modeling anything by hand.
Organic Expansion
As you feed more documents, OGE refines, splits, merges and extends the ontology, keeping it coherent and increasingly rich over time.
Graph-Native Reasoning
Questions are answered by traversing the graph, selecting sub-structures and then using LLMs to synthesize explanations and strategies you can act on.
Pattern & Risk Detection
Identify hubs, brokers, blind spots, anomalies and fragile links that regular text-based tools will never surface — in business, security or research contexts.
Scenario Simulation
Remove a node, add a hypothetical actor, disrupt a relation and see how the ontology and its narratives adapt, so you can test moves before making them.
Domain-Agnostic
Customer journeys, markets, criminal networks, climate models, policy spaces, R&D pipelines — OGE does not care about the domain. It learns it from your data.
Applications — 20 places where this changes the game
Because the Engine is ontological, epistemological and praxeological, OGE can be dropped into very different industries and still generate clear value. Here are twenty concrete applications.
1. SaaS churn & LTV optimization
Map signals, cohorts, touchpoints and pricing into a single churn ontology. Value: focus retention moves on the structurally riskiest segments.
2. Hospital patient pathways
Connect conditions, protocols, staff, delays and outcomes. Value: see bottlenecks and risk surfaces before they turn into incidents.
3. Manufacturing supply chain resilience
Model suppliers, routes, components and geopolitical risk as one graph. Value: simulate disruptions and choose more robust configurations.
4. Retail demand & assortment
Link products, stores, demographics, campaigns and seasonality. Value: decide assortment and pricing from a structural view of demand.
5. Climate risk & adaptation scenarios
Combine physical risk, assets, policies and communities. Value: prioritize adaptation projects with highest cross-layer impact.
6. Smart city mobility (e.g. UAE)
Represent flows, infrastructure, behavior and regulations. Value: test mobility policies against a living city ontology.
7. Public policy impact mapping
Connect laws, stakeholders, incentives and unintended effects. Value: see who is truly affected and where resistance or support emerges.
8. University research landscape
Map labs, topics, methods, citations and partners. Value: identify white spaces and potential cross-disciplinary projects.
9. M&A and investment theses
Represent markets, players, moats, synergies and risks. Value: justify deals with a transparent structural map, not just decks.
10. Venture studio & problem spaces
Use the 200K Lucrative Problems ontology to design new ventures. Value: start from validated problems instead of random ideas.
11. Cybersecurity attack graphs
Model assets, vulnerabilities, paths and attacker playbooks. Value: see which chains matter most and where to harden first.
12. Oil & gas asset and risk view
Connect wells, pipelines, contractors, regulations and environment. Value: balance production, safety and compliance structurally.
13. Renewable energy project pipeline
Relate sites, technologies, permits, communities and investors. Value: prioritize projects with better systemic fit and lower friction.
14. Tourism & destination ecosystems
Model attractions, routes, services, seasons and segments. Value: design more resilient and profitable destination strategies.
15. Agribusiness value chains
Represent producers, inputs, logistics, markets and climate. Value: see where margin leaks and where coordination creates upside.
16. Financial compliance & AML
Connect entities, transactions, patterns and red flags. Value: move from rule-based alerts to structural risk patterns.
17. Talent networks & skills ontologies
Link people, skills, projects, outcomes and aspirations. Value: design teams and careers from a dynamic skills graph.
18. Legal precedent & case graphs
Map cases, arguments, decisions and references. Value: surface analogies and doctrinal paths that aren’t obvious in text.
19. Corporate strategy & OKR maps
Connect bets, metrics, teams and dependencies. Value: see how initiatives interact and where misalignment lives.
20. NGO programs aligned with SDGs
Relate projects, beneficiaries, partners and SDG targets. Value: prove systemic impact and find better collaboration routes.
Built for people who think in systems
If your work depends on understanding complex, evolving structures — OGE becomes your unfair advantage.
Intelligence & Analysis
Map actors, regimes, conflicts, networks. Interrogate the structure behind events instead of drowning in raw reports.
Research & Academia
Build living ontologies for fields, literatures, debates, schools of thought and cross-disciplinary questions.
Strategy & Innovation
Model markets, ecosystems, supply chains and threat surfaces. Move from linear roadmaps to structural intelligence.
Join the first wave of Auto-Ontology Intelligence.
We’re onboarding a small group of teams and researchers who deal with complex domains and high-stakes decisions.
Early access comes with direct contact to the core team and influence over the roadmap.