Decision AI Architecture | Ontology & Reasoning

Powering 30,000+ Business Decisions

Trusted by Chiefs of Staff, Strategy teams, and GTM leaders

Recognized by NVIDIA as an AI-driven startup shaping the future of intelligent decision-making.

NVIDIA Partner

01 · Enterprise Context Layer

Ontology Studio builds Data, Domain & Decision Ontology.

Ontology Studio uses Ontology Agents to extract data ontology, import or extend domain ontology, and build decision ontology from unstructured enterprise context, so reasoning is grounded in how your business actually works.

Ontology Studio · State of Art · AI Teams

Three Ontologies. One Causal State Graph.

Ontology Studio builds a living causal model of your enterprise across data, domain knowledge and decision history. Agents do the extraction. Your team governs what gets used.

Ontology Agents

01

Data Ontology

Metrics, schema and lineage

Agents extract source systems, metric definitions, schema and data lineage automatically. AI understands what gross_margin_pct means in your business, not just how to spell it.

MetricsSchemaLineageConventions

02

Domain Ontology

Industry rules and business logic

Import semantic models, brand hierarchies, channel structures and operating workflows. The context your business runs on, formalised and machine-readable.

BrandsChannelsWorkflowsRules

03

Decision Ontology

Goals, logic and decision history

Extract decision goals, reasoning logic, policies and outcome history from Slack, PDFs and AOPs. Decisions become institutional knowledge, not lost meeting notes.

GoalsPoliciesHistoryOutcomes

Built for reasoning, not just retrieval. The foundation that makes zero-hallucination AI possible and the reason DecisionX answers why, not just what.

02 · Data Ops Agents

Enterprise AI should not require months of data engineering.

Data Ops Agents handle the messy reality of enterprise data: formats, files, lineage. Ontology Agents turn that cleaned foundation into structured business meaning, ready for reasoning.

Metadata extraction Semantic joins Ontology generation

Metadata ExtractionStep 01

Understand source systems, however messy.

Agents infer tables, fields, business entities, descriptions and lineage automatically. Works across structured databases, unstructured files, Excel exports and third-party agency data, regardless of format.

TablesFieldsEntitiesDescriptionsLineageMessy formats

Human review available at every step

Semantic JoinsStep 02

Connect fragmented context.

DecisionX maps relationships across systems, files, planning documents and business workflows, building a unified semantic layer from scattered sources.

SystemsFilesPlanning docsWorkflows

Human review available at every step

Ontology GenerationStep 03

Build enterprise context in hours, not months.

What used to take 4 to 5 months of manual ontology work, done autonomously in under an hour.

Ontology Agents extract business meaning and propose enterprise-ready structures for review. Data, Domain and Decision ontology built automatically, grounded in your actual systems.

Business meaningStructuresOntologyReview

Human review available at every step

Human Review Step 04

Govern every ambiguity.

Conflicts are surfaced continuously for human approval so enterprise context stays accurate, explainable and in your control at every point. Agents surface the decision. Humans make it. Every resolution is logged with a full audit trail.

ApprovalExplainabilityGovernanceControlAudit trail

03 · Finite State Enterprise Model

Most AI systems analyze snapshots. DecisionX reasons over state transitions.

DecisionX models enterprise workflows, entities and operational transitions as finite state systems, enabling AI to understand how business states evolve over time.

Step 01

Signal Detected

A metric, event or business condition changes, triggering the reasoning engine to begin evaluation.

Step 02

State Classified

The system classifies the entity: healthy, at-risk, blocked, escalated, or optimized.

Step 03

Transition Reasoned

The system evaluates why the state changed, tracing causal dependencies and impact paths.

Step 04

Decision Triggered

A recommendation, intervention or escalation is generated based on the reasoned transition.

Step 05

Workflow Executed

An agent coordinates the action across connected systems, closing the loop automatically.

Step 06

Outcome Learned

The result updates the model's future reasoning, making each cycle more accurate than the last.

State-aware AI is the difference between answering questions and operating the enterprise.

Multi Layer Reasoning Engine

Multiple reasoning systems working together.

DecisionX routes enterprise questions dynamically across specialised reasoning systems based on the decision required, instead of relying on a single LLM response.

Layer 1

Statistical Reasoning

Trend analysis, anomaly detection, correlations and performance pattern recognition.

Trend analysisAnomaly detectionCorrelations

Layer 2

Causal Reasoning

Attribution, root cause analysis, dependency tracing and impact paths.

AttributionRoot cause analysisImpact paths

Layer 3

Predictive Reasoning

Forecasting, scenario simulation, next-state prediction and risk modelling.

ForecastingScenario simulationRisk modelling

Layer 4

Strategic Reasoning

Prioritisation, investment evaluation, resource allocation and trade-off analysis.

PrioritisationInvestment evaluationTrade-off analysis

Layer 5

Operational Reasoning

Workflow actions, approvals, escalations, interventions and execution monitoring.

Workflow actionsEscalationsExecution monitoring

05 · Decision Memory + Learning

Decisions become reusable enterprise intelligence.

DecisionX stores each decision as a structured object and feeds outcomes back into the ontology, creating a compounding intelligence loop that improves with every cycle.

Decision Object DX · 2024·0041

GoalWhat are we trying to improve?

ContextWhat data and signals matter?

ReasoningWhy is this happening?

DecisionWhat was chosen?

OutcomeWhat changed?

LearningHow should future reasoning improve?

Goal

Define the improvement target

Context

Surface the relevant signals

Reasoning

Trace the causal chain

Decision

Record what was chosen and why

Outcome

Measure what actually changed

Learning

Feed result back into the ontology

Compounds into next cycle

Every decision DecisionX makes becomes a permanent record. The more it runs, the sharper the enterprise gets.

Security & Governance

AI you can trust and defend.

Every decision DecisionX surfaces is explainable, traceable and auditable. Built from the ground up for enterprise governance, compliance and regulated environments.

Explainability

Every answer shows its working

Summarised, deep dive and trail. Every answer is grounded in your data.

Traceability

Full trail on every response

Source files, reasoning chain, inferences and executed SQL, fully logged.

EVALs

Continuous evaluation

Accuracy and grounding measured continuously, with drift alerts built in.

97%Ontology

94%Query

Security & Compliance

Enterprise-grade, from day one

Built for regulated enterprise environments.

RBAC, IAM & SSO Public / private cloud Multi-LLM governance Full audit trail

Enterprise Decision AI

Build the architecture for
enterprise decision AI.

See how DecisionX combines ontology, finite-state modelling, multi-layer reasoning and decision memory into a governed enterprise AI system.

Ontology Studio Finite State Modelling Multi-Layer Reasoning Decision Memory Human Governance

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Technology built for Enterprise Decision Accuracy.

Powering 30,000+ Business Decisions

Ontology Studio builds Data, Domain & Decision Ontology.

Three Ontologies. One Causal State Graph.

Enterprise AI should not require months of data engineering.