Technology · DecisionX Architecture

Technology built for Enterprise Decision Accuracy.

DecisionX combines enterprise context, finite-state business modelling, multi-layer reasoning and decision memory to deliver grounded, explainable enterprise AI.

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Layer 01
Enterprise Context
Layer 02
Data Ops Agents
Layer 03
Finite State Model
Layer 04
Multi-Layer Reasoning
Layer 05
Decision Memory

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.

Zero hallucination Why not what Causal AI

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.
DecisionX
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

The system composes reasoning paths dynamically.

Enterprise decisions rarely need one model. They need the right sequence of reasoning layers applied to the right business context.

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

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.

Three response layers summarised, deep dive and trail. Every answer is grounded in your data with full causal reasoning visible on demand.

Causal reasoning Deep dive Source attribution No black boxes
Specific Search-Term Actions by Recommendation
Most search terms still need more evidence, but the clearest immediate action is to scale a small set of proven exact-match tea terms and cut a narrow set of high-spend underperformers.
⚠
Data coverage note: 'impressions' exists in 3 tables with potentially different values. Suggested action: Specify which dimension/table to use.
⚠
Data coverage note: 'clicks' exists in 4 tables with potentially different values. Suggested action: Specify which dimension/table to use.
27Scale terms
62Negate / Reduce
5,009Test Further
68.7kAttributed sales
Key Findings
Only 27 of 5,098 rows are ready to scale classified as Test Further.
Strongest scale candidates:
Clearest cut cases:
Long tail of low-volume noise
Actionable Recommendations
Scale
Watch
Reduce
Analysis∧
12 Active Sources
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Traceability

Full trail on every response.

Every response carries a Trail tab source files used, the full reasoning chain, inferences made and the executed SQL. Nothing is a black box. Every step is logged and auditable.

Source files Reasoning chain Executed SQL Full audit log
Sources Used1 table · 5,098 rows
Sponsored_Brands_Search_term_report.xlsx
sponsored_brands_search_term_report5,098 rows
customer_search_termtargetingmatch_typecampaign_namead_group_nameimpressionsclicksspend_14_day_total_sales_14_day_total_orders
Reasoning Chain5 steps
1
Query classification DATA_TOOL
Query asks for specific data from structured sources
2
Table selection sponsored_brands_search_term_report
Table matched query intent based on schema metadata and column availability
3
Filter application 3 filters
CAST(date AS DATE) >= DATE '2024-05-03' CAST(date AS DATE) <= DATE '2026-05-03' customer_search_term IS NOT NULL
Filters derived from query constraints
4
Aggregation grouping
Grouped by: customer_search_term, targeting, match_type
5
SQL execution
Executed on 1 table · returned 5,098 rows
Inferences1
1
Selected columns with 'no suffix' suffix (audience segment) column_convention
Query Logic
Looks at Sponsored Brands search-term results May 2024–2026, totals performance per unique search phrase + targeting + match type, labels each as Scale / Negate/Reduce / Test Further, returns top 100 sorted by recommendation.
SELECT customer_search_term, targeting, match_type, campaign_name, ad_group_name, SUM(impressions) AS impressions, SUM(clicks) AS clicks, SUM(spend) AS spend, SUM(_14_day_total_sales) AS attributed_sales, 100.0 * SUM(spend) / NULLIF(SUM(_14_day_total_sales),0) AS acos_pct, CASE WHEN SUM(clicks) >= 25 AND SUM(_14_day_total_sales) >= 250 AND acos_pct <= 35 THEN 'Scale' WHEN SUM(clicks) >= 10 AND SUM(_14_day_total_sales) = 0 THEN 'Negate/Reduce' ELSE 'Test Further' END AS recommendation FROM sponsored_brands_search_term_report WHERE CAST(date AS DATE) BETWEEN '2024-05-03' AND '2026-05-03' GROUP BY 1,2,3,4,5 ORDER BY recommendation, attributed_sales DESC LIMIT 100;
Analysis∧
12 Active Sources
Type in your question...
EVALs

Continuous evaluation at ontology and query level.

Accuracy and grounding measured continuously at ontology and query level.

Ontology EVALs97%
Entity definitionsRelationshipsConventionsContinuous
Query EVALs94%
Factual groundingReasoning chainEvery queryDrift alerts
Periodic automated runsDrift alertsHuman review triggersScore history
Security & Compliance

Enterprise-grade. From day one.

Built for regulated enterprise environments.

SOC 2Type II
GDPRCompliant
ISO27001
RBAC, IAM and SSO
Public and private cloud deployment
Multi-LLM routing with governance
Full audit trail on every query

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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