LLM Mapping
The Semantic Interface
Interprets natural language and proposes mappings into the controlled reasoning space.
Advanced causality platform
From opaque outputs to traceable AI workflows.
CauseXAI™ combines LLMs, DAG reasoning, Bayesian networks, and GraphRAG grounding to make AI workflows transparent, inspectable, and audit-ready.
Governed LLM mappingGraphRAG groundingAudit-ready XAI
Architecture at a glance
One governed chain from language to traceable reasoning control.
CauseXAI combines semantic mapping, graph structure, source grounding, probabilistic reasoning, and what-if analysis into one explainable AI defense stack.
The design principle
LLM proposes, governance admits, graph reasons, sources ground, audit explains.
Five core technology layers
Explainability built into the reasoning process.
Each layer has a distinct governance role. Together, they move explainability from a post-hoc narrative into the reasoning process itself.
DAG Reasoning Graph
The Causal Reasoning Blueprint
Structures variables, dependencies, rules, and explainable reasoning paths.
GraphRAG Grounding
The Source Grounding Layer
Links reasoning nodes to reviewed documents, policies, pathways, and source passages.
Bayesian Inference
The Probabilistic Guardrail
Estimates downstream risk signals over the graph and enables inspectable thresholds.
Counterfactual & Sensitivity Analysis
The Continuous Auditor
Tests what would change under alternative states and ranks which inputs matter most.
Beyond post-hoc XAI
Explain before, during, and after a reasoning run.
CauseXAI is designed to show what evidence was admitted, what path was activated, which sources were used, how risk changed, and which variables matter most.
What evidence was admittedWhat path was activatedWhich sources were usedHow risk changedWhich variables matter most
Enterprise-grade XAI by Design
Built for transparency, control, explanation, and review.
Global transparency
DAG and Bayesian-network structures show the overall reasoning model, dependencies, and risk pathways.
Local transparency
GraphRAG and evidence trace show which source passages, variables, and admitted states influenced a specific run.
Ante-hoc control
Admission rules, validation, allowed states, and thresholds can be inspected before execution.
Post-hoc explanation
Audit-ready reports summarize evidence, inference, grounding, narrative, and counterfactual effects after a run.
Contrastive reasoning
Counterfactuals answer what would have changed if a variable had a different allowed state.
Robustness insight
Sensitivity analysis ranks which inputs have the strongest effect on downstream synthetic risk signals.
Where it fits
For domains where users need more than a chatbot.
Healthcare pathways, regulatory procedures, compliance playbooks, operational SOPs, internal policy guidance, and risk-control workflows.
CauseXAI™Advanced Causality Platform
Experience source-backed explainable reasoning.
Experience our powerful platform using synthetic sample artifacts.