Workflow path
workflow.structureSignal -> Policy check -> Recommendation -> Human review -> Action
tasks / gateways / approvals / exception paths
Regulatory sandbox / operational assurance / decision systems
Architecture-driven assurance for governed decision systems.
Flux shows whether AI-enabled workflows are Workflow ownership, review authority and acceptance state are explicitly defined., Required governance checkpoints, approvals and evidence obligations are linked to workflow execution paths., or Evidence, telemetry, exceptions and maturity posture are updated as the workflow changes..
Problem framing
Evidence is becoming the bottleneck for AI adoption.
Regulated teams are being asked to adopt AI-enabled workflows faster than their evidence models, control records, and assurance processes can adapt.
- AI adoption is moving faster than evidence.
- Execution visibility is not governance.
- Model confidence is not auditability.
What Flux does
Compiles operational reality into governed evidence.
01
Ingests Workflow and decision structure can be ingested from BPMN and DMN, including tasks, gateways, decisions and exception paths., architecture, Evidence captures what happened, why it was allowed, which policy applied and who authorised the action. and telemetry.
02
Flux compiles obligations and controls into structured assurance mappings linked to workflow paths. obligations, controls and The governed route followed when normal workflow execution fails, escalates or requires intervention..
03
Produces evidence packs, maturity posture and The next highest-value remediation required to improve assurance posture. actions.
Why BPMN + DMN matters
gives Flux the operating path: tasks, reviews, approvals, gateways and exception routes.
gives Flux the decision logic: rules, thresholds, policies and outcomes.
Together they let Flux connect workflow structure, decision authority and evidence into a reconstructable assurance trail.
Decision logic
decision.logicInputs -> Rules -> Outcome
thresholds / policy logic / decision tables / escalation criteria
Flux assurance output
- decision provenance
- mapped controls
- review evidence
- exception handling
- fix-next action
Why it matters
Regulated teams need more than execution visibility.
A reconstructable chain showing user intent, data source, model or agent step, policy check, human review and action.
Proof that review happened at the right point, by the right role, before the action was taken.
The governed route followed when normal workflow execution fails, escalates or requires intervention.
The ability to replay what happened, which evidence existed, and why the decision or action was allowed.
Example workflow
From workflow signal to reconstructable decision evidence.
Applicable to Fraud / AML / patient pathway / autonomous engineering workflow.
01->
Fraud signal
02->
AML policy
03->
Risk recommendation
Workflow ownership, review authority and acceptance state are explicitly defined. / Required governance checkpoints, approvals and evidence obligations are linked to workflow execution paths. / Evidence, telemetry, exceptions and maturity posture are updated as the workflow changes.
04->
Human review
05->
Action
06END
Reconstruction
Assurance stack
Governance evidence spans the full operating stack.
01
Identity
02
source control
03
CI/CD
04
runtime
05
Retrieval sources, permissions, citations, freshness and lineage for AI-assisted outputs.
06
model/provider
07
External systems or actions an AI agent can invoke, such as tickets, APIs, messages, records or workflow steps.
08
monitoring
09
Control records, review artefacts, telemetry and exception records packaged for assurance.
Who it is for
Teams operating AI where assurance is part of delivery.
Finance
NHS
CNI
public sector
defence-adjacent
SaaS AI
AI-assisted engineering
The challenge is not AI capability.
It is evidencing AI-informed decisions.
Lantrix works with regulated and assurance-heavy environments where decision systems must be explainable as operating architectures, not as promises.