lantrix.io

Regulatory sandbox / operational assurance / decision systems

Architecture-driven assurance for governed decision systems.

Flux shows whether AI-enabled workflows are The workflow has an owner, review route and documented acceptance., Controls, approvals, exception paths and evidence requirements are mapped to the workflow., 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 workflows, architecture, evidence and telemetry.

02

Maps obligations, controls and The path taken when a decision, control or approval cannot proceed normally..

03

Produces evidence packs, maturity posture and The next highest-value remediation required to improve assurance posture. actions.

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 path taken when a decision, control or approval cannot proceed normally.
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

The workflow has an owner, review route and documented acceptance. / Controls, approvals, exception paths and evidence requirements are mapped to the workflow. / 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 evidence, exception records, telemetry and review artefacts 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.