// 01
What is a reviewable AI workflow?
A reviewable AI workflow keeps the use case, inputs, sources, generated output, reviewer, and approval state visible so a person can inspect the work before it is used.
AIRIG helps organisations turn AI ideas into reviewable workflows: define use cases, assess data exposure, test output quality, and keep human accountability visible before rollout.
AIRIG operating map
Review layer
// 01
ScopeUse case, users, data and risk boundaries
defined// 02
BuildLexium, Medixium or governed custom workflow
mapped// 03
EvaluateSources, edge cases, quality and review gates
tested// 04
OperateAudit trail, change control and accountable owners
activeProducts
2
Model
Review
Output
Trace
// 01Applied AI
// 02Governance
// 03Evaluation
// 04Adoption
// 01A - Governance proof
Signals that keep AI work accountable.
AIRIG product and advisory work is framed around evidence teams can inspect before AI-assisted outputs move into real operations.
Review gates
Citation trails
Audit logs
Privacy controls
AIRIG signal trace
VERIFIED
// 01
Review gatesDefined handoffs before pilot, deployment, or export.
mapped
// 02
Citation trailsSource context remains close to generated work.
linked
// 03
Audit logsReviewer actions and workflow changes stay traceable.
visible
// 04
Privacy controlsData exposure is scoped before wider rollout.
scoped
Inputs
Scoped
Review
Human
Record
Trace
// 01 — Mission
Useful AI, with human oversight kept visible at every step.
AIRIG exists to connect AI research, product design, and governance controls for teams that need practical, reviewable systems — not opaque copilots.
A
Artificial
Probabilistic systems engineered with explicit scope, limits, and review handoffs.
I
Intelligence
Models that surface evidence and reasoning — not just answers.
R
Research
Empirical evaluation: benchmarks, red-teams, and field studies before deployment.
I
Innovation
New product surfaces for review, source attribution, and decision audit.
G
Group
A cross-disciplinary team of operators, clinicians, lawyers, and engineers.
// 02 — Capabilities
Six practices that turn AI work from demo to deployment.
A grounded set of priorities for product teams building governance frameworks, evaluation harnesses, and responsible adoption plans.
// 01
Scoped product work
We build and evaluate narrow AI workflows before expanding into larger operating systems. Lexium and Medixium are the live exhibits.
> scope :: { domain, users, success_metric, fail_modes }
// 02
Reviewable workflows
Every output traces back to its inputs, its prompt, its model version, and the human who approved it. No black boxes.
> trace :: input → context → output → reviewer → decision
// 03
Responsible adoption
We help teams write policy, set data boundaries, and design review practices before AI touches an operational decision.
> policy.load("AU.privacy.app6") :: ok
// 04
Evaluation harness
Quality, safety, and fairness checks defined per-use-case. Output is gated by tests you can read, run, and falsify.
> eval.run() :: 94.2% pass · 3 regressions · 0 critical
// 05
Data handling
Source control, permission boundaries, retention windows, redaction. Sensitive data minimised by design — not by promise.
> redact(["PII", "PHI", "credentials"]) :: 7 hits
// 06
System integration
Workflows wire into existing tools, records, approvals, and reporting — they do not replace them. The org of record stays put.
> hooks: { audit, ehr, signoff } :: connected
// 03 — Interactive demo
Walk a document through the review pipeline.
Pick a sample, run the pipeline, then sit in the reviewer seat: approve, edit, or reject the model output. Every action is logged to an audit trail you can inspect.
●airig://workflow/clinical
HARNESS v2026.5.1·SCOPE MEDIXIUM·IDLE
// Select sample
01
Ingest
— idle
02
Extract
— idle
03
Evaluate
— idle
04
Review
— idle
05
Decision
— idle
// pipeline log · clinical0 events
— idle — press ▶ to run pipeline —
// Model output · CLINICAL NOTE1 flags
— output appears after evaluation —
// workflow_idwf_clinical_48210
// reviewer_poolL2 · clin-review
// policyclinical-au-v3
// statusidle
// 04 — Ethical pillars
Three principles that shape every decision.
Cognitive equity, privacy, and accountability — operationalised, not framed as a mission statement.
01 · BALANCE
User clarity
AI systems should make their role, limits, and review expectations clear to operators, reviewers, and downstream readers.
02 · PRIVACY
Privacy by design
Sensitive data is minimised, protected, and handled per the needs of the use case — not the maximum a model could consume.
03 · ACCOUNTABILITY
Named owners
If a system informs a decision, people need a practical way to inspect, challenge, and correct it — with named human owners.
// 05 - Products
Two live product tracks. Both governed end to end.
Lexium and Medixium are scoped operator-facing AI products built on the AIRIG harness. Both keep citations, evaluations, reviewer queues, and audit evidence close to the work.
LEXIUM - LEGAL
Lexium
Contract analysis, clause comparison, obligation tracking, and matter review with mandatory citation back to source documents.
v2.3 - deployedView track ->
MEDIXIUM - CLINICAL
Medixium
Discharge summaries, drug interaction reviews, and chart synthesis with clinician sign-off and evidence trail review.
v1.7 - pilotView track ->
// 06 - Research notes
Practical writing from the AIRIG team.
Field notes on responsible AI, evaluation, and product design, written for operators rather than press releases.
// 07 - AI adoption FAQ
Questions before teams put AI into reviewed work.
Short answers for buyers and operators comparing governance, evaluation, and reviewable workflow options.
// 02
How does AIRIG keep human accountability visible?
AIRIG designs workflows around review gates, audit trails, source context, and clear ownership so AI output remains working material until a responsible person accepts or changes it.
// 03
What does AIRIG evaluate before rollout?
AIRIG checks workflow scope, data exposure, output quality, reviewer handoffs, privacy controls, and the evidence teams need to decide whether an AI use case is ready.
// 08 - Partner with AIRIG
Make AI
accountable.
accountable.
Work with AIRIG on scoped AI initiatives with practical governance, evaluation, and reviewer handoffs. Start with a briefing, product review, or implementation proposal.