Policy exists, but product teams cannot apply it
Teams may understand the principles but still lack a practical answer to what they can use, what they must test, who decides, what evidence they must retain, and when a person must intervene.
All professional services04 / 05 · RESPONSIBLE AI, GOVERNANCE & EVALUATION
Know what the AI may do.Prove how it behaves.
Smart Scale turns responsible-AI policy into operating controls: use-case risk classification, human authority, representative evaluation, access boundaries, monitoring, change review, audit evidence, and incident response.
IS THIS THE PROBLEM YOU HAVE? / 01This service may be a strong fit
This service may be a strong fit
if any of these feel familiar.
The consequence of an error is material
A customer commitment, sensitive record, access change, financial action, employment decision, regulatory requirement, or public-facing output needs clear boundaries and a recovery plan before release.
Several functions need one workable control model
Procurement, risk, security, legal, data, technology, and delivery teams need a shared language that makes releases possible without handing responsibility from one committee to another.
A working system now needs repeatable evaluation
Models, prompts, retrieval, tools, source content, policies, and vendors change. The organisation needs a way to test the complete service again—not merely rely on an initial demonstration.
Translate policy into product decisions
Define approved, restricted, and prohibited uses; data and access boundaries; human decision rights; and release responsibilities for each system.
Test the work, not just the model
Evaluate source support, task completion, action accuracy, access, escalation, prompt injection, difficult scenarios, failure, and recovery.
Keep control after release
Monitor production behaviour, incidents, overrides, quality drift, source changes, vendor changes, and the evidence needed for continued approval.
ONE SERVICE · 06 PART BUSINESS STORY01 / 06THE BUSINESS SITUATION
Know what the AI may do.
Prove how it behaves.
Your organisation has principles and approval committees, but product teams still lack a practical answer on what to test, which failures matter, when a human must decide, who can approve release, and what evidence must be monitored in production.
02 / 06WHAT SMART SCALE DOES
Govern AI through the lifecycle
without separating control from delivery.
We make responsible AI visible in the product and the operating process. Each use case receives clear boundaries, named decision rights, realistic tests, release evidence, production monitoring, change controls, and an intervention route when the system reaches uncertainty or risk.
03 / 06WORKSTREAM · 01
Governance model
Define risk tiers, approved uses, prohibited uses, decision rights, review bodies, documentation standards, vendor controls, and release responsibilities.
04 / 06WORKSTREAM · 02
TrustLayer evaluation
Create representative test sets, quality measures, safety and access tests, human review thresholds, regression checks, and evidence required for release.
05 / 06WORKSTREAM · 03
Release and intervention design
Define the evidence required to release, the owner who can approve it, the actions that require a person, the signals that pause the system, and the recovery route when behaviour or evidence is unsuitable.
06 / 06WORKSTREAM · 04
Operational assurance
Monitor production behaviour, incidents, model and prompt changes, policy exceptions, user feedback, data drift, and scheduled control reviews. Controls remain connected to the service after launch rather than being left in a policy document.
COMMON QUESTIONS / 03Questions specific to
Questions specific to
Know what the AI may do..
What does responsible AI governance change in practice?
It turns general policy into day-to-day product decisions: intended use, approved and prohibited actions, data and access boundaries, human authority, representative tests, release criteria, monitoring, incident response, vendor change review, and records that show why the system was allowed to operate.
When is this not the right first service?
A basic low-consequence experiment with no sensitive data, external action, or material impact may not need a full governance engagement. It still needs clear ownership and sensible boundaries. If the real problem is an unknown process or unclear opportunity, an assessment should come before detailed controls.
What do you need to review?
We need the intended use, affected people, key workflows, source information, connected systems and actions, access model, existing policies, supplier or model information, known constraints, and the people accountable for business, technical, risk, and operational decisions.
How are human controls designed?
We separate low-impact reversible work from actions that must be approved or remain human. For an escalation, the reviewer receives the relevant context, evidence, uncertainty, pending consequence, and controls to approve, correct, reject, transfer, pause, or stop the work.
What failures are tested?
Tests cover the normal path and the moments likely to cause harm or loss of control: missing or conflicting sources, denied access, unsuitable instructions, unavailable systems, duplicate events, incorrect actions, low confidence, policy conflict, handoff, correction, rollback, and recovery.
How do we know the controls are effective?
The evidence includes traceable intended-use and approval records, representative evaluation results, release decisions, production monitoring, intervention and incident patterns, source or model change reviews, and proof that operators can respond when the service reaches a defined boundary.