Retail & e-commerce
Retailers and e-commerce platforms run AI at scale across the customer journey, from what shoppers see to what they pay and how they are served. Much of it is lower-risk, but transparency, fairness and the rise of action-taking shopping agents still demand proportionate, demonstrable governance. Gamut lets retail teams govern a large AI estate efficiently without over-governing the routine.
AI systems typically in scope
Section titled “AI systems typically in scope”- Recommendation and personalisation engines.
- Dynamic and personalised pricing.
- Customer-service assistants and agentic shopping or support agents.
- Demand forecasting, inventory and supply-chain optimisation.
- Fraud and returns-abuse detection.
Governance drivers
Section titled “Governance drivers”- Transparency. Clear disclosure where customers interact with AI or see AI-driven content.
- Fairness in pricing. Evidence that personalised pricing does not produce unfair outcomes.
- EU AI Act. Transparency obligations for many customer-facing uses.
- Proportionality at scale. Govern a large estate without drowning low-risk systems in process.
Use cases
Section titled “Use cases”Govern a large estate proportionately
Section titled “Govern a large estate proportionately”How Gamut solves it: a deterministic governance weighting profile tiers each system consistently, so high-impact pricing models get deep GTSAF assessment while routine recommenders are governed lightly.
Put a customer-service agent under runtime control
Section titled “Put a customer-service agent under runtime control”The scenario: an agent that answers customers and can act (refunds, order changes).
How Gamut solves it: govern it through Agentic CISO with an ATF level, and let Gateway require approval for refunds or external actions while Claw executes only through governed paths.
Evidence pricing fairness and transparency
Section titled “Evidence pricing fairness and transparency”How Gamut solves it: route to the EU AI Act for transparency obligations and capture fairness evidence through control tests.
Quick start
Section titled “Quick start”- Surface and register the estate via the Discovery Inbox and AI System Records.
- Run intake; let weighting tier each system proportionately.
- Route high-impact systems to GTSAF and the EU AI Act; govern agents through the agentic stack.
- Evidence transparency and fairness in the Evidence Tracker.
- Track gaps on the Remediation Roadmap.
- Report estate-wide posture from reporting.
Frameworks that apply
Section titled “Frameworks that apply”GTSAF, EU AI Act, NIST AI RMF, and for agentic commerce ATF and MAESTRO.
- Technology & SaaS: for platforms shipping AI products.
- Telecommunications: another high-volume customer-AI vertical.
- Industry playbooks: the full set.