Case study · Governance
AI Brand Review Governance Platform
Continuous, automatic checking of hundreds of brand reviews against official brand websites, replacing occasional manual checks, for editorial and operations teams.
- Business area
- Content governance
- Users
- Editorial and Operations
- Technology
- AI, web scraping, NLP
- Outcome
- Continuous automated auditing
The problem
What was getting in the way.
Hundreds of long-form brand reviews required continual validation against official brand websites. Manual auditing was repetitive, difficult to scale and increased the risk of outdated information remaining live.
Why it was hard
High volume
Hundreds of long-form brand reviews required continual validation.
Constant re-checking
Official brand sites change, so validation is never truly finished.
Reputational risk
Outdated information could remain live and mislead customers.
Hard to scale
Manual auditing was repetitive and could not keep pace.
The solution
What was built.
Developed a scheduled AI governance platform with two workflows: a weekly audit of new reviews and a monthly validation of all published reviews. The system scrapes official brand sites, compares products, features and content, flags inconsistencies, identifies new products and highlights stale reviews of six months or more. Findings are prioritised as red (incorrect information), orange (requires attention) and blue (improvement opportunities).
Design decisions
- Run two cadences: weekly checks on new reviews and a monthly full validation.
- Compare against the official source of truth, not internal assumptions.
- Prioritise findings by severity, rather than flagging everything equally.
How it works
From input to outcome.
01
Brand websites
02
Scheduled scraper
03
AI comparison engine
04
Flagged findings
05
Prioritised report
Weekly and monthly cycles run automatically, without manual triggering.
What it does
The capabilities.
Weekly and monthly scheduling
Two automated cadences cover new and existing reviews.
Automated site scraping
Continuously reads official brand websites.
Product and feature comparison
Compares published content against the source of truth.
Inconsistency flagging
Surfaces mismatches and outdated claims.
Stale content detection
Highlights reviews untouched for six months or more.
Severity-based prioritisation
Classifies findings as red, orange or blue.
The result
What changed.
Continuous monitoring
Introduced continuous monitoring rather than periodic reviews.
Severity prioritisation
Prioritised work using severity-based classifications.
Improved confidence
Improved confidence in published content through repeatable validation.
What could this become?
- Real-time change detection
- Approval workflows
- AI summaries
Facing something like this?
Describe the process in a short note. Every system here started with exactly that.