Audit your product catalogue like an AI agent

Manual audits catch the obvious but can miss the costly. If your catalogue fuels search, merchandising and service, you need something sharper than quarterly spot-checks. An AI agent is a machine learning system which can perceive its environment, assess the state of play, plan actions, make decisions, and take action to achieve a goal autonomously, with only minimal human intervention needed.

Our article outlines what it means to think like an AI agent, with a modern PIM at the centre. This turns auditing from aftercare (or ‘aftershock’) into an always-on, systemised capability.

What acting ‘like an AI agent’ means for product data

An AI agent isn’t just a passive chatbot. It understands an end purpose (such as: “Keep every listed product compliant, consistent and channel-ready”), It does this by gathering evidence from your systems, deciding what to do, acting through workflows and APIs, and then measuring the outcomes. This shifts our organisational mindset from a “find errors and correct” approach to “continuously optimise catalogue quality and commercial impact.”

So, how do we go about it?

Phase 1: set the objective and scope

Here’s a new acronym: VIVO – “Vague Intentions = Vague Outcomes.”  You need to define specific, measurable goals – below are some representative examples of what this specificity means:

• Compliance: All electricals carry required marks; chemicals include REACH fields (Registration, Evaluation, Authorisation and Restriction of Chemicals) 

• SEO effectiveness: Top revenue lines have unique, keyword- and key phrase-rich titles and meta data

• Completeness: Minimum mandatory attributes for priority categories ≥95% before publishing to channels

• Style: Titles follow a pattern (Brand + Product + Key feature)

Your PIM is the agent’s primary ‘sensor’: It exists as the single (and definitive) governed view of products, attributes, assets, and channel templates. The governance rules encode what “high-quality” must be. Additionally, the PIM dashboard offers a snapshot of the state of data health.

Phase 2: ingesting and reconciling the data

The big plus is that, compared with the practice which so many merchants still persist with, agents don’t audit spreadsheets by hand! They connect to ERP, PIM, DAM, feeds from eCommerce platforms and marketplaces. The agent also extracts product records, rich media (digital assets), and information on pricing and stock levels. Last but certainly not least, it records data lineage (as in ‘who’s changed what, how, and when). This creation of a unified inventory to test is foundational for identifying any drift between the product data “Golden Record” in the PIM, and what’s actually live on site at any given moment.

Phase 3: running quality checks at scale

As well as simple validation, the AI agent layers its machine intelligence on top of the established and deterministic rules.

• Completeness: mandatory fields, channel-specific templates, minimum acceptable rich media items.

• Consistency: standardised taxonomies and units: For instance: colour: Black a  ≠ Color: black ≠ Blk).

• Correctness: It cross-references product claims against trusted sources, such as verifying that calling something “waterproof” doesn’t contradict IP rating.

• Anomalies: It detects outliers (key for minute differences – e.g., a 0.3 kg ladder, a 10,000 mm screw), duplicate SKUs, and obviously mistaken dimensions.

• Language quality level: It corrects grammar, as well as monitoring readability, and consistency of brand tone for text content; It checks for avoidance of risky phrases and enforces overall style.

• Image checks: It checks for correct matching of imagery with variants and confirms correct use of resolution and aspect ratios per channel.

The whole point is speed, proactivity, and the ‘persistence’ which a human simply cannot match: The AI agent inspects thousands of SKUs without getting tired or losing concentration!

Phase 4: planning actions and repairing issues

Flagging isn’t enough – too reactive; An audit must actively drive remedial measures. This means:

• Auto-remediation: appending missing units, converting erroneous measurements, harmonising allowed values, swapping low-res images for quality-approved assets, and regenerating short product descriptions from structured specs.

• Delegating with context: routing complex edits to the right human specialist (typically a copywriter, compliance lead, or category manager) with a concise brief, the errors in question, and suggested remedies.

• Deciding and tracking: for each and every issue, the AI agent chooses between auto-fix or assign, logs every decision made, and timestamps the outcomes to facilitate audit at a later stage.

Workflows established in your PIM system make this an eminently practical affair: It can program tasks, and feed into approvals and rollback procedures where needed.

Phase 5: enforcing compliance and governance

A key factor in any effective audit is prevention. When essential areas like regulations, claims policies and channel rules are encoded as validation gates it, allow the agent to check on:

• Region-specific compliance fields (like safety labels, warnings, materials, or allergens).
• Implementation of audit trails for every high-risk edit.

• They use role-based approvals protocols for highly sensitive product categories (such as medical equipment, electrical goods, and chemicals).

• The scheduled reviews for data fields with time-sensitive information (such as expiry dates for certifications and warranties).

Furthermore, when a marketplace changes a template (this happens more often than we might think), the agent will update the channel rules and trigger product data re-validation before re-listing takes place.

Phase 6: Reporting, prioritising, and acting on insight

Not all issues are of equal importance or urgency. The AI agent can produce a list which ranks from urgent and important down to non-urgent and less important (but still important nevertheless!): For the purposes of brevity, here’s a generic example:

1. A misleading legal claim – clearly critical

2. Price inconsistency – high priority

3. A missing bullet in a list of features on a product description – medium importance

4. A single spelling error in a description – low priority

The agent will also highlight situations where quick wins are possible (such as fixes applied in bulk) and ‘needle-movers’ (where the changes/repairs made correlate directly with potential uplift in conversion). It ties in any reporting with your main business metrics – typically:

• Discoverability (SEO)
• Average Basket adds (order value)
• Returns rate
• Drivers of customer support calls
• Time-to-market

Phase 7: keep humans in the loop

The autonomy of AI tools is a time and effort-saver but never overlook human judgement. Those complex or reputationally risky edits should still require human sign-off. In fact, the AI agent learns from these human decisions to minimise the chances of future ‘false positives.’ Human editors use their expertise to assess the context and can then approve, amend, or reject with one click; these actions are then useful as training data.

Phase 8: Keeping it continuous

In this day and age, the nature of a data audit has been transformed. Now, it doesn’t need to be periodic, on punctual one-off occasions. You can instrumentalise a catalogue so that the AI agent is permanently monitoring for:

• Data drift, like ERP vs site price mismatches, or changes in specs which haven’t been syndicated

• Zero-result searches, meaning its capacity to feed improvements in taxonomy and attributes.

• Digital shelf analytics by channel, such as broken links to images, suppressed listings, or missing attributes

• Content performance, by identifying which descriptions, assets or FAQs reduce returns or lower rate of support tickets.

These tasks can be scheduled daily, or can be event-driven, such as onboarding supplier data, pre-channel go-live, or after updates in taxonomy.

Where Start with Data helps

If your catalogue audits still rely on spreadsheets, samples, and crossed fingers, it’s time to rethink the approach.

We help teams turn product data auditing into a continuous, systemised capability. Not a one-off clean-up, but an always-on discipline that improves data quality, reduces risk, and supports real commercial outcomes.

If you want to see what this would look like for your catalogue, get in touch and we’ll talk it through. Get in touch with us today