Describely

Almost every online catalogue has the same problem in the same place. The top fifty products have good descriptions written by a person, and the remaining several thousand have whatever the supplier's spreadsheet contained.

That is not negligence, it is arithmetic. Writing eight thousand product descriptions by hand is a year of somebody's life, and no retailer approves that headcount for copy nobody is certain converts.

Describely is built for that specific gap: producing and enriching catalogue content at a scale where hand-writing was never a realistic option.

Describely: Product Copy At Catalogue Scale

• Bulk generation of descriptions, titles and meta descriptions across thousands of listings at once.
• AI Content Rules that constrain every generation to your brand, rather than prompts retyped per product.
• SKU scoring for AI search — how likely each product is to surface in ChatGPT, Google and Gemini.
• A content audit that verifies claims and catches errors, which matters when errors replicate at scale.
• Shopify, WooCommerce, Akeneo, Wix, Squarespace and CSV, with Target Australia cited at 1,000+ descriptions a week.

Why Do Product Descriptions Break At Scale?

Describely offers bulk content generation for thousands of listings, product data enrichment for sparse details, and generation of descriptions, titles and meta descriptions.

The failure at scale is not quality on any individual product. It is the distribution: a handful of excellent listings, a long tail of supplier-imported fragments, and no consistency between them.

That inconsistency has direct commercial consequences. A shopper comparing two products on the same site gets a paragraph for one and a specification code for the other, which reads as neglect regardless of which product is better.

The data enrichment piece addresses the more fundamental version of the problem, where the source information is simply missing. A description cannot describe what nobody recorded, and supplier feeds are routinely incomplete on exactly the attributes shoppers care about.

The honest limit is that enrichment infers, and inference about physical products can be wrong in ways that matter — a material, a dimension, a compatibility claim. That is the specific risk that makes the audit feature load-bearing rather than optional, and it is why catalogue imagery pipelines such as Clipping Magic sit alongside rather than inside this workflow.

A shopper comparing two products gets a paragraph for one and a specification code for the other. That reads as neglect regardless of which product is better.

Content Rules Instead Of Prompts

Describely's AI Content Rules are described as tailoring content to your brand, with a named customer noting that adjusting rules per department removed the need for constant fine-tuning.

This is the architectural decision that separates a catalogue tool from a chat window, and it matters more than any output quality claim.

Prompting is stateless. Every generation restates the requirements, and any inconsistency in how they are stated appears in the output — which across eight thousand products means drift nobody planned.

Rules are stateful. Brand voice, forbidden claims, required attributes, formatting conventions and regulatory constraints are defined once and applied to everything, which is how you get a catalogue that reads as one catalogue.

The per-department variation the customer describes is the practical refinement. Electronics copy and homeware copy should not sound identical, and rules that can differ by category while sharing a brand voice is exactly the right granularity.

Scoring SKUs For AI Search

Describely's headline offer is grading every SKU on how likely it is to surface in ChatGPT, Google and Gemini, and how ready it is to convert, then generating content to close the gap.

This is a current and defensible move. Product research increasingly starts in an assistant — what is a good X for Y under Z — and the answer is assembled from product information the assistant can actually read and reason about.

What that rewards is different from what keyword optimisation rewarded. Assistants need specific, structured, unambiguous attributes: materials, dimensions, compatibility, use cases stated plainly rather than implied by marketing language.

The consequence is convenient. Copy written to be legible to an assistant — concrete, specific, complete — is also copy that answers a shopper's actual question, so the two objectives point the same way for once.

The caution is that nobody can verify these scores externally. Assistant visibility is measured by asking questions repeatedly and observing, which makes the number a vendor's method rather than a fact — the same limitation that applies to dedicated visibility platforms such as Conductor.

• Define Content Rules before generating anything at volume.
• Vary rules by category — electronics and homeware should not read identically.
• Run the audit before publishing, since a wrong claim replicates across the catalogue.
• Write for specificity, which serves assistants and shoppers simultaneously.
• Treat AI visibility scores as one vendor's method, not an external fact.

The Content Audit Nobody Runs

Describely lists a Content Audit that verifies claims and catches errors, and it is the feature most likely to prevent an expensive incident.

Bulk generation changes the shape of risk. A single wrong description is a customer service email; the same wrong claim generated across a category is a compliance problem and potentially a recall of every listing that carried it.

The categories where this bites are specific and predictable. Materials, allergens, safety certifications, compatibility, dimensions and country of origin are all things a generator can produce plausibly and incorrectly.

Regulated claims are worse still. Words that imply a health, environmental or performance benefit carry legal weight in most jurisdictions, and a model with no knowledge of your regulatory position will use them naturally because they are persuasive.

The right posture is to treat generation as drafting and the audit as a gate, with human review required for any regulated category. That is slower than generate-and-publish and it is the only defensible way to run this at scale.

Where Generated Catalogue Copy Falls Down

Even done well, there are things bulk generation cannot produce, and knowing them tells you where to spend your remaining human attention.

It cannot know what is actually good about a product. The detail a buyer noticed at a trade show, the reason this supplier's version is better made, the thing customers keep saying in reviews — none of that is in a specification feed.

It cannot write a genuinely distinctive brand voice. Rules constrain output towards a defined style, which prevents drift and does not create a voice somebody would recognise. That still requires a writer.

It cannot fix a bad photograph, and product imagery outperforms product copy on conversion in almost every study anyone has published.

The sensible allocation follows from that. Generate the long tail, write the top fifty by hand, and put remaining effort into photography — the reverse of how most teams instinctively spend their content budget, and the same tension writing tools like Scribeist face wherever volume meets voice.

Rewriting A Catalogue Without Chaos

The failure with bulk content is generating everything, publishing it, and then discovering a systematic error in twelve hundred listings.

This sequence catches problems while they are still cheap to fix.

1. Write Content Rules first, including the claims you must never make.
2. Generate one category and read fifty listings end to end before anything else.
3. Vary the rules by category once you see how they read side by side.
4. Run the content audit and inspect every flagged claim by hand.
5. Publish one category and watch conversion before doing the rest.
6. Keep the top fifty products hand-written, and spend the saving on photography.