How Enrichio Researches a Product Before It Writes

Paste a thin product description into a general AI chat tool and ask it to expand the copy, and you will get more words. You will rarely get more information. The tool has nothing to work from except what you gave it, so it elaborates on the same small set of facts, often repeating the same idea in three different sentences. I ran into this constantly while trying to fix descriptions across the Lotz Outdoors catalog. The output looked longer and more polished, but a customer reading it still could not answer the questions that actually mattered: what is this made of, who is it for, will it fit what I already own.

That experience is the reason Enrichio is built around research first, writing second. If the input is the only source of truth, the output can never be more complete than the input. Something has to add the missing information, and that something has to be research, not rephrasing.

What research actually means for a product listing

When Enrichio generates a description, it is not only working from the existing product title and the supplier's blurb. It looks at a broader set of signals connected to the product, the brand, and the store, including:

• The product itself. Existing title, description, images, and any structured data already present in Shopify, such as variants, tags, or product type.
• The brand. Known brand context that helps explain design choices, materials, or positioning, without inventing brand history that cannot be supported.
• Specifications. Dimensions, materials, capacity, compatibility, and other structured facts a buyer would need to compare this product to alternatives.
• The intended customer and use case. Who is likely to buy this kind of product and in what situation, so the opening and benefits can speak to a real use rather than a generic audience.
• The merchant's own instructions. Global and section-level instructions set in your template, which shape tone, structure, and any claims to avoid.

The goal is not to guess or invent facts that cannot be supported. It is to gather what is actually knowable about the product and organize it into the five-part structure: opening, summary, features and benefits, detailed description, and specifications. Research fills in real information. It does not fabricate it.

Why this is different from rephrasing

A general-purpose AI tool has no persistent understanding of your catalog, your brand voice, or your template. Every time you paste a description in, you are starting from zero and repeating the same instructions you used the last time. It will happily generate longer text, but it has no built-in mechanism for going out and finding the product's actual dimensions or verifying what materials it uses. It works with what is in front of it.

Enrichio's job is to close that gap by treating research as a distinct step, before generation happens, rather than assuming the existing text already contains everything worth saying. That is the difference between a description that sounds better and a description that tells the customer something new and true.

An example: from a thin listing to a researched one

Take a typical inherited listing: a product title, a SKU, and one sentence of marketing copy from the supplier. "Durable and lightweight, great for everyday use." That sentence is not false, but it also is not useful. It could describe thousands of unrelated products.

A rephrasing tool turns that into three sentences that all say some version of "durable and lightweight." A research-driven pass instead asks: what is this actually made of, what does "everyday use" mean for this specific product category, what dimensions or capacity would a buyer need to know, and who is the realistic customer for this item. The resulting listing might state the actual material and construction, name a plausible use case supported by the product type, and list real specifications instead of repeating the adjective "durable" in different words.

Where research fits in the four-step workflow

Research is not a separate step you have to manage yourself. It happens inside the generate step of the Enrichio workflow: build a template, load products, generate, then review and publish. Once your template defines the structure you want, and your products are loaded from Shopify, generation is where research and writing come together to produce a draft that follows your five-part structure with the fullest information available for that product.

Research still needs a human check

Research-driven generation produces a stronger starting draft than simple rephrasing, but it does not remove the need for review. Specifications should be verified against the actual product, especially for anything safety-related, regulated, or highly technical. Enrichio supports research and generation, but merchants should always confirm facts before publishing, the same way you would check a description written by a new employee before it goes live.

A short checklist before you generate

To get the most out of research-driven generation, it helps to give the workflow good material to work with:

1. Make sure the product's title, type, and any existing specifications are accurate in Shopify before loading it.
2. Add any brand or use-case context you know to your template's global instructions, rather than assuming it will be inferred.
3. Flag any claims you never want made, such as unverified health or performance claims, in your section-level instructions.
4. After generation, check the draft against the actual product page, packaging, or manufacturer spec sheet before publishing.

Better research produces a better draft, but the merchant is still the last line of defense for accuracy. That combination, research plus review, is what turns a thin listing into one a customer can actually trust.

Research quality depends on what you give it to work with

Research-driven generation is not magic. It works better when the product record it starts from is not completely empty. A product with a clear title, an accurate product type, and at least a few real attributes gives the workflow something solid to build on. A product with almost nothing filled in, a placeholder title, and no category assigned gives it much less to work from, and the resulting draft will reflect that gap.

This is one reason I recommend cleaning up the most basic fields in Shopify, title, product type, and any obvious variant data, before loading a batch of products for enrichment. It takes a few minutes per product and meaningfully improves what research can find and use. Garbage in, garbage out is not a new idea, but it applies just as much to a research-driven workflow as it did to my old spreadsheet-and-prompt process.

Why this matters more as your catalog grows

For a store with a handful of products, a merchant can personally research each one: read the manufacturer spec sheet, check a competitor's page, ask the brand directly. That does not scale to a catalog of a few thousand products, let alone the roughly 60,000 that Lotz Outdoors carries. At that size, the only realistic path to complete listings is a workflow that treats research as a repeatable step rather than a one-off task performed by a single overworked person.

That is the practical argument for research-driven generation over manual rephrasing at scale. It is not that a human researcher would do a worse job on any single product. It is that no team of a realistic size can do that job by hand across an entire catalog and keep it current as products change.