By John Wheeler | Wheeler Intelligent Systems
A buyer asks for a product specification sheet. The product exists. Your team knows what it sells. Yet preparing a clear answer can still mean searching through item records, spreadsheets, old documents and email.
The hard part is often finding out which facts are missing, which ones can be trusted and which ones belong in the document you are about to send.
A neat sheet can hide an incomplete record. A blank brand name may look like a small oversight, but it raises a useful question: if that fact is missing, what else needs attention before this product information reaches a buyer?
That is the business problem behind a focused Business Central product-data readiness extension we are developing at Wheeler IS. The work centers on organizing product facts, identifying gaps and preparing a draft specification for review.
Development status: The latest version is deployed for testing. Code review supports the capabilities described below, but the new synthetic walkthrough still needs runtime evidence. The example in this article is a proposed workflow, not a completed-product demonstration. All company names and product details in the example are invented.
The document is only as useful as the facts behind it
Think about a fictional supplier called Cedar Vale Pantry and Distribution. A buyer requests information about its Tomato and Basil Soup, packed six one-kilogram units to a case.
The supplier has a product name and a case pack. Its brand name is blank. Other facts, such as ingredients, allergens or nutrition, have not been supplied for this example.
A person could make the sheet look finished by copying a brand from another document and filling the remaining spaces with guesses. That would create a polished page without establishing whether the information was correct.
A stronger approach starts with the record behind the page. Where does each fact belong? Who can confirm it? Which missing facts matter for this request? What must stay blank until someone checks it?
These questions matter to manufacturers and distributors because a product record serves several jobs. Information used to identify an item internally may not be enough to explain it to a buyer. Preparing a buyer-ready specification requires both the right facts and a review of how those facts are presented.
A focused extension inside Business Central
The extension under development keeps the product-data task connected to Business Central. The reviewed source includes item facts, a Product Core area, grouped readiness views, routing toward priority gaps, explanatory methodology and draft specification export.
Those are code-verified capabilities in the project’s source inventory. That means the code contains support for them. It does not establish that every step has worked in the latest installed version with the new synthetic records.
The intended use is focused: help a person find and review product information before preparing a specification. We are developing an extension around that task. A complete PIM, or product information management system, would involve a broader set of needs than this article covers.
The distinction matters when evaluating software. A useful narrow tool should have a clear job and evidence that it can perform that job. Its value does not depend on calling it a complete platform.
Proposed workflow using entirely synthetic data
The following sequence describes the walkthrough we intend to test. It shows the business method and the evidence we still need. It does not report actions already completed in a running system.
Start with the product facts
Open the fictional soup item and review its current information. The proposed example begins with a known product name and case pack, while Brand Name is blank.
The reviewed source separates readiness information into Mandatory, Recommended and Research views. Those groups can help frame the review, but their labels need context. A field’s place in a group does not establish that every buyer requires it or that the supplier has satisfied a current buyer’s rules.
The practical question is simple: what information do we have for this product, and what information do we still need for the task at hand?
Follow one missing fact to its record
For this proposed test, the missing fact is Brand Name. The planned edit is to enter the invented brand Cedar Vale Pantry in Product Core.
The important part is where the change is made. Editing only the final sheet could leave the product record incomplete. The proposed workflow instead follows the gap back to the record used for that fact.
Runtime proof must show that the value saves and remains there after the record is reopened. A value visible while someone is typing is not enough to prove persistence.
Refresh the checks and keep remaining gaps visible
After saving, the proposed next step is to refresh the readiness checks and inspect the Brand Name gap. The expected result is that this specific gap changes because the value now exists.
That is an expected test result, not an observed result we can claim today. The walkthrough must also retain another unresolved gap so the story does not suggest that one edit makes the whole product ready.
A completeness measure can help direct attention, but it is not a judgment that every fact is accurate. The project’s visible AI Answerability score is based on field coverage. It does not establish tested AI answer accuracy, search visibility or buyer acceptance.
Prepare a draft specification and check the output
The reviewed source supports draft specification export. In the proposed walkthrough, the next step is to generate the actual draft and check that Cedar Vale Pantry appears where the brand belongs.
The test needs the real exported file, not a recreated page that merely looks like an export. A reviewer should compare the output with the product record and confirm that the document remains clearly marked as a draft.
Unknown information must remain unknown. We will not invent nutrition, allergens, certifications or product identifiers to make the example appear more complete.
A draft still needs a human decision
Generating a sheet is a step toward preparing an answer. It does not decide whether that answer is ready to send.
A reviewer still needs to ask whether the facts are current, whether the buyer’s request is covered and whether any claim needs supporting evidence. A filled field might contain an old value. A missing field might be essential for this request. A clear page might still need an explanation.
The project’s Reviewed-for-Sharing export option should not be read as proof of a formal approval workflow. The review decision needs a responsible person and a clear standard.
This is also why we separate code review from runtime behavior. Source review tells us what the software is built to support. Runtime evidence tells us what happened with a particular installed version and set of inputs. Independent review adds another check on whether that evidence supports the claim.
What we can say today
The extension has a focused development scope, and its reviewed code supports the proposed product-facts-to-draft-specification path. The latest version is deployed for testing. The new synthetic walkthrough remains open until saving, rechecking and actual export behavior are captured and reviewed.
We are not presenting GDSN certification, a working Syndigo integration or proof that an outside recipient will accept the output. We are also not reporting customer results or measured time savings.
The lesson is useful even while testing continues: before sending a product specification, examine the information behind it. Find the gaps, trace changes to the product record and review the draft against the buyer’s request.
If you work for a manufacturer or distributor, what makes product-data requests difficult for your team? I would welcome a conversation about the facts you have trouble finding or confirming. We can start with a description or an entirely synthetic sample, without sharing confidential product or customer files.