Show Real Manufacturing Expertise in AI-Assisted Content

Expertise shows on a page as details only someone who makes the product could supply: the conditions a test was run under, the tolerance you hold in production, and what goes wrong on the line. An AI model cannot generate those, because they are not on the web. Someone has to collect them from an engineer before the draft is written, and the article should carry the name of a real person who is accountable for it.

What first-hand detail looks like

Compare two versions of the same sentence. The values in the right-hand column are placeholders to show the form. They are not data.

Generic, could be written by anyone	First-hand (example wording)
"Our pumps deliver consistent dosage."	"Example: output is checked on a sample from every batch, weighed on a bench scale after priming, and recorded against the drawing tolerance."
"We use high-quality materials."	"Example: the housing is moulded from one named resin grade. We stopped using a second grade after it stress-cracked with a customer's oil-based formula."
"Strict quality control at every stage."	"Example: the most common reject on this line is short shots at start-up, so the first parts of every run are scrapped and not inspected."
"Suitable for a wide range of applications."	"Example: we do not recommend this closure for products filled hot, because the liner deforms."

The right-hand column has four things the left lacks.

Test conditions. A number means something only with its conditions: sample size, temperature, method, instrument, and who ran the test. "Passed leak testing" is a claim. "Leak tested inverted under vacuum, method described below" is information a buyer's quality engineer can compare with their own requirement.

The tolerance you actually hold. State what your process holds in routine production, and say where that differs from the drawing or the best case. A buyer comparing suppliers wants the routine figure.

What goes wrong. Every process has typical defects, and practitioners know them. Naming the defect, its cause and how you catch it is the clearest sign that the writer has stood next to the machine.

What you would not do. A supplier who says "we do not recommend this for that" is more believable than one who says everything suits everything. It also saves both sides a wasted enquiry.

Google's guidance on helpful, people-first content asks whether content clearly demonstrates first-hand expertise and depth of knowledge, and whether it has easily verified factual errors. The same document says E-E-A-T is not itself a specific ranking factor. So treat these details as what a buyer needs. Do not treat them as a score to be raised.

Capture it from an engineer in ten minutes

Engineers rarely have time to write and usually have time to answer. Record the conversation with their permission and ask about one product or one process per session. Do not ask "tell me about quality". Ask questions that can only be answered with specifics.

Minutes 1 to 3: the test

1. Which test on this product do customers ask about most?
2. How exactly do you run it? What equipment, how many samples, and what counts as a pass?
3. What result do you normally get, and what result would make you stop the line?

Minutes 4 to 6: the process

4. Which dimension or property is hardest to hold, and what do you hold it to in normal production?
5. What is the most common defect on this line, and what causes it?
6. What did you change in the last year or two that made the biggest difference?

Minutes 7 to 9: the buyer

7. What do buyers most often get wrong when they specify this product?
8. What do you ask a new customer before you quote?
9. When do you tell a customer this product is the wrong choice?

Minute 10: the check

10. Is there anything I should not publish, and which of these numbers may I state?

Question 10 matters as much as the other nine. Some figures are confidential, some belong to a customer, and some are true only for one machine. Write down what was cleared.

Afterwards, type up the answers the same day, mark each number with its conditions, and send the notes back to the engineer for a yes or a correction. Those notes are your source material for several articles, not only one.

Blogo builds this step into its workflow. Before it writes an article it asks you three to five specific questions about your first-hand experience, and it does not start writing until at least one has a usable answer. You can answer "I don't know" to any single question.

Put a real name on it

Google's guidance asks whether it is self-evident to visitors who authored the content, whether pages carry a byline where one would be expected, and whether the byline leads to further information about the author.

For a manufacturer, that means:

• The byline is a real person at your company who wrote the article or reviewed it. If marketing wrote it and an engineer checked it, say both: "Written by" and "Technical review by".
• The name links to an author page with the person's role, how long they have worked with the product, and the areas they cover. Two or three true sentences are enough.
• The markup matches the page. If you use Article structured data, Google's Article documentation recommends listing all authors shown on the page and putting only the name in the name field, without a job title or the publisher's name. It also suggests an author URL pointing to a page that identifies the person.
• The role is the real one. A sales engineer is a credible author for an article on specifying a product. Do not promote them to "Chief Scientist" for the byline.

On AI use, the same Google guidance suggests asking whether the use of automation is self-evident to visitors through disclosures or in other ways. A short line such as "Drafted with AI assistance, reviewed by" followed by the reviewer's name is honest and tells the reader who stands behind the facts.

If nobody at your company will put their name to an article, it is not ready to publish.

What never to fake

AI drafting makes all of the following easy to produce. Each one is checkable by a buyer, and one discovered fake puts every true statement on your site in doubt.

• An author who does not exist, or a stock photo with an invented name and career.
• Credentials and job titles the person does not hold.
• Test results you did not obtain, or results from one sample presented as routine.
• Certifications you do not hold, have let lapse, or that cover a different product line or site.
• Customers, case studies and testimonials that did not happen, or real ones used without permission.
• Factory photos that are stock images or pictures of someone else's plant. Use your own, however plain.
• Experience stories: "last quarter a customer came to us with" followed by an event that never took place.
• Years in business, capacity and headcount rounded up.

The rule for an AI-assisted draft is simple to state. The model may arrange and explain what you know. It may not supply what you know. Any first-hand statement in the draft must trace back to a note from a real person at your company. If it does not, delete it.

A check before publishing

• At least three details in the article came from your own production and are not on any competitor's page.
• Each number has its conditions next to it.
• The engineer who supplied the details has read the final text.
• The byline names a real person and links to their page.
• Nothing in the list above appears.

Blogo enforces one of these mechanically: a draft whose author name is blank or still a placeholder is flagged as not ready until a real person's name is set. The other four are yours to check.