Getting an AI to Describe Your Business Correctly

Try this before you read further. Open any AI assistant and ask it about your business by name. Then ask it who does your kind of work in your county, and write down both answers.

For most local businesses, the first answer is partly out of date and the second leaves them out. You can fix both. The fix is reconciliation, which is plain work.

What a model is doing

A generative engine holds no record for your business the way a database would. It has a statistical impression built from everywhere your name appears, weighted by how often and how consistently it shows up. Sources that agree give it a sharp picture. When they disagree the picture blurs, and a blurry business gets left out of the answers that name the sharp ones.

So the job is getting your sources to agree with each other.

The audit: find every version of you

Start by collecting what’s out there. Search your business name, your phone number in quotes, and your old addresses. You’re looking for contradictions.

1. Your own website. Is the address in the footer the one you’re in?
2. Google Business Profile, Bing Places, Apple Business Connect
3. Facebook, LinkedIn, and any industry directory you joined once
4. Chamber of commerce and trade association listings
5. Yelp and the aggregators that scrape it, which is most of them
6. Old press coverage and partner sites that describe you in their own words

You’ll commonly find a suite number that no longer exists and a phone number from a line you cancelled. The service list will match whatever you sold when the listing went up. A model may give weight to every one of those.

Write the canonical description once

Before you fix anything, decide what’s true and write it down in one place. That means your legal name, the name you trade under, address, phone, hours, service list, and a two-sentence description. Every listing you touch afterward gets checked against that document.

People who skip this step usually end up adding a fourth version of their details to the three already out there.

Decide what the sources should say before you try to make them agree.

Fix your own site first

Your site carries a lot of weight because it’s the primary source. Put the canonical facts in the footer of every page. Mark up an Organization or LocalBusiness node with the same values, and give each service a page named with the words your customers use.

That last point matters more than it sounds. Call it "infrastructure modernization" when customers call it "moving our old database," and the model learns a phrase nobody searches for.

Then the listings, in order of weight

Do Google Business Profile first. It’s the most heavily weighted single source for a local business and the easiest to correct. The other mapping platforms come next, then the big aggregators, and the long tail last. Each long-tail listing counts for little on its own and adds up across dozens, so give it an afternoon and skip the subscription.

Give the model something to say

Consistent details stop a model from describing you wrong. Getting named when someone asks "who does this in my area" takes more, because the model needs something specific to name you for.

That means a page about the specific work you do in a specific place, with its constraints spelled out. "We serve Southeast Ohio" won’t do it. A page about what the work looks like in Marietta, with details you’d only know from doing it there, gives a model a reason to mention you.

Measure it by asking

Nobody offers a dashboard for this. We keep a fixed list of prompts, run it every month across the major assistants, and log the answers. After a few months you can see whether you’ve moved from absent to mentioned to named first, and whether the description of you has caught up with the last three years.

That prompt log goes into the monthly report on managed search. With no public metrics to point to, asking is the honest way to measure it.