Getting an AI to Describe Your Business Correctly

Try this before you read further. Open any AI assistant and ask it to tell you about your business by name, then ask it who does your kind of work in your county. Write down what comes back.

For most local businesses the first answer is partly stale and the second does not include them. Both are fixable, and the fix is not clever — it is reconciliation.

What a model is actually doing

A generative engine does not have a record for your business the way a database does. It has a statistical impression assembled from everywhere your name appears, weighted by how often and how consistently. When those sources agree, the impression is sharp. When they disagree, it blurs, and a blurred entity gets left out of answers where a sharp one gets named.

So the work is not persuasion. It is making the sources agree.

The audit: find every version of you

Start by collecting what exists. Search your business name, your phone number in quotes, and your old addresses. You are looking for contradictions, not compliments.

1. Your own website — is the address in the footer the one you are 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 words

It is common to find a defunct suite number, a phone number from a line you cancelled, and a service list that matches what you sold when the listing was created. Every one of those is a source a model may weight.

Write the canonical description once

Before fixing anything, decide what the truth is and write it down in one place: legal name, the name you trade under, address, phone, hours, service list, and a two-sentence description. That document is the reference for every listing you touch afterwards.

Without it, cleanup produces a fourth variant. With it, everything converges.

You cannot make the sources agree until you have decided what they are agreeing on.

Fix your own site first

Your site is weighted heavily because it is the primary source. Put the canonical facts in the footer on every page, mark up an Organization or LocalBusiness node with the same values, and give each service a page whose name matches the words people use.

The last one matters more than it sounds. If you call it "infrastructure modernization" and customers call it "moving our old database," the model learns the phrase nobody searches for.

Then the listings, in order of weight

Google Business Profile first — it is the highest-weighted single source for a local entity and the easiest to correct. Then the other mapping platforms, then the big aggregators, then the long tail. The long tail matters less individually and more in aggregate, so it is worth an afternoon and not worth a subscription.

Give the model something to say

Consistency stops you being described wrong. It does not get you named in an answer to "who does this in my area." For that, there has to be something specific to name you for.

A page about the exact work, with the exact constraints, in the exact place. Not "we serve Southeast Ohio" but a page about what that work looks like in Marietta with detail that could only come from doing it there. That is the difference between an entity a model knows exists and an entity a model has a reason to mention.

Measure it by asking

There is no dashboard. What there is: a fixed list of prompts, run monthly across the major assistants, with the answers logged. Over a few months you can see whether you have moved from absent, to mentioned, to named first — and whether the description of you has stopped being three years out of date.

That prompt log is part of the monthly report on managed search, because it is the only honest way to report on something with no public metrics.