# Generative Engine Optimisation (GEO)

URL: https://goreview.uk/services/geo/
Updated: 2026-09-19

> Generative Engine Optimisation shapes the sources a generative engine retrieves and trusts, so your business is named in the recommendation with accurate, positive context.

## You cannot edit the answer, but you can change its inputs

When someone asks an assistant to recommend a business like yours, the reply is assembled from sources the model retrieves and trusts at that moment. You have no direct control over the output. You have a great deal of control over the inputs.

Generative Engine Optimisation is the work of shaping those inputs. It means being present, consistent and well reviewed on the third party sources these engines lean on, and making your own site the clearest available statement of the facts. Where AEO targets a specific answer slot, GEO targets the recommendation itself.

## Why third party sources dominate

Generative engines are cautious about taking a business at its own word. A claim on your homepage that you are the best rated plumber in Leeds carries almost no weight. The same claim implied by a hundred recent reviews across Google and Checkatrade, a consistent listing in the directories that cover your trade, and a mention in a local publication carries a great deal.

This is the single biggest difference between GEO and traditional SEO, and the reason our reputation platform is part of the service rather than an upsell beside it.

At a glance

Best for Businesses competing on recommendation rather than price 

First results Three to six months. Source building is slow and compounding. 

Pairs with Reputation Management, AI SEO, LLMO 

Deliverable Source audit, citation building, sentiment tracking and prompt reporting 

## What the work involves

Find the sources, fix what they say, then measure the answer.

### Source audit

We run your buying prompts through the major assistants and record which sources they cite. The answer is rarely what businesses expect. It is usually a mix of directories, review platforms, local press and a competitor comparison page, and almost never the business website alone.

### Fix the record

Name, address, phone, hours, services and pricing corrected everywhere they appear. Contradictions between sources are the fastest way to be dropped from a recommendation, because the model has no way to decide which version is true.

### Build corroboration

Review volume and recency on the platforms that feed your sector, listings on the directories the engines actually cite, and earned mentions where they are realistically achievable for a local business.

### Sentiment, not just presence

Being named is not enough if the summary that follows mentions slow response times. We track the adjectives that appear alongside your business and work the underlying cause, which is usually a pattern sitting in your reviews.

### Competitive framing

Where comparison content decides the recommendation, we make sure an accurate, current description of your offer exists in the places those comparisons are drawn from.

## How we report it

A fixed panel of buying prompts, run monthly across the major assistants. For each one we record whether you were named, your position in the list, which competitors appeared, the sentiment of your mention, and the sources cited. Trends matter more than any single run, because these systems are not deterministic.

60k+
Reviews collected by GoReview, the corroboration layer this service depends on

## Questions

GEO, AEO, LLMO. Are these not the same thing?They overlap and the industry uses them loosely. We separate them by target. AEO goes after the answer slot on a search result. GEO goes after the recommendation a generative engine produces and the sources behind it. LLMO is the technical work of making your own content ingestible by language models. Most clients buy a plan that draws on all three rather than one in isolation.

Can competitors game this against me?Fabricated reviews and fake listings do happen. They are also increasingly detected and penalised. Our answer is defensive: a high volume of genuine recent reviews and consistent verified listings makes an outlier attack statistically obvious rather than damaging.

How do I know it is working?The prompt panel. If you are named in two of ten buying prompts in month one and seven of ten in month six, that is the result. We show the raw responses, not just a score.