Does each AI engine recommend differently?

Yes — each one leans on different sources, so being recommended by one doesn’t mean you’re recommended by all. Here’s the pattern we see across our audits:

Based on Oracite’s own audits across engines, June 2026. Tendencies, not fixed rules — and they shift as the engines update. Microsoft Copilot joined our audit panel in August 2026 and isn’t in this table yet, because we haven’t run enough of it to say anything we’d stand behind.

The practical takeaway: visibility is earned engine by engine. A win on Perplexity (primary sources, named experts) is built differently from a win on Google’s AI (clean, consistent business information). For most local and everyday searches, Google’s AI has the biggest audience — and it’s the hardest to crack, because it lists many options per question.

What that means in practice

The differences aren’t academic — they change what you’d actually do first.

• If ChatGPT matters most to you, the community and editorial record does the heavy lifting. Being discussed in the places people genuinely discuss your category counts for more than another page on your own site.
• If Perplexity matters most, named expertise travels. It will cite a specific person, so a practitioner with a real, attributable presence — author of something, quoted somewhere, clearly credentialed — has an advantage over an anonymous brand.
• If Google’s AI matters most, and for most local businesses it does, the win is unglamorous: structured data, a complete and accurate Google Business Profile, and business information that says the same thing everywhere.
• If Claude matters most, well-sourced, clearly documented reference pages are what get picked up — depth and citation over marketing copy.

Don’t optimise for one engine

It’s tempting to pick the engine you personally use and aim at it. Two reasons not to.

First, your customers aren’t all using your engine, and the split isn’t stable. Second, these tendencies are tendencies — they shift as engines update, retrain and change what they lean on. A strategy tuned tightly to one engine’s current habits is a strategy with a short shelf life.

The signals that work across all of them are the boring, durable ones: accurate listings, genuine reviews, and clear answers to real questions.

Engine-specific work is worth doing on top of that foundation — not instead of it. The foundation is what how AI decides who to recommend covers.

How to check for yourself

Ask the same customer question — “best [what you do] in [your town]” — in each engine separately, in a fresh session, and write down who gets named and which sources each answer cites. The differences between the four answers are the map: they tell you which sources each engine is reaching for in your category, and therefore where your absence is costing you.

Answers also vary by person, location and over time, so a single run is a snapshot rather than a finding. That’s why we re-run the same questions monthly and seal each report, and why our own public record shows the engines disagreeing about us too — in our August 2026 self-audit, Google AI Overviews named us in none of its answers while other engines did.