Writing / GEO in production

Local GEO: how a multi-location network becomes citable by AI

Damien Martiré · July 2026 · 6 min read

A multi-location network becomes citable by AI when each individual location is a clean, consistent, verifiable entity, not when the brand shouts louder. When someone asks an assistant “where can I get my car repaired in Lyon?”, the model does not reward your national brand. It reaches for whichever local business it can resolve confidently: matching name, address and phone across sources, a real presence at that postcode, and structured data that says exactly what happens there. That is the whole game.

I run SEO and GEO for DELKO, a French network of roughly 168 franchised car-repair centres. These are field notes from that work, not a theory. I will separate what is proven from what is a working hypothesis, because on this topic a lot of confident advice is neither.

Why does local search now run through AI answers?

Local intent is one of the fastest-moving surfaces in AI search, and it matters because “near me” questions convert. Google’s AI Overviews rolled out to over 100 countries and territories in October 2024, reaching a billion users a month, and expanded to 200+ countries and 40+ languages by May 2025 (Google, 2024). Local discovery is squarely inside that surface now: BrightLocal’s 2026 research found nearly half of consumers had used an AI tool such as ChatGPT, Gemini or Perplexity to find a local business in the past year, up from a small minority the year before (BrightLocal, 2026).

Assistants like ChatGPT, Perplexity and Google’s AI mode now sit between that intent and the click. The practical shift: a network no longer just competes for the map pack. It competes to be the sentence an assistant generates when a human asks a very local question.

What actually makes a single location “resolvable” to a model?

A location becomes resolvable when a model can cross-check its identity without hitting a contradiction. In my experience the deciding factor is not volume of links, it is the absence of conflicting facts. If your name, address and phone (NAP) disagree between your own site, Google Business Profile, PagesJaunes and the official company register, the model has no confident answer to anchor to, so it picks a competitor it can verify.

For DELKO I built a per-location NAP scoring system: every centre gets a 0-100 consistency score, recomputed on a schedule. It cross-references three official sources, the French business register (INSEE/SIRENE), the national address base, and directory listings, then flags mismatches. The subtle failures were the interesting ones: a SIRET number pointing at the holding company or the property SCI rather than the workshop itself. To a human that looks fine. To a machine trying to confirm “is there a car garage at this address?”, it is a contradiction that quietly disqualifies the location.

For a multi-location brand, AI citability is decided at the individual location level. Each site must present a consistent name, address and phone across its own pages, Google Business Profile, directories, and the official business register; a single contradiction lets the model default to a verifiable competitor instead.

How is local GEO different from ordinary local SEO?

Local GEO shares the plumbing of local SEO but changes the target: you are optimising for a generated sentence, not a ranked list. Classic local SEO fights for map-pack position and the ten blue links. Local GEO asks a harder question: when a model composes an answer about “car repair in [town]”, does it have enough clean, structured, corroborated data to name you as the local option, with a source it trusts?

The overlap is large but not total. Map-pack ranking rewards proximity, reviews and prominence. AI citation seems to reward something adjacent but distinct: disambiguation. A model needs to be sure which entity it is talking about before it will name it. So the highest-leverage work for a network is not chasing more citations, it is removing every reason for a model to be uncertain about which of your 168 locations serves which town.

The two layers you have to keep aligned

Entity layer: one unambiguous identity per location (NAP, structured LocalBusiness data, the official register entry).
Content layer: local pages that answer real local questions (“clutch replacement in [town]”, opening hours, services) in extractable prose.

What can you do at scale across 168 locations?

At network scale the only workable approach is systematic, because you cannot hand-tune 168 pages weekly. My working direction for DELKO has three moves, and I am honest that these are the direction I set rather than results I can already prove with citation-share numbers.

Fix the entity layer first. Score every location’s NAP consistency, then correct the real mismatches. A clean register entry (correct SIRET, correct business activity code for a garage, not the holding) is the foundation everything else sits on.
Make each local page independently extractable. Answer-first paragraphs, explicit town and service names, structured data. A model should be able to lift one self-contained sentence that says what this centre does and where.
Corroborate across sources. The same facts, identical, on the site, the Business Profile and the directories the model is likely to read. Consistency is the ranking factor here.

The audit that surfaced most of the value crossed the official company register against the yellow-pages listings against the national address base. Roughly ten of 168 locations had a genuine anomaly. That is a small percentage, but each one was a location a model could not confidently resolve, and those are exactly the ones you lose in a generated answer.

What is proven, and what is still a hypothesis?

Let me be precise, because this field rewards overclaiming and I would rather not. Proven: AI Overviews and assistant answers now intercept local queries at real scale, and inconsistent NAP data measurably weakens local presence, which is long-established local-SEO ground (BrightLocal has documented consistency and review effects for years). Also proven, for us: the per-location scoring surfaces concrete, fixable contradictions.

Hypothesis, honestly held: that improving per-location disambiguation raises the rate at which assistants name specific DELKO centres in local answers. It is a reasonable bet, entity clarity is exactly what these systems need, but I do not yet have a clean before/after citation-share figure across the network, and I will not invent one. The direction is set; the measurement is the next job.

FAQ

Does a strong national brand make my locations more citable by AI?: Not directly. Brand strength helps recognition, but for a “car repair in [town]” query, models resolve the specific local entity. A well-known brand with contradictory local data still loses to a smaller competitor whose single location the model can verify without conflict.
Is NAP consistency really still relevant in the AI-search era?: More than ever, in my experience. Consistent name, address and phone across your site, Google Business Profile, directories and the official register is what lets a model confirm which entity it is describing. Contradictions do not just lower rankings; they can remove you from a generated answer entirely.
Where should a multi-location network start?: Start with a per-location entity audit before touching content. Cross-check each location’s official register entry, address base and directory listings, score the consistency, and fix the real mismatches first. Clean local pages built on contradictory identity data will not earn reliable AI citations.