Which GTA Agencies Do AI Engines Actually Recommend? A Measured 2026 Audit

TL;DR — What We Found

We tracked 10 GTA digital marketing and SEO agencies across 5 AI platforms (ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, and Gemini) over a 3-week window ending June 18, 2026, using Ahrefs Brand Radar on a set of buyer-intent prompts. Every number below is a measured output — no estimates, no projections.

• No single agency wins every platform. The competitive landscape splits along platform lines — the top brand on ChatGPT scores 0% on Google AI Overviews and AI Mode; the top brand on AI Overviews scores 0% on ChatGPT and Perplexity.
• Search Engine People leads conversational AI — 91.7% Share of Voice (SOV) on ChatGPT, 86.3% on Perplexity, and 53.3% on Gemini.
• dNovo Group owns Google’s AI surfaces — 77.8% on AI Mode and 70.6% on AI Overviews.
• Thread Digital dominates AI Overviews at 90.0% SOV.
• The top non-competitor citation sources are directories, not agency websites. digitalagencynetwork.com (10 responses) and agencies.semrush.com (7 responses) are the pages feeding AI recommendations — cited more often than any individual agency’s own site. Reddit appeared in 4 responses.
• Digital Estate Media’s own baseline: 0% SOV on 4 of 5 engines, 11.9% on Gemini. Named in zero ChatGPT or Perplexity answers. Our domain was cited as a source in 4 responses — the engines find our content, but do not yet include us in the recommended set. We’re publishing this baseline because the gap is the story.

Why Are AI Recommendations the New First Page of Google?

When a prospect asks ChatGPT or Gemini “who’s the best SEO agency in Toronto?”, they typically receive a short, confident list of names — and they act on it. That list is not a Google SERP; there is no page two. An agency that isn’t in that set effectively does not exist for that query.

This creates a new visibility problem that traditional SEO metrics don’t capture. Rankings, impressions, and clicks don’t tell you whether an AI engine is naming your agency to a prospective client. Share of Voice in AI responses is a separate signal, driven by different inputs — and right now almost no agencies are actively tracking it.

We designed this audit to measure that gap with real data. The goal is a reproducible benchmark: one that other agencies can cite, dispute, or replicate for their own market.

How Did We Measure This?

Tool: Ahrefs Brand Radar, report “DEM GTA Agency Tracker” (report ID 019e627e-cab2-77c5-810d-f952fe40b4f9, created 2026-05-26). The report runs weekly captures across custom GTA-agency buyer prompts and returns per-platform Share of Voice.

Platforms tracked: ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, and Gemini.

Measurement window: approximately 3 weeks of weekly captures ending June 18, 2026.

Tracked prompts (ChatGPT sample, by search volume):

What “SOV” means here: Share of Voice = a brand’s proportion of AI responses (to the tracked prompts) that mention it. On multi-brand-answer platforms such as Gemini and AI Overviews, per-brand SOV can sum to more than 100% because a single response often names several agencies. Numbers are reported per platform and never blended — averaging across platforms would obscure the most actionable finding (which engine to prioritize for which brand).

What “mention” vs. “citation” means: being named or recommended in the answer text is a mention. Being linked as a source domain is a citation. These are tracked separately. A cited domain doesn’t always earn a recommendation; a recommended brand isn’t always linked. Both matter, but they are different signals.

Caveats — read these before the numbers:

• This is a single 3-week snapshot. Brand Radar rebuilds weekly, so the trend line will grow over time. Re-baselining quarterly is the right cadence.
• Correlation is not causation. SOV rank tells you who appears, not definitively why. The cited-domain analysis below is correlational evidence for the directory-inclusion hypothesis, not controlled experimental proof.
• One prompt in the tracked set, “aeo services Toronto” (933 monthly volume), was found to be contaminated: ChatGPT returns customs-broker results because “AEO” also means Authorised Economic Operator, not Answer Engine Optimization. That prompt’s responses were excluded from interpretation, and we recommend removing or rewording it in any reproduction of this study.

Who Does AI Recommend? The Full Competitive Leaderboard

The table below shows measured Share of Voice across all five platforms for the tracked GTA agencies. A 0% means the brand did not appear in any tracked response on that platform during the measurement window.

All figures measured 2026-06-18. SOV = share of AI responses mentioning the agency for the tracked GTA-agency prompt set. Data source: Ahrefs Brand Radar — DEM GTA Agency Tracker (measured 2026-06-18).

The most striking pattern is how completely the platforms diverge. An agency that dominates one engine can be invisible on another. This is not a single race.

Does Any Agency Win Every Platform?

The short answer is no. Each engine has a different leader, and the gap between leaders is stark.

ChatGPT and Perplexity: one brand captures almost everything

On both conversational AI engines, Search Engine People holds near-monopoly visibility (91.7% on ChatGPT, 86.3% on Perplexity). No other tracked agency exceeds 14% on either platform, and eight of the ten agencies score 0% on ChatGPT. In the context where buyers are most likely to have an open-ended “who should I hire?” conversation, one brand owns the answer.

Google AI Overviews: a different winner entirely

Thread Digital leads AI Overviews at 90.0% SOV, with dNovo Group (70.6%) and Convex Studio (67.7%) as runners-up. Search Engine People — the brand that dominates ChatGPT — scores 0% here. The content model driving AI Overviews visibility is measurably different from what drives ChatGPT recommendations.

Gemini: the most distributed platform

Gemini shows the widest spread of any platform tracked. Search Engine People leads (53.3%), but five other agencies have measurable SOV: dNovo Group (31.5%), Qode Media (30.4%), Thread Digital (26.3%), Digital Estate Media (11.9%), and MediaForce (7.5%). Gemini appears to surface a more diverse consideration set per query, which lowers the floor for smaller agencies to appear.

Google AI Mode: dNovo Group’s territory

dNovo Group leads AI Mode at 77.8% SOV. Seologist (22.2%) and Ignite Digital (22.2%) are the only other agencies with any presence; seven agencies score 0%. dNovo Group shows the mirror image of Search Engine People — invisible on conversational engines, dominant on Google’s AI surfaces.

The strategic implication

Treating “AI visibility” as a single metric will mislead you. A single-platform strategy produces single-platform visibility: agencies tracking only ChatGPT will miss the Google AI surfaces entirely, while agencies optimizing for AI Overviews may remain invisible in conversational results. The data here show these are genuinely distinct competitive environments, not noise — so you need a per-platform view.

Where Do AI Engines Actually Get Their Recommendations From?

This is the most actionable finding in the audit.

When we look at which domains AI engines cited as sources across all platforms during the measurement window, the picture is clear: the top sources are third-party directories and listicle pages, not individual agency websites.

All figures from the Brand Radar cited-domain data for the same three-week window.

digitalagencynetwork.com appeared in 10 responses — more than any individual agency’s own website. agencies.semrush.com appeared in 7. Reddit, with no SEO content at all, appeared in 4 — the same count as DEM’s own site.

The implication is direct: AI engines appear to build their recommended agency lists by reading the same third-party pages a human researcher would consult, then synthesising those into conversational answers. If your agency is not listed on the pages these engines cite, you are unlikely to appear in the recommendations they produce. Getting listed on those pages is not merely a citations play for SEO — it is, on this evidence, a primary mechanism by which AI recommendations are constructed.

This also explains a specific finding in our own data. digitalestatemedia.com was cited as a source in 4 responses, which means AI engines are indexing and reading our content. But DEM did not appear in the named-recommendation set on 4 of 5 platforms. The content is visible; the brand is not yet in the directory and listicle pages that produce named recommendations. That is a precise, solvable gap.

How Do AI Engines Decide Who to Recommend?

Understanding how these answers get built clarifies what you need to do to appear in them. Retrieval-augmented AI systems do not independently evaluate agency quality from scratch — they retrieve passages from indexed web content and synthesise them into a ranked answer. The question is therefore which web content gets retrieved. For GTA agency queries, our citation data points to three main source types:

1. Directory and listicle pages — curated, regularly crawled, and already formatted as recommendation lists, making them easy for AI to extract and present. These were the two highest non-competitor sources in our dataset.
2. High-authority competitor sites — agencies with substantial content depth (e.g. threaddigital.ca at 6 citations) have their own pages cited directly.
3. Community content — Reddit appeared in 4 responses, suggesting conversational engines weight peer-generated discussion alongside published lists.

The named-agency SOV figures reflect which agencies appear across these source types with enough frequency that engines produce them as consistent recommendations.

How Do You Actually Get Into the AI-Recommended Set?

The citation-source data makes the pathway concrete. These levers trace directly to the measured outputs — not general GEO advice. We are using this sequence ourselves.

1. Get onto the pages AI engines actually cite.

digitalagencynetwork.com (cited in 10 responses) and agencies.semrush.com (7 responses) are the two highest-frequency citation sources in our dataset. These pages are directly upstream of AI recommendations — an agency that is not on them is not in the source pool. Semrush Agencies is a free listing for verified agencies; getting a complete, accurate listing on both should be treated as a prerequisite, not a nice-to-have. sortlist.com (2 responses) is a lower-impact secondary target in the same category.

2. Build or activate a Reddit presence.

Reddit appeared in 4 responses — tied with our own domain. Community threads surface in AI answers because they read as peer-reviewed community opinion rather than self-promotional copy. Substantive participation in relevant subreddits (r/SEO, r/marketing, r/Toronto) builds the kind of named-brand mentions that aggregate into AI recall over time. This is about being genuinely present where buyers and AI engines both look for peer opinion — not gaming the platform.

3. Earn named mentions in editorial listicles, not just bare directory entries.

The difference between a bare directory listing and a named write-up in an editorial piece is the difference between a citation and a recommendation. Engines appear to pull from directory pages to populate candidate sets, then surface brands with enough corroborating mentions across multiple sources to appear in the generated answer.

4. Close the mention-vs-citation gap on your own site.

Thread Digital’s site (6 citations) and dNovo Group’s site (4–6 citations) appear as direct source domains — the first-party path, earned through substantial original content. If your domain already appears in AI source citations but you are not named in the recommendations (our situation), the on-page content is reaching the retrieval layer but the brand signal is not yet strong enough to trigger a named recommendation. The intervention is off-page: more named-brand mentions in third-party listicles, community discussion, and earned coverage — not more on-page content.

5. Target the right engine for the right content type.

Because the platforms behave as distinct systems, strategy should differ by engine. A program that improves page structure without also building off-page brand presence may move AI Overviews numbers without moving ChatGPT numbers — and vice versa.

What Is Digital Estate Media’s Actual Baseline?

We are the agency that ran this audit, so our own baseline is part of the dataset — and we are publishing it exactly as measured.

DEM appeared in zero ChatGPT and Perplexity link sets across all tracked prompts. On Gemini, we hold 11.9% SOV — mid-pack in a crowded field. Despite ~0% named SOV on four engines, www.digitalestatemedia.com was cited as a source domain in 4 AI responses, meaning the engines are reading our content but not yet naming us as a recommended agency.

Publishing this number rather than hiding it is deliberate. An agency running AI-visibility research and concealing its own below-average baseline would undermine the credibility of everything else in the article. The 11.9% on Gemini is a real signal — on-page GEO work is producing some visibility. The 0% on ChatGPT and Perplexity tells us exactly where the off-page and directory work needs to happen next. This is the starting line, and the playbook above is the one we are running to move it.

What Does This Mean for GTA Businesses Hiring an Agency?

The practical takeaway is narrow but clear: an agency’s AI recommendation rate reflects its third-party reputation footprint, not just its own website. If an agency you are evaluating has no presence on digitalagencynetwork.com, Semrush Agencies, or similar directories — and no Reddit or community footprint — it likely has a thin external citation profile. That affects its ability to help you build yours.

A fair question to ask any agency you’re considering: what is your own AI recommendation SOV, and where does your domain appear in AI citation sources? If they haven’t measured it, that’s worth factoring into your evaluation.

Methodology Summary and How to Reproduce This

Tool: Ahrefs Brand Radar with custom GTA-agency prompt sets, running on an ongoing weekly cadence. The data in this article represents the 3-week window ending June 18, 2026.

Limitations to carry forward:

• Per-platform only. SOV figures are never blended; averages across platforms are meaningless here because the platforms behave as distinct systems.
• Snapshot, not trend. A 3-week window establishes a baseline but cannot confirm causal relationships. Results will shift as agencies adjust strategy, as AI retrieval systems update, and as the directory ecosystem changes. Quarterly re-baselining is the right cadence.
• Mention ≠ citation. Being named in an answer and having your domain linked as a source are separate metrics, reported separately.
• Scope. This covers ten agencies on a GTA-specific prompt set. Agencies outside the tracked set, or performance on national or vertical queries, are not captured here.
• The “aeo services Toronto” prompt was excluded from interpretation due to the customs-broker contamination described above.

To run your own version: configure Ahrefs Brand Radar with your target brand set and a prompt list calibrated to your buyers’ language. Run prompts fresh (no personalisation, consistent locale) on each platform separately, recording which agencies are named, their position in the answer, and which domains are linked as citation sources. Track weekly for at least 4 weeks before drawing directional conclusions, and never blend platforms into a single metric — the divergence is the finding.

Data collected using Ahrefs Brand Radar, report window ending June 18, 2026. All figures are measured outputs — no estimates or projections. Re-baseline planned for Q3 2026.