Our team uses use machine learning and mechanistic interpretability to understand exactly why AI systems recommend a brand, then make yours the brand they recommend.
We cover the main AI ecosystems including Google, OpenAI and Anthropic. This includes AI Mode, AI Overviews, Gemini App as well as ChatGPT, Perplexity, Copilot and Claude Models.
Book a conference call with our senior strategy team to discuss your project in detail.
Engaged by global brands.
How we work
01
Map Connections
We nurture a strong culture of testing and measuring. We like to know what works, what doesn’t work, and most importantly, we like to know why.
02
Find Connection Strength
We innovate all the time. It’s in our DNA. When working together with your team we’re very likely to come up with something that’s never been done before.
03
Selection Rate Optimization
We see ourselves as an extension of your team and take great care to ensure that you understand our work. Our best campaigns are based on strong collaboration.
Your page can rank #1 and still never appear in an AI answer.
When someone asks a question in ChatGPT, Gemini, Perplexity, Claude, or Google AI Mode, the model runs its own search, pulls a handful of competing pages, and decides — in a single pass — which source to trust, which passage to quote, and which brand to recommend. That decision isn’t driven by keywords or backlinks. The model weighs clarity, relevance, structure, evidence, specificity, and how completely your content answers the question. Most pages were never written for that evaluation.
Content Optimizer is built for it.
Optimize a single page against a single query, or batch hundreds of page-and-query pairs in one pass. Content Optimizer reuses its research across the batch, so overlapping competitors are analyzed once — and you get a portfolio-level view of where you’re winning and where you’re not.
Every run turns analysis into changes your team can act on.
See your page measured against the exact competitors an AI would weigh for a query, and understand why one source is preferred over another.
See which content attributes helped or hurt your page in the model’s evaluation — and where the next gain is most likely to come from.
Optimization results turned into a clear, actionable editorial brief — the concrete page edits your writers can execute straight away.
Sharpen the specific passages AI systems are most likely to quote, cite, or summarize when they answer on your topic.
A plain-English summary of what worked, what didn’t, and the key insight from the run — so anyone on the team can follow the reasoning.
Optimizes for the decision, not the ranking
Traditional SEO optimizes for where you sit on a results page. Content Optimizer optimizes for something different: whether an AI grounding system would choose to quote you.
The mechanism is direct. When an AI assistant answers a question, it compares competing sources and selects the most quotable passage. Content Optimizer simulates that exact decision with an AI ranker — then iteratively rewrites your page, or the snippet an AI would lift from it, until the ranker prefers your content over the competition.
It isn’t a checklist or a static score. It’s a measured contest, run round after round, until your page is the one the model picks.
This is not traditional SEO
It works alongside your SEO — but it optimizes for a different moment in the user’s journey.
A single, transparent loop you can watch round by round.
Start with the question, entity, or search intent you want your page to win.
Content Optimizer pulls the live results for that query and gathers the competing pages an AI assistant would actually encounter and weigh.
An AI ranker scores your page against those competitors, using multiple independent samples for a stable, trustworthy starting rank.
Each round, the engine forms a hypothesis, applies a targeted edit, and re-runs the ranker. Changes that improve your rank are kept; the rest are discarded.
The loop repeats until your content is the preferred source — or until you’ve seen exactly which changes move the needle and which don’t.
When the run finishes, you get a plain-English narrative of what worked and a content brief of concrete edits to apply.
Client Success: OWAYO
| Metric | Apr 15, 2026 | May 31, 2026 | Percentage Points Up | % Increase |
| Share of Voice | 2.18% | 3.87% | +1.69 | +77.52% |
| Mention Share | 2.06% | 4.37% | +2.31 | +112.14% |
| Citation Share | 2.30% | 3.38% | +1.08 | +46.96% |
At the start of the AI Visibility campaign OWAYO wasn’t being recommended in AI assistant chat sessions and AI Mode for audiences in the USA.
Using our bayesian content optimizer we found that the brand was overly EU-centric causing models to withhold recommendations for the audiences in the USA.
An on-site optimisation followed by a 6 month off-site brand alignment campaign resulted in OWAYO’s AI visibility by up to 90% per entity and 2% global uplift for all targeted entities.
FAQs
Does this replace my SEO?
No — it complements it. Traditional SEO gets your page into the competitive set an AI assistant considers. Content Optimizer helps you win selection once you’re there, so you’re the source that gets quoted and recommended.
Which AI models does it optimize for?
It models the source-selection behavior of the major AI search systems — Google AI Mode, ChatGPT search, Perplexity, Gemini, and Claude — and gives you rank-factor attribution broken down per model, so you can see what works where.
Do I have to rewrite my whole page?
No. Snippet mode tunes a single extractable passage. Page mode proposes targeted, line-level edits rather than a full rewrite. You always decide what to apply.
Will optimized content be penalized by Google?
No. The changes improve clarity, structure, evidence, and specificity — the same qualities that serve human readers. There’s no keyword stuffing and no manipulation; the page simply answers the question better.
How long does a run take?
A single snippet run completes quickly. Page-mode and batch runs take longer because they make more changes and test more competitors. Every run ends with a narrative summary and a content brief.
Can my editorial team keep control?
Yes. Human-in-the-loop mode lets your team choose each change and write it themselves, while the AI ranker keeps an objective score of whether it actually improved your standing.
What do I need to get started?
A page — or a set of pages — and the queries you want to win. We handle the competitive research, scraping, ranking, and reporting.
DEJAN’s methodology transcends traditional AI SEO, diving into the core mechanics of LLMs to provide actionable intelligence for AI visibility. Our approach is built on:
Testing.
We nurture a strong culture of testing and measuring. We like to know what works, what doesn’t work, and most importantly, we like to know why.
Innovation.
We innovate all the time. It’s in our DNA. When working together with your team we’re very likely to come up with something that’s never been done before.
Collaboration.
We see ourselves as an extension of your team and take great care to ensure that you understand our work. Our best campaigns are based on strong collaboration.
Meet our core team
We’re an all-senior team with experience in a wide range of projects and industries.
Mike Jolly
Director of Strategy
Blake Walsh
SEO
Giordano Chng
SEO
Liam Buttery
SEO
Dan Petrovic
AI SEO
Martin Reed
Technical SEO
Bianca Hall
Public Relations
Alex Petrovic
SEO
Danielle White
Operations
Milos Dosen
CFO
Josip Ivanovic
Developer
Nemek Nowaczyk
PPC
Dragan Grubacki
Technical SEO
Finn Arrowsmith
Outreach
We were given our very own bespoke internal link recommendation engine that leverages world-class language models and data science. It’s one thing to theorize about the potential of machine learning in SEO, but it’s entirely another to witness it first-hand. It changed my perspective on what’s possible in enterprise SEO.
Scott Schulfer
Senior SEO Manager
Zendesk
Featured In
Dan Petrovic, an academic and consultant on SEO and generative AI, said Google’s size, expertise and massive trove of search data gave it a massive advantage, but that Gemini 3 Pro would probably be a more expensive model to run.
Tim Biggs, The Sydney Morning Herald
Dan Petrovic made a super write up around Chrome’s latest embedding model with all the juicy details on his blog. Great read.
Featured in “Moz Top 10“, twice.
Moz Recommended Agency
Book a conference call with our senior strategy team to discuss your project in detail.