When buyers turn to ChatGPT, Perplexity, or Gemini instead of typing queries into a search engine, traditional attribution goes blind. The AI typically recommends only two or three brands. If yours isn't one of them, the customer journey ends before your analytics even register a visit.
Vikas Malik spent two decades applying AI to complex, high-stakes industrial systems and enterprise data operations. Now, as the founder of EvidentlyAEO, he is tackling the next massive blind spot for CMOs: how synthetic engines construct brand preference, and how modern marketers must adapt from classic SERPs to unified Brand Intelligence.
Q1: You spent years working on industrial AI for power plants and large-scale industrial controls before moving into executive data roles at NeuCo, GE, and Sensor Tower. That’s a stark contrast to marketing software. What made you shift?
Vikas Malik: In industrial systems, if your control loop is miscalibrated by two percent, a turbine trips or emissions spike. The feedback loop is immediate, brutal, and mathematical. When I moved deeper into digital analytics and mobile intelligence, I realized modern marketing is dealing with an identical systems problem except the signal-to-noise ratio is exponentially worse. Marketers are swimming in disconnected dashboards, impression metrics, and attribution models that lie to them. They spend millions trying to cut through noise, but they lack closed-loop clarity. My focus has always been pattern recognition inside complex, noisy environments. Marketing didn't need another generic reporting dashboard; it needed rigorous, engineering-grade intelligence to show what actually moves the needle.
Q2: Most leaders saw ChatGPT arrive in late 2022 and viewed it simply as a copy generation tool. When did you realize it was actually going to dismantle traditional search and brand discovery?
Vikas Malik: Because I was already tracking user behavior shifts where the signal shows up months before the analytics dashboards catch up. When conversational interfaces took hold, people stopped browsing lists of ten blue links. They started asking for recommendations, trade-offs, and decisions. Inside an AI answer, there is no page two. The "first-choice recommendation" is the new above-the-fold. If an engine summarizes a category and recommends three competitors while leaving you out, you haven’t just lost a click you’ve lost the deal at the point of consideration. What stunned me was watching enterprise CMOs celebrate record direct traffic while their competitive win rates quietly cratered. They didn't realize that buyers were using AI as their primary research layer, and the models had already formed an opinion about them.
Q3: When you tell CMOs their classic SEO playbooks won’t save them in AI search, what is the core mechanism they are misunderstanding?
Vikas Malik: Legacy SEO was an extraction problem: match keywords, optimize metadata, build backlinks, and win page rank. Large Language Models don’t work that way. They don't just extract sentences; they synthesize brand identity from high-authority consensus across the entire web documentation, PR, customer forums, reviews, technical benchmarks, and community discourse. If your narrative is inconsistent across those sources, the model fills the void with whatever competitor has clearer consensus. That’s why you can rank number one on Google for an organic keyword and be completely invisible inside Claude or Perplexity for the exact same prompt. It isn't a content volume problem. It’s an entity and narrative coherence problem.
Q4: In the early days of building EvidentlyAEO, what surprised you most when you put real LLM audit data in front of marketing teams?
Vikas Malik: The shock on their faces. We’d take 30 prompts that high-intent buyers actually ask when evaluating their software or service, run them across several leading LLMs, and show them the raw output. A team that spent hundreds of thousands of dollars on content marketing would see an AI tell a prospect: "Brand X is good for small teams, but lacks enterprise security." The brand team had no idea that was the model's consensus narrative. The first response was always denial: "Can we just add a few landing pages to fix that?" That was the inflection point where people realized you can’t game an LLM with thin content; you need real-time visibility into your narrative share, mention frequency, and sentiment across every major foundation model.
Q5: The company is called EvidentlyAEO, but you frequently push back on people who label you purely as an "AEO/GEO measurement tool." What is the broader scope?
Vikas Malik: AEO and GEO are our entry point, our beachhead. If you cannot measure how AI perceives and recommends you, you are flying blind. But solving brand survival in the AI era cannot stop at monitoring search outputs. We are building EvidentlyAEO as a comprehensive 360-Degree Brand Intelligence Platform. Consider the fragmentation today: your organic team looks at rank trackers, your PR agency looks at sentiment mentions, and your performance team manages paid ads in an isolated silo. None of these systems talk to one another.We connect those dots. If our platform identifies an organic visibility deficit in how LLMs recommend your category, that insight shouldn't sit in a slide deck. It should immediately inform your paid acquisition strategy, refine your positioning, and trigger corrective action across both organic and paid channels.
Q6: How does that play out operationally? For instance, connecting organic AI intelligence to paid media and commerce?
Vikas Malik: It comes down to unifying discovery, intelligence, and execution:
●Ads Intelligence & Execution: It’s not enough to deliver insights; teams lack the bandwidth to act on them. We are linking organic AEO insights directly into our Ads Intelligence module, giving brands the capability to plan, optimize, and run their entire ad campaigns directly from the EvidentlyAEO platform to reinforce weak narrative zones.
●Agentic Commerce: Search is shifting from conversational answers to autonomous transactions. AI agents are beginning to negotiate, compare specifications, and purchase on behalf of consumers and enterprises. Our Agentic Commerce module is engineered to position brands so their products are discoverable, interpretable, and selectable by autonomous purchasing agents.
●Real-Time Brand Chatter: Sentiment isn't static. Our Brand Chatter engine monitors real-time social and conversational discussions across platforms, surfacing shifts in public perception before they get permanently baked into the training weights or retrieval datasets of major LLMs.
AEO is where we begin. Full-spectrum intelligence and closed-loop campaign execution are where we are headed.
Q7: You work with both enterprise in-house teams and growth agencies. How do their demands differ when dealing with a platform like this?
Vikas Malik: Their operational pressures are completely different.
In-house enterprise leaders want clarity, risk mitigation, and executive-level reporting: "Where are we losing market share to competitors in AI answers, and what levers do we pull to fix it before the board meeting?"Agencies, on the other hand, are looking for scalable, repeatable operating systems. They want to move away from low-margin, commoditized keyword reporting and sell high-value strategic transformation. For agencies, EvidentlyAEO serves as an operational engine giving them the audit frameworks, competitive benchmarks, and workflow tooling to deliver proven pipeline impact to their clients.
Q8: If someone evaluates your product roadmap over the next several years, what core principle defines what you choose to build?
Vikas Malik: Our fundamental promise is simple: we are not building for today or for temporary algorithmic quirks. We are building for next month, next year, and the next decade. The transition from keyword indexes to generative engines, MCP protocols, and agent-driven economies is the largest architectural paradigm shift in enterprise computing since the birth of the web. Platforms that simply wrap a few API calls around legacy SEO dashboards will be obsolete within two years. We are continuously engineering infrastructure that anticipates where machine intelligence is going next. We will never stop innovating because the surface area of how machines represent reality is continually evolving.
Q9: When a CMO finishes reading this, what is the single, non-negotiable question they should take to their next team standup?
Vikas Malik: Don't ask your team what your Google rank is for your primary keyword. Ask them this:"When an enterprise buyer asks an AI assistant to evaluate the top three solutions in our space, does it name us, does it frame our strengths accurately, and what evidence are we using to verify that today?"If the room goes quiet, you have an urgent strategic blind spot. That is the exact gap EvidentlyAEO exists to solve.
Final Thoughts on the Founder
Vikas Malik's journey brings to light an intriguing shift within SaaS and AI firms.Customer support has long been considered as a last-mile player in the value chain.Marketing generated demand.Sales acquired customers.Product built features.Engineering shipped them.And Support took care of all the problems that arose later.That particular model is going the way of the dinosaur.Modern support systems sit on some of the most valuable information within a tech company; it's what problems customers have, where products fall, what frustrates them, what makes them likely to leave, what they'll pay for to avoid leaving, and more.With the power of AI, that information becomes even more potent.Combining case history, telemetry, diagnostics, knowledge base, customer signals and incident patterns will enable support to shift from ticket resolution to predicting customer operations.Vikas's approach to automation of support is therefore more than just that.
It's a matter of changing the economics and strategic function of the role.Reduce MTTR.Prevent repeat incidents.Automate routine work.Improve margins.Incorporate customer data into Product.Create premium services.Protect retention.Strengthen trust.Where applicable, make some money.He has had a unique experience with AEO and GEO.AI is starting to impact the entire customer journey – from finding a business to getting assistance after customers buy from them.Businesses that do know both can definitely benefit.In the age of AI, the customer is the key to victory, and the customer retains the key to victory: the same thing – the asset.Trust. Talk to our experts
