As Work Moves Into Vertical AI Agents, Visibility Has to Follow It There

A lawyer at an AmLaw 100 firm no longer opens ChatGPT to ask a general question about a contract clause. Increasingly, they open a purpose-built legal agent that was trained on their firm's own precedent, drafts in their firm's own style, and never touches a general-purpose chat window at all. That shift, from general assistants to closed, vertical-specific agents, is already underway in law, and it is coming for every other professional category next.

Harvey shows how fast this moves

Legal AI platform Harvey is the clearest example of how far vertical agents have already gone. The company raised $200 million at an $11 billion valuation on March 25, 2026, co-led by GIC and Sequoia.¹ On May 5, 2026, it launched more than 500 purpose-built legal agents spanning different practice areas, alongside an Agent Builder tool that lets law firms construct their own.² Harvey's customers have since built more than 25,000 custom agents on top of that foundation, and the platform now serves a majority of the AmLaw 100, more than 500 in-house legal teams, and 50 asset management firms across 60 countries.²

Harvey's CEO, Winston Weinberg, put the shift plainly: "AI isn't just assisting lawyers. It's becoming the system through which legal work gets done."² That is not a chatbot bolted onto existing workflow. It is the workflow.

Why this changes where visibility has to happen

A general-purpose assistant answers from the open web and whatever the model was trained on. A vertical agent like Harvey's is scoped to a firm's own documents, precedent, and practice areas, which means the brands and vendors it recommends, cites, or integrates with are determined by a much narrower and more deliberate set of signals than a ChatGPT answer to "what's the best contract review tool."

That narrowing cuts both ways. It is harder to be recommended by accident inside a vertical agent, because there is no broad web crawl to get lucky in. But it also means that once you are built into how a vertical agent describes a category, a competitor, or a workflow, that positioning is far stickier than a general chat answer that resets with every new conversation.

The surfaces are multiplying, not consolidating

Legal is one category. Finance, healthcare operations, procurement, and engineering are all building or adopting their own closed agents on the same trajectory Harvey has already run. Each one is a new surface where a brand can be described accurately, described wrong, or left out entirely, and none of them behave exactly like the general-purpose platforms most GEO thinking was built around.

At Egaki, our platform already covers the full range of general-purpose model families people actually use, including ChatGPT, Claude, Gemini, Perplexity, Grok, DeepSeek, Qwen, Doubao, Kimi, and GLM, because brand visibility questions do not stop at one vendor's model. The same discipline, tracking what a model says about a brand, in which context, for which persona, is the one that extends naturally as more of the day's work moves into agents scoped to a single profession or workflow rather than a general chat window.

What to watch for in your own category

A vertical agent does not announce itself the way a new consumer AI product does. It shows up first inside the tools a profession already uses, often as a feature rather than a separate brand. The practical signal worth watching is whether your buyers' day-to-day tools (the case management system, the EHR, the ERP) are starting to embed agents that make recommendations or complete workflows on their own, rather than simply retrieving information for a human to act on.

Once that shift happens in a category, the question stops being "does AI mention us" in the ChatGPT sense, and becomes "does the agent our buyers actually work inside of recommend, cite, or route to us." Those are related questions, but they are not the same question, and a visibility strategy built only for the first one will miss the second entirely.

Build for where the work is going, not just where it is now

Harvey went from a $200 million raise to 25,000 customer-built agents in a matter of months, which is a useful reminder that vertical agents do not creep into a profession slowly. They arrive, get adopted inside the existing workflow, and become the default interface faster than most brand or marketing teams are tracking.

The categories where this has not happened yet are not categories where it will not happen. That forward-looking bet is exactly why we built Egaki's coverage to span the full range of model families, including the ones that will anchor tomorrow's vertical agents, rather than just the one or two platforms that dominate headlines today. Egaki's product-facing tools and our broader approach to AI visibility are built around a simple premise: wherever a model, general-purpose or vertical, is making a recommendation that affects your brand, that moment deserves measurement, and we intend to be the ones measuring it first. As more professional work moves into agents scoped to a single industry, our coverage moves with it.