AI Search Optimization Services for B2B Companies
That Want to Win The New World of Buyer Discovery

It drives revenue, and it shows up in the same reports your board already reads.

One Optimist client, a B2B technology company, grew LLM referral revenue 49x over 14 months, alongside 26x growth in LLM referral traffic.

We helped a fintech client grow LLM conversions 8x in eight months. Those are revenue and conversion numbers, not just visibility scores.

AI search sends fewer clicks than Google does, and it will for a while. What it sends is different. A buyer arrives after a model has already framed the category, compared the options, and named you as one of them, so they land further along than a cold organic visitor and they convert accordingly.

Track AI-referred leads and revenue from the first month and the hype question answers itself in a quarter.

Prioritize where your buyers already are, which for most B2B companies means ChatGPT first and Google AI Overviews second.

This matters less than it sounds, though.

Many of the levers that earn a brand mention or a citation are the same everywhere. Clear entity signals, coverage of the questions your buyers actually ask, answer-first structure, and credible third-party coverage all play a role in nearly every model’s reasoning.

Work done for ChatGPT shows up in Perplexity and Gemini too. What differs between engines is retrieval behavior and which sources each one leans on, and that changes sequencing rather than strategy.

We benchmark all five so the priority comes from your data instead of a guess. Some categories are Perplexity-heavy. Some are almost entirely AI Overviews. You’ll know which one you are inside the first month.

Don’t move budget out of SEO. Add AI search optimization on top of it.

Google still drives the majority of organic pipeline for most B2B companies. More to the point, these aren’t two budgets doing two jobs.

AI assistants build answers by searching, and what they retrieve is largely what ranks. Optimist’s AI Fan-Out Study tested 199 buyer-style prompts across five AI surfaces against 1,708 Google SERPs and found that pages ranking for a model’s own follow-up queries explain its citations at roughly five times the rate of a randomized control.

Rankings and AI citations ride the same rails.

Cutting SEO to fund AI search optimization removes the foundation it runs on. The reallocation that does make sense happens inside the organic budget. It’s less volume for volume’s sake, more coverage of the questions buyers ask, better structure on pages you already have, and cleaner brand and entity signals everywhere models read about you.

As for replacement, Google is running AI Overviews on top of its own index. The likelier outcome is that the interface changes while the retrieval layer stays recognizable. Plan for a shift in how buyers read answers, not for the disappearance of search.

Usually less than you’d expect, and almost never more articles on topics you already cover.

AI models synthesize the median answer in a category. Another competent post about a topic that already has twenty competent posts gives a model nothing it needs, and volume for its own sake is the pattern search engines built their scaled-content policies to catch.

Three things move AI visibility more than volume.

1. Coverage of the questions your buyers ask that nobody in your category has answered well.
2. Structure that lets a model lift a clean answer off the page — direct answers near the top, real headings, schema.
3. Consistent entity signals, so every place a model reads about you describes you the same way.

Most of the AI search optimization work we do in the first quarter happens on pages that already exist.

Yes. If you have a strong content operator and someone who owns measurement, you should probably run most of it yourself.

The hygiene layer is largely public knowledge now (although there’s still much debate on specifics).

Schema, entity cleanup, FAQ and answer-first structure, a visibility tracker for a few hundred dollars a month. A capable in-house team can stand all of that up without help.

Most teams look for outside help to provide guidance and expertise rather than pure execution capacity. The dashboard reports your score, not which gaps are worth money or what to fix first.

Teams end up tracking prompts nobody buys from. There’s no line from a brand mention to a closed deal, so the program dies at the first budget review. And most self-serve tools still don’t cover every engine, so the picture you’re steering by is partial.

The judgment layer is critical. If you want the plan without the delivery, Optimist offers a one-time strategy and roadmap engagement that gets you the sequenced action and your team executes it. If you want ongoing strategy, briefs, and specs while your team ships, Optimist offers an advisory retainer. 

Working with Optimist starts with a call, then an analysis of where you stand, then a roadmap. Retainer work runs from there, month-to-month.

The analysis benchmarks your brand across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews, maps the topics and questions your buyers are actually asking, and compares your visibility against the competitors showing up in your place.

It covers the SEO foundation underneath as well, since that’s where most AI citations get retrieved from.

That becomes your roadmap, built on Optimist’s Complete Organic Revenue Engine (CORE) Framework. The CORE Roadmap is sequenced by impact with a measurement baseline.

For CORE Advising engagements, we manage the roadmap, strategy, and performance reporting along with briefs, specs, and implementation plans to help your team ship fast and ship the projects that matter for your business.