Get cited by ChatGPT. Then automate the work behind it.
94% of B2B buyers use language models while they research vendors, and the citation game runs on different rules than Google. I measure where you stand against named competitors and fix it. Then I rebuild the execution layer underneath so the function runs on automated workflows instead of headcount.
Why this exists
The shortlist now forms inside LLMs. Buyers use ChatGPT, Claude, Perplexity and Gemini to compare vendors mid-funnel, and the visitors those tools send convert at 14.2% against 2.8% for Google organic, because they arrive pre-informed and recommendation-primed.
Ranking well in Google no longer covers you: the overlap between top Google results and AI-cited sources has fallen from roughly 70% to under 20%, and only 38% of AI citations come from top-10 organic results. A company can own page one and be invisible where the decision actually forms.
It gets worse than invisible. Seer Interactive measured a 61% drop in organic click-through when an AI Overview triggers on the same query, across 3,119 queries and 42 companies. If you are not the cited source, you lose the click to whoever is. And Gartner now expects 90% of B2B buying to be intermediated by AI agents by 2028, which means your first reader is increasingly a model, not a person.
Almost nobody serving robotics, aerospace or industrial companies is competent at this yet. That is the window.
The numbers behind the service
Sources: Mersel AI GEO 2026, LLMrefs 2026, 6sense Buyer Experience Report, Seer Interactive 2025, Gartner 2026
The half nobody sells you
Getting cited is the front door. The expensive part is everything after the click, and that is where AI has quietly stopped being a content toy and started being an operating layer.
Nucleus Research measures a 12.2% reduction in marketing overhead and a 14.5% increase in sales productivity from automated marketing workflows, at an average return of $5.44 per dollar over three years with payback typically inside six months. McKinsey values gen AI marketing productivity at 5% to 15% of total marketing spend, and puts the concentrated gains in specific workflows rather than in a vague AI strategy: content drafting at roughly 3.2x return, personalization 2.7x, audience research 2.4x.
Meanwhile 15.3% of the average 2026 marketing budget is already going to AI tooling, and only 30% of CMOs say they can actually scale it. Everyone bought the licenses. Almost nobody built the system. That gap is the entire offer.
What automation is worth
Sources: Nucleus Research, McKinsey (The Economic Potential of Generative AI), Gartner CMO Spend Survey 2026
What the visibility work is
Definition
The AI Visibility Audit runs a fixed library of roughly 25 prompts, the questions your buyers actually ask, across ChatGPT, Claude, Perplexity and Gemini, and measures how often you are cited or mentioned against named competitors. Then the gaps get fixed, and the library gets re-run monthly.
What gets fixed
- Entity consistency: the same 40-word description of who you are, everywhere AI systems look
- Schema markup and an llms.txt: the machine-readable layer
- Answer-structured content: pages built around extractable facts, named numbers, named companies, dated claims
- Comparison assets: LLMs disproportionately retrieve comparison content when asked to evaluate options
- Third-party presence: the directories, communities and publications that citation actually flows from
- Technical crawlability for AI agents: no content behind JavaScript, no blocked AI crawlers
What does not work: keyword stuffing. The Princeton, Georgia Tech and Allen Institute GEO research found it has negligible or negative effect on AI rankings. Fact density, citations and semantic structure are what move the number. Worth knowing: roughly a third of B2B companies still block AI crawlers in robots.txt, which removes them from the answer set entirely. That is a fifteen-minute fix that most competitors have not made.
What the execution layer is
Definition
The execution layer is the set of automated workflows that run your marketing between campaigns: monitoring, research, drafting, sequencing, routing and reporting. I design them, wire them into your CRM, put a human review gate on anything that publishes or sends, and document them so your team owns them when I leave.
What runs without a person
- Buying-signal monitoring: funding rounds, executive hires, job postings, patent filings, trade show exhibitor lists, all scored into a prioritized outbound queue
- Account research and contact enrichment, instead of an intern with a Crunchbase tab open
- Prompt library re-runs and competitor citation tracking across four models, monthly, automatically
- Technical content drafted from your own documentation: spec sheets, application notes, comparison pages, FAQ blocks, with human editorial control before anything publishes
- Lifecycle email and nurture sequencing tied to real product and pipeline stages
- Lead scoring, routing, deduplication and CRM hygiene, so nothing sits unworked for a week
- Trade show follow-up inside hours instead of the usual three weeks, which is where most hardware companies lose the pipeline they paid $40k of booth money to generate
- Board-ready reporting and multi-touch attribution without a full-time analyst
Nurtured pipelines generate roughly 50% more sales-ready leads at 33% lower cost per lead. The mechanism is not clever copy. It is that the follow-up actually happens, every time, on time.
The economics, said plainly
AI has collapsed the cost of marketing execution. It has not touched the cost of judgment. That is the whole shape of the 2026 market: Gartner shows marketing budgets flat at 7.8% of revenue with 15.3% now committed to AI, while labour as a share of budget rose from 21.9% to 24.5%, because the people who can direct these systems got more expensive and the people who only produced first drafts got automated.
For a Series A to B hardware company, that math is unusually kind. You do not need the eight-person department. You need one senior operator who can build and steer the system, and then the system. A well-built stack does the coordination, research and first-draft work that would otherwise absorb two or three junior hires and an agency retainer, and it does not take a week off in July.
That is also why the engagement has an end date. I build the visibility function and the execution layer, run them long enough to prove the numbers, document them, and hand them to you. The team designed to be deleted: the system stays, the retainer stops.
The part almost nobody else can offer
Aerospace, defense and dual-use companies frequently cannot put roadmaps, customer names or technical specs into a public LLM. For export-controlled or ITAR-sensitive work, I build the same systems on locally-hosted models with your IT: signal monitoring, enrichment, content workflows, retrieval over your own document library, so nothing leaves your infrastructure.
This is the reason the offer exists in this vertical at all. The generalist AI marketing agency cannot touch a company whose most valuable content is controlled. I grew up in power electronics and test and measurement, I can read your datasheet, and I can stand up a local model stack that keeps your material inside your own walls.
What I will not do
Gartner expects more than 40% of agentic AI projects to be cancelled by 2027, and the reasons are consistent: weak governance, no observability, unclear ROI attribution. McKinsey finds 56% of deployment teams blocked by poor data quality before model quality is ever the issue. Automation built on a dirty CRM and no review process produces expensive noise.
So: no publishing without a human gate. No volume content that your own engineers would be embarrassed by. No agent turned loose on a database nobody has cleaned. Every workflow gets an owner, a measurement, and a documented kill switch before it touches a customer.
Where it applies
Every vertical I work in. The buyers all research the same way now, and they all have the same manual follow-up problem.
AI visibility and execution questions
What is AI visibility?
How often AI assistants (ChatGPT, Claude, Perplexity, Gemini) mention or cite your company when buyers ask the questions that build a shortlist. It runs on different rules than Google ranking: fewer than 20% of AI-cited sources overlap with top Google results, so you can rank well and still be invisible where the decision forms.
How do you measure it?
A fixed library of roughly 25 prompts, the queries your buyers actually ask, in their language, run monthly across ChatGPT, Claude, Perplexity and Gemini. I log citation and mention rate for you and your named competitors, so progress is a number, not a feeling.
What actually gets fixed?
Entity consistency across the web, schema markup, an llms.txt, answer-structured content with real fact density, comparison assets, third-party presence on the platforms AI systems weight, and technical crawlability for AI agents. Keyword stuffing does nothing here. The research shows fact density and citations are what move it.
What does the execution layer actually automate?
Buying-signal monitoring into a scored outbound queue, account research and contact enrichment, technical content drafted from your own documentation, nurture sequencing, lead scoring and routing, CRM hygiene, trade show follow-up, and reporting. Roughly the work that a coordinator, a research analyst and a junior writer would otherwise do, running on a schedule instead of a salary.
Does this mean I can hire fewer marketers?
It means you hire differently. Nucleus Research measures a 12.2% reduction in marketing overhead and a 14.5% increase in sales productivity from automated workflows, and McKinsey values gen AI marketing productivity at 5% to 15% of total marketing spend. What automation does not replace is judgment. The realistic outcome for a Series A to B hardware company is one senior operator plus a system, instead of three or four junior hires plus an agency retainer.
How do I know the AI output will not be slop?
Because a human owns every publish decision and the source material is yours: your spec sheets, your application notes, your engineers. Gartner expects more than 40% of agentic AI projects to be cancelled by 2027, mostly for weak governance and unclear ROI, and 56% of deployment teams cite poor data quality as the blocker. Both failure modes are design problems, which is why the workflows get built against your data and reviewed before anything ships.
How fast does it move?
Visibility moves faster than classic SEO, because the niche queries that matter to hardware companies are barely contested. In uncrowded verticals, citation movement inside 8 to 12 weeks is realistic. Automation payback is usually quicker: Nucleus Research puts average return at $5.44 per dollar over three years, with payback typically inside six months. Measurement is monthly either way.
Can this run on export-controlled or ITAR-sensitive material?
Yes. For work that cannot go into a public model, I build the same stack on locally-hosted models with your IT, inside your own infrastructure, so nothing leaves the building.
Want to see what ChatGPT says about you?
Book the review and I will run three of your buyers' real prompts live on the call, then show you which parts of your follow-up should never have been a person's job. Nothing sells this like watching a model recommend your competitor.