26 questions B2B manufacturers ask us

We build an inquiry pipeline with two lines. The outbound line researches target companies and emails the person who signs off, one company at a time. The GEO line makes your products citable, so ChatGPT, Perplexity, Claude and Gemini name you when a buyer asks who to source from. Both lines feed one inquiry pool, and every inquiry reaches your sales dashboard graded A, B or C with its account research attached.

B2B manufacturers, brand owners and distributors selling across borders, in categories such as chemicals, advanced materials, machinery, electronic components and consumer goods. It works best for technical, spec-heavy products, where buyers compare parameters before they compare prices. See the nine industry playbooks.

Contacts come from 100+ paid data sources in six categories and are cross-verified before anyone is emailed; only about 14% make it onto the send list. Each email is written for one company and must cite at least two facts from the account research. No subject and opener combination repeats on the same domain within 60 days, follow-ups run Monday to Friday in the buyer's time zone, and messages go to the decision-maker, not an info@ inbox.

Sites, domains and sending accounts are deployed for your project as part of the setup, and delivery and bounces are monitored continuously to protect the sender domains. Contacts are verified before they reach the send list, which keeps bounce rates down.

We email only business contacts at work addresses, about products relevant to their role, under legitimate interest for B2B communication. Every email identifies the sender, includes a postal address and offers a one-step opt-out. Opt-outs are honored within 2 business days and added to a suppression list that covers every campaign we run. The approach is designed for the GDPR, UK PECR, CAN-SPAM and CASL. Read how outreach data is handled.

Outbound starts with 4–6 weeks of infrastructure build and account warm-up. No emails go out in that period, and the first inquiries follow once sending begins. For GEO, AI usually restates your specs more accurately within 2–6 weeks of the structured content going live, and mention rate typically moves in months 2–4.

Every inquiry is graded. A-grade is ready to quote, B-grade needs a quick confirmation, and C-grade is genuine interest. Each one arrives on your sales dashboard with the account research behind it. We report a verifiable count of graded inquiries, not a send-volume report.

No one can guarantee a position inside an AI answer, and we put that in writing. What we commit to in the contract: no fabricated data, no black-hat tactics, and fully traceable work, with screenshots and timestamps for every test. Read our quality standards.

Every plan is quoted individually, after the free audit, based on your markets, languages, product range and volume. Outbound plans (Starter, Standard, Flagship) are billed monthly with a one-time setup, and are 15% off when paid annually. GEO plans (Foundation, Growth, Leader) are billed annually, and the GEO plan is 30% off when you run both lines. Compare the plans.

Your main product category, target markets and the competitors or competitor models you are measured against, plus the technical documents behind your products, such as TDS and COA. Nothing appears in our content unless you have provided it in writing.

We ask ChatGPT, Perplexity, Claude and Gemini the questions your buyers ask, compare you with your main competitors and deliver a screenshot-backed report within 3 business days: who gets named and why, where you fall short, and the five fixes with the best return. No charge and no obligation. See a sample report.

GEO (Generative Engine Optimization) optimizes whether an AI names you when it generates an answer. SEO competes for a rank in a list of results; GEO competes for a citation inside the answer itself. The unit of competition is different too: SEO compares keywords and pages, while GEO compares facts, specifications and entity relationships. SEO earns trust through backlink authority. GEO earns it when several independent sources say the same thing about you.

The starting point of the buying journey is moving. Instead of typing keywords into Google, buyers ask an AI directly: "Which suppliers offer an alternative to X?" or "What should I choose if I need spec Y?" By the time a buyer first contacts anyone, the shortlist has already been filtered once. If you weren't on it, you never find out you were eliminated.

Because the training data holds more technical descriptions of them than of anyone else, and those descriptions agree with each other. Legacy incumbents have decades of papers, patents, technical manuals and trade-press coverage behind them, so models are confident about their specifications and applications. A challenger brand with better specs but thin source content is one the model doesn't dare to recommend. It isn't bias. It's insufficient evidence.

Yes. Niche categories give GEO its highest return on effort. The thinner the existing source content, the faster a small amount of high-quality, structured content builds authority, and you may well be the only supplier in the category doing it. In a large category you compete with dozens of brands. In a niche, you may only need to win one slot.

Within 2–6 weeks of the structured-content changes going live, you can usually see AI restate your product specifications more accurately. That is the earliest signal. Meaningful movement in mention rate typically shows up in months 2–4, depending on how competitive the category is and how much source content you already have.

Track four reproducible metrics: AI mention rate (the share of target-category questions where your brand is named), recommendation position (where you rank on the shortlist), cited sources (whether AI cites your own website or third-party content), and specification accuracy (whether AI restates your specs correctly). You can retest all four yourself in the models. You don't have to take anyone's word for it.

Being readable in English is not the same as being citable. The gaps are usually in three places: technical specifications are still locked in PDFs, there is no structured data markup, and there is no third-party cross-verification. An English website is necessary, but it isn't sufficient.

In most cases it can't, or it reads them unreliably. Tables in PDFs are often parsed into scrambled text, so values and units no longer line up. The fix is to publish the specifications that drive product selection as HTML with Schema markup, and keep the PDF as an attachment. Which specifications count as key depends on the category: for chemicals it's performance indicators, for components it's tolerances and materials.

A machine-readable annotation format (JSON-LD). Take a line such as "activity 5000 U/g" or "tolerance ±0.005 mm." Written as plain text, AI has to guess what it means. Marked up as an additionalProperty with a name / value / unitCode triple, it becomes a definite fact the model can compare and cite.

This is the most common misconception. Blocking AI crawlers doesn't protect your content. It simply guarantees that AI never thinks of you when a buyer asks. Your technical specifications exist so buyers can see them; the goal is to be cited, not hidden. What genuinely needs protecting is formulations and process know-how, and those should never be online in the first place.

A Markdown summary file in your website's root directory that tells large language models, in plain language, who the company is, what it does, what the key facts are and where the important pages live. It isn't a mandatory standard, but it costs almost nothing and gives models a high signal-to-noise source of facts when they crawl your site.

It means building a specification-level mapping between your grades and the model numbers of major international brands, so that when a buyer asks AI for "an alternative to model X," the model has grounds to list you as a candidate. The legal line is clear. You may make objective specification comparisons and state that a product suits the same applications. You may not use the other company's trademark as your own, or claim your product is "equivalent" or "authorized by the original manufacturer." We only state specification facts that can be verified.

Yes, and we encourage you to start with three things on your own: let AI crawlers in, move your core product specifications from PDF to HTML, and write an llms.txt file. These three steps cost nothing and fix the basic problem of AI not knowing you exist. Where outside support usually comes in is the competitor-model mapping library and ongoing third-party content seeding. That part is a long game.

The risk profile is different. Many SEO tactics exploit loopholes in ranking rules, so when the rules change the gains disappear. GEO's core asset is accurate, structured, cross-verifiable facts, which is exactly what models rely on more with each new version. If the direction is right, model updates work for you, not against you.

In the short term, yes. Over time, the mix changes. Paid channels buy traffic at the very end of the decision, and the cost rises every year. GEO claims the starting point of the decision: you build it once and keep reusing it. The practical approach is to run both. Use paid channels to keep current inquiries flowing, and use GEO to reduce your dependence on them step by step.