2 Steps Ahead Start a conversation

A GEO agency for B2B software and tech.

We make sure buyers who ask ChatGPT, Claude, Gemini or Perplexity for a vendor in your category hear your name, with every result checked against a baseline we froze before we started.

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AI Search as a Service
Answers we work in
ChatGPT Claude Gemini Perplexity Copilot Grok Meta AI AI Overviews

Every model builds its shortlist from different sources, so a vendor can be named in one and missing in another. We track all eight and show you where you are named, and where a competitor is named instead.

The questions
Perplexity
Best employee app for frontline staff without a desk?

Apps built for frontline and shift workers, based on reviews and vendor documentation:

  1. Best overallYour brand Teamlink Frontline App From €3 per employee/month News, chat and shift swaps for people without a desk, with a mobile-first design. Best for: frontline and shift workers.
  2. Best for office teams Staffy Intranet From €4 per employee/month A classic intranet with a desktop-first design. Best for: office-based teams.
  3. Best budget Shiftr Comms From €2 per employee/month Announcements and basic chat. Best for: very small teams.

Check that the app works for people without a company email address.

Fictional vendors. Actual answers vary with model, market and day.

Buyers describe a problem, not a vendor

Ask an LLM which software to pick and it does not return ten links, it returns a short list. Which vendors land on it depends on how readable and well vouched for they are, and that is what we work on.

Try both answers

Flip the demo between the answer buyers get today and the answer once your product pages, comparison content and mentions are in order.

What LLMs draw on when buyers ask

Three layers, and we work on all three

For software questions an LLM draws on three sources. A vendor that is only strong in one of them gets named less than one that is consistent in all three.

  1. 1

    We make your site and product data easy to quote

    LLMs recommend software they can read and verify. Clear product pages, pricing and integration documentation, with structured data behind them, decide whether an assistant can quote you at all.

    What we do. We check what your pages say against the prompts that matter, and fix structure, entity clarity and crawlability so your answer can be read and attributed.

  2. 2

    We write the pages buyers compare you on

    Buyers ask for comparisons, alternatives and use cases. Pages that answer those questions directly, with plain claims and numbers, are the ones an LLM quotes.

    What we do. We decide which comparison, alternative and use-case pages have to exist, write them with you and check them against what the assistants answer today.

  3. 3

    We build what others say about you

    LLMs weigh what others say about software: review sites such as G2 and Capterra, communities such as Reddit and LinkedIn, and best-of lists. A vendor that is strong on its own site only gets named less than one that others vouch for.

    • We write it. Review requests, answers, posts, comparison pitches and video briefs, from your real facts, customers and tone of voice.
    • You approve it. We work it out together, and nothing goes live without your sign-off.
    • We place it. On the sites LLMs check before they recommend software, such as G2, Reddit, LinkedIn and best-of lists, and we report which ones get cited.
Case
2% → 29% of qualified inbound per month via AI search, nine months apart
1× → 5× meetings booked per month via AI search, nine months apart
“Before we started optimising for AI search, it barely brought in any leads. Nine months later it brings in close to a third of our qualified leads each month, and five times as many meetings per month.”
Sander Roose Founder & CEO, Omnia Retail

Nine months of this work, as it looked at Omnia Retail

Omnia Retail asks every inbound lead how they heard about the company. At the start 2% of qualified leads named an LLM or AI search. Nine months later it was 29%.

Meetings booked through AI search grew fivefold over the same months, so the channel became bigger in absolute terms, not only as a share.

Why it pays

Buyers pick their vendors in AI chatbots

These are the numbers on how software buyers use assistants before they choose. Read them with the sample next to each: they come from other people, and they are not about your site.

51%

of B2B software buyers begin their research in an AI chatbot more often than in Google.

G2 survey among 1,076 B2B software buyers (April 2026)
92%

of B2B professionals who use AI at work say it shaped which vendors made their shortlist.

Semrush survey among 643 US B2B professionals (March to April 2026)
83%

of them say AI influenced the vendor they finally chose, and 97% say it helped them discover new vendors.

Semrush survey among 643 US B2B professionals (March to April 2026)
69%

of software buyers bought from a vendor they had not planned on, after an AI chatbot recommended it.

G2 survey among 1,076 B2B software buyers (April 2026)

Each figure names its sample, and none of them is about your site. Surveys show what buyers say they do, which is why we measure every client against their own baseline and ask new leads how they found you. See every source in the full breakdown.

What we do

Six things, done properly

Everything we do for software companies comes back to one question: when a buyer asks an LLM which vendor to pick, is it your name that comes up? These are the six parts of the work.

  1. 01

    Prompt research

    We list the questions your buyers ask, by market and language, and pick the ones worth winning.

  2. 02

    Citation-ready content

    We decide which pages have to exist, and in what shape, so an assistant can quote you as the answer.

  3. 03

    Technical AEO

    Structured data, entity clarity and crawlability: the groundwork that makes the rest reachable.

  4. 04

    Tracking and reporting

    A dashboard against your frozen baseline, plus manual checks where a number needs a real, logged-in test.

  5. 05

    Offsite authority

    We find which publishers, communities and review sites move your prompts, and focus the effort there.

  6. 06

    AI search advertising

    Optional

    Paid placement inside the answer, bought and measured against the same baseline.

Questions we get

Questions software companies ask us

What does a GEO agency for B2B software do?

We find the prompts your buyers ask LLMs, measure which vendors get named, and work on the product pages, comparison content and third-party mentions that decide it. Every result is checked against a baseline frozen before we started.

How do you measure results?

Per market we freeze a baseline of which prompts name you, at what position and which sources the answers draw on. Every later figure is compared with it, including the months when nothing moves, and the report says so. Next to visits and sessions we add a required "How did you hear about us?" question on your contact form, which shows how many leads came through an LLM and what they asked it.

Do you guarantee a position in ChatGPT?

No. LLM answers differ by model, market and day, so we do not promise a position. We promise a baseline, a clear monthly plan and honest reporting.

Who writes the content?

We create it together with you: from your real facts, customers and tone of voice, and approved by you before anything goes live.

What does it cost, and how long is the commitment?

Three packages (Core, Scale and Max), each one flat monthly fee, with no setup fee and a three-month minimum. Pricing is on request, and a longer contract lowers the monthly fee.

Who it suits

Where we are useful, and where we are not

A good fit

You sell B2B software or tech. Buyers ask LLMs for vendors in your category, and your competitors are named where you are not.

Not a good fit

You have not found product-market fit yet, or you sell to a purely local market.

See where your company stands in LLM answers

Send us your domain and the category you compete in. We come back with the prompts buyers use, which vendors are named today, and what is worth fixing first.

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