How Large Engineering Companies Can Get Recommended by GenAI

Right now, your potential clients are opening tools like ChatGPT, Gemini, and Perplexity and asking questions like:

“Which consultancies are best for sustainable transport innovation in Europe?”

“Who has the strongest track record in offshore wind feasibility studies?”

“Compare [your company] with [your competitor] for a smart energy infrastructure project.”

AI responds with comparisons and recommendations. If your company isn't showing up, isn't described accurately, or is missing key information, you’re missing out on a channel that’s fast becoming the go-to for informing decisions, whether it’s a government procurement team narrowing down potential bidders, or a private sector client shortlisting partners.

So, what can engineering comms and marketing teams do to give their company the best shot in these searches? We share some practical insights, based on some heavy reading about how the models work, and lots of questioning the models themselves.

1. Define your objectives

Engineering companies have a lot to say, so start by setting clear objectives. This will help focus your GEO strategy, and allow you to measure impact and learn lessons before expanding to other areas.

What to do:

• You can't rank top for everything, so identify the priority areas you want to be best known for, or where you have a competitive edge.
• Define your positioning goals – how do you want to be seen: Strategist? Subject expert? Hands-on problem solver?
• List questions potential customers might ask in each focus area (such as those in the intro).
• Plan an ongoing programme of content and outreach aligned to providing clear answers to these questions.

2. Plan a programme of content based on what GEO likes, including...

i. Content that answers client questions directly

When a client types “What experience does [Your Company] have in high-speed rail?”, you want ChatGPT to answer with specifics from your own materials. Generative AI models often latch onto FAQ or Q&A content that mirrors how clients phrase questions.

What to do:

• Publish FAQ pages and knowledge articles answering common sector questions such as “What sectors do we serve in environmental engineering?” or “How does our approach reduce commissioning time?”

ii. Content that positions your firm as the authority in your field

When AI responds to, for example, “Who are the leaders in offshore wind feasibility?” or “Recommend experts in digital twin implementation,” it tends to recommend firms with a visible track record as thought leaders.

What to do:

• Publish white papers, technical explainers, and original research – both for peers and non-specialists.
• Create authored insights from discipline leads, chief engineers, and sector directors.

iii. Content that showcases real-world delivery and partnerships

AI uses evidence of real-world delivery to assess credibility. Publicly available case studies, client collaborations, and project announcements are proof points.

What to do:

• Publish case studies with problem–solution–impact narratives, including measurable results.
• Highlight partnerships in high-profile programmes, such as government innovation pilots or international consortia.

3. When creating each piece of content, write for LLMs

i. Publish clear, technical content

LLMs prioritise content that’s specific to the search query and written for people – with a clear narrative, accessible language, and expert-level terminology used in context.

What to do:

• Create detailed service pages for each key offer, including your unique methods, tools, and measurable results.
• Structure narratives clearly: problem → approach → impact → call to action.
• Use sector-specific language that clients (and AI) look for, such as “hydrodynamic modelling,” “BIM Level 3,” and “predictive asset management.”
• But avoid jargon and platitudes.

ii. Use semantic structure and metadata in all content to signal relevance and make content crawlable

Whilst LLMs like human language, they read web content differently to humans. They depend on HTML structure, semantic tags, and metadata to identify what a page covers.

What to do:

• Write titles and meta descriptions that describe exactly what the page covers:
  “Delivering advanced flood defence engineering using AI modelling for urban and coastal environments.”
• Use descriptive headings like “AI-Driven Rail Network Capacity Modelling” instead of vague labels like “The Future of Intelligent Rail.”
• Apply Schema.org markup for Organization, Service, CaseStudy, and FAQ.
• Make everything public and in HTML – no paywalls or pdfs.

4. Boost recognition by placing content in places GenAI considers authoritative 

i. Earn coverage in high-credibility media

GenAI models are trained on patterns from high-quality, balanced, information-rich content. As a result, they prioritise media outlets with a reputation for quality, and a proven record of authoritative coverage in the subject area.

What to do:

• Pitch project wins, innovation milestones, and research breakthroughs – as well as thought leadership – to tier-one and specialist engineering publications.
• Focus on quality over quantity: one mention in Financial Times or The Engineer is worth more than dozens of low-credibility blogs.

ii. Gain citations from academic, government, and standards sources

Citations in .gov, .edu, and standards body documents carry outsized weight in LLM outputs.

What to do:

• Partner with academic institutions on research and ensure your work is cited in resulting papers.
• Contribute your ideas and case studies to government consultation papers, technical standards, or strategic frameworks where your firm’s name is credited.

iii. Speak at key industry events

Event programmes, speaker bios, and abstract pages are crawlable and often referenced by AI when citing subject matter experts.

What to do:

• Secure speaking slots based on your thought leadership at flagship conferences like World Infrastructure Summit or Smart Cities Expo.
• Ensure your name, role, and topic are clearly visible in publicly accessible event listings.

5. Measuring GEO success

This is probably the hardest part. GenAI has natural variability in its answers, even to the exact same question, so measurements are imprecise. That said, you can track progress over time.

What to do:

• Before you start, ask your list of questions (see define your objectives above) to each of the models. If time is short, focus on ChatGPT which has by far the most users at the time of writing. 
• Run each question 5–10 times. Record how often you are mentioned, what the sentiment is, and copy specific answers into a spreadsheet.
• Repeat this test at regular intervals, aligned to your content publishing schedule, and monitor changes in mentions and tone over time. 
• There are tools that can automate this at scale, but it’s easy enough to do manually - particularly when taking your first steps. That also has the advantage of allowing you to personally assess whether a wording change feels like an improvement for your audience – something dashboards can’t easily measure.
• Use findings to refine your strategy. Ask the model why certain content is not surfaced and make changes accordingly.
• Be patient and stay the course - these things take time to filter through.

In summary...

1. Identify your core themes
2. Create clear web pages and FAQs for your website on those themes
3. Create a programme of regularly published case studies, customer/partner announcements, and thought leadership tied closely to those themes.
4. Create a whitepaper offering deep insight and problem-solving for each theme
5. Structure all content clearly, with descriptive headings and metadata. Use industry language. Publish in HTML, not PDF.
6. Publish all content on your site, and pitch them to business media and respected trade press wherever possible.
7. Speak at relevant events and partner with relevant academic or government organisation – ensuring your contribution is mentioned in their materials.
8. Test changes to GenAI responses to a set of customer-style questions monthly.

In procurement and partnership decisions, generative AI is fast becoming the new first-impression channel. The firms that take control of their GenAI footprint today will be the ones these tools recommend tomorrow.