---
title: "How engineering firms win project inquiries from AI search"
description: "Engineering firms get named by AI when their project types, licenses, people and locations are stated clearly and confirmed by sources clients already trust."
canonical: "https://underneath.agency/resources/engineering-firms-project-inquiries-ai-search"
published: 2026-10-07
updated: 2026-10-08
publisher: "Underneath (https://underneath.agency/agent)"
entity: "https://underneath.agency/.well-known/entity.json"
---
Guide · AI search

# Will clients find our engineering firm when they ask AI who can design their project?

They can, if an assistant can confirm what you design, where you are licensed, who leads the work and which similar projects you have delivered. Referrals still bring most engineering work, but a referred client now checks a firm online before calling, and AI assistants are becoming part of that check.

This guide is for engineering services firms: civil, structural, mechanical, electrical and plumbing (MEP), geotechnical and process engineering. It covers private clients, architects and prime firms that hire consultants, and public owners that select engineers by qualifications. It is not about engineering software, which we cover separately.

## The short version

1. The market is large but capacity-bound: the [ACEC Research Institute](https://www.acec.org/news/last-word-blog/post/new-acec-institute-research-on-engineering-industrys-economic-impact/) puts US engineering and design services revenue at $459 billion in 2024, and more than half of ACEC member firms turned down projects because they lacked staff.
2. Firms are choosing work more carefully: in [Deltek’s 2025 Clarity study](https://www.deltek.com/en/about/media-center/press-releases/2025/what-the-46th-annual-deltek-clarity-ae-study-reveals-about-the-industry), proposal volume fell 38% while the value of awarded work grew 52%, and the median win rate rose to 50%.
3. Fit now beats relationships: the same study found “fit for the type of work” overtook existing relationships as the top factor in deciding which projects to pursue.
4. Firms already use AI; their clients are next: in [Deltek’s 2026 study](https://www.deltek.com/en/about/media-center/press-releases/2026/the-latest-deltek-clarity-industry-studies-highlight-ai-challenges) of 896 architecture and engineering firms, AI adoption rose from 53% to 70% in a year, and 42% believe they could lose market share within two years without significant digital transformation.
5. Public work is selected on qualifications: under [federal law](https://www.law.cornell.edu/uscode/text/40/1103), an agency must hold discussions with at least 3 firms and rank the most highly qualified before any fee is negotiated.

## Who hires engineering firms, and what is one client worth?

Public owners, private developers, architects, industrial owners and prime firms, and a good client returns project after project.

The industry is large. The ACEC Research Institute found engineering and design services revenue grew 5.3% in 2024, with growth expected to slow to 2.3% in 2025, and the industry directly employs 1.7 million Americans. Texas ($96 billion) and California ($94 billion) led all states in economic value added. At the top, the ENR Top 500 Design Firms grew revenue 7.4% to $158.7 billion in 2025, [according to ENR figures quoted by BL Companies](https://www.blcompanies.com/bl-companies-rises-up-rankings-on-2026-engineering-news-record-top-500-design-firms/), which ranked No. 231 with more than $111 million in revenue.

The buyers differ by market:

- **Public owners** such as federal agencies, state transportation departments, cities and water utilities. Federal selection follows the Brooks Act and [FAR Subpart 36.6](https://www.acquisition.gov/far/subpart-36.6): firms file the Standard Form 330 qualifications statement, a board ranks them, and the agency then negotiates a fair and reasonable price with the top-ranked firm.
- **Private owners and developers** who hire civil, structural and MEP engineers for buildings, sites and industrial facilities, often after a referral.
- **Architects and prime firms** that bring in consultants for a pursuit, such as a structural engineer for a mass timber building or a specialty subconsultant to complete a team.
- **Industrial owners** who need process, electrical or controls engineering for a plant expansion.

No public source gives an average fee per client, so we do not quote one. What the data shows is that each relationship compounds. Firms are winning half of what they chase, and in a capacity-constrained market the value of a client is the stream of follow-on projects, on-call contracts and referrals it brings.

## Where do clients meet AI before they call an engineer?

Mostly at the checking step, after a referral or before a shortlist. Someone needs to know who does this work nearby.

We found no current survey of how owners and developers use AI to choose engineering firms. The most recent public study we found on how end users pick engineers is old: a 2012 survey by Accountability Information Management, [reported by Canadian Consulting Engineer](https://canadianconsultingengineer.com/clients-mostly-use-peer-recommendations-to-choose-engineers), found that 64% of end users relied on recommendations from peers, 33% on contractors’ recommendations and 14% on web searches. We cite it as a baseline, not as today’s picture.

Today’s cross-industry evidence points in one direction. In [Gartner’s survey of 645 B2B buyers](https://www.gartner.com/en/newsroom/press-releases/2026-05-20-gartner-survey-finds-sixty-nine-percent-of-b-two-b-buyers-turn-to-sales-reps-to-validate-ai-generated-insights), 45% had used generative AI in a recent purchase, mainly to gather information on vendors, and 69% prefer to validate AI-generated insights with a salesperson. For an engineering firm, that “salesperson” is usually a principal on the phone.

The firms themselves are early AI users. Deltek’s 2026 study found generative AI use among architecture and engineering firms rose from 64% to 78% in a year, and its 2025 study listed proposal development and business development among the main uses. Our inference: the people who hire engineers work in the same offices and use the same tools, so AI-assisted checking of consultants is a reasonable expectation, but it has not been measured. The vendors selling design tools to these offices face the same shift, covered in [how engineering software gets found](https://underneath.agency/resources/engineering-software-ai-search).

## Which questions do clients and partners ask AI about engineering firms?

Questions that combine a discipline, a project type, a place and a constraint, much like a request for qualifications.

These examples are our own, written to show the shape of real requests. They are not captured from any assistant or client.

| Who is asking | Example question |
|---|---|
| Developer | “Structural engineers in Denver with mass timber mid-rise experience” |
| Architect | “MEP engineering firms that design laboratories and cleanrooms in the Boston area” |
| Industrial owner | “Process engineering firms for a battery materials plant in the Southeast” |
| Prime firm | “Certified small business geotechnical firms licensed in Texas for a highway project” |
| City or utility staff | “Engineering firms that have designed water treatment plant upgrades for cities under 100,000 people” |
| Referred client | “What projects has [firm name] done, and who are its principals?” |

Each one is a filter. If your project pages never say “mass timber”, your licenses by state are not listed, or your lab work sits inside a PDF brochure, an assistant has nothing to match.

## How does an AI mention turn into a project?

Through an inquiry or invitation, a qualifications check, an interview and a negotiated fee, then repeat work.

1. **Named or checked.** A client asks for firms, or checks a firm someone recommended. The answer shapes who gets the call.
2. **Invited.** The client sends a request for proposal or qualifications, or a prime firm calls about teaming. For public work, the formal notice still goes out; AI influences who notices it and who is invited to partner.
3. **Qualified.** The client reviews similar projects, licensed staff and references. Under federal rules, the agency must discuss the work with at least three of the most highly qualified firms and rank them in order of preference.
4. **Interviewed and negotiated.** Price comes after selection in qualifications-based selection, and it is negotiated, not bid.
5. **Repeated.** A successful project leads to the next one, an on-call contract or a referral.

Assistants shape who is named and who is invited, and little after that. Your people and past work win the rest. In Deltek’s data, firms submitted far fewer proposals but won more value, which suggests the right invitations matter more than the number of them.

## What makes an assistant name one engineering firm over another?

Verifiable facts about projects, people, licenses and place; the platforms explain their searches, not their choices.

**Documented by the platforms.** [OpenAI says](https://help.openai.com/en/articles/9237897-chatgpt-search) ChatGPT search rewrites a question into one or more targeted queries sent to search providers, and that sites must allow its crawler, OAI-SearchBot, to be eligible for inclusion. Google’s [AI Mode announcement](https://blog.google/products/search/ai-mode-search/) describes a “query fan-out” technique that sends several related searches across the subtopics of one question. Neither publishes how firms are chosen.

**Observed in our studies.** Engineering is often a local or regional purchase, so our local studies are the closest evidence, with the caveat that they covered other local services:

- In [our study of ChatGPT local recommendations](https://underneath.agency/research/chatgpt-local-recommendations-study), two answers to the same question on the same day shared a business-set overlap of 0.65, on a scale where 1 means identical. The firms named shift from run to run.
- In [our study of which Google Maps businesses ChatGPT recommends](https://underneath.agency/research/chatgpt-local-picks-google-profile-study), businesses with more reviews than the local median were 19.5 points more likely to be listed, after adjusting for other signals.
- In [our business-facts study](https://underneath.agency/research/ai-business-facts-accuracy-study), 18.9% of answers stated at least one fact that differed from the business’s Google profile, mostly where the business’s own sources disagreed.

**Our inference for engineering firms.** The trust factors are the ones a selection board already scores, put where machines can read them: project type, size, location and your role; licensed professionals and the states they are licensed in; [professional licensure](https://ncees.org/licensure/) itself, which typically requires an accredited degree, four years of experience and two exams; certifications such as small or disadvantaged business status; awards and trade coverage; and consistent firm facts across your site, directories and profiles.

## What does it cost an engineering firm to be left out?

Invitations and teaming calls that go to another firm, which no pipeline report will show.

We have no measurement of projects lost to AI absence, so this is our reasoning, labeled as such:

- **Growth is slowing.** ACEC expects revenue growth to ease from 5.3% to 2.3%, and Deltek’s 2026 study found firms forecasting 9.5% net revenue growth for 2026 while backlogs soften. When work tightens, being on the first list matters more.
- **Staff is the constraint.** With staff growth of only 1.2% in Deltek’s data and turnover above 13%, firms cannot answer every request for proposals. Being invited to well-fitted work is worth more than chasing volume.
- **Wrong facts disqualify.** An answer that lists an old office, misses a state license or describes you as an architecture firm can remove you from a search before anyone reads your qualifications. Our guide on [fixing wrong brand information in AI answers](https://underneath.agency/resources/fix-wrong-brand-information-in-ai-answers) explains how to trace the source.

## How does GEO work for an engineering services firm?

Generative engine optimization (GEO) makes your firm easy for AI assistants to find, describe accurately and confirm.

For an engineering firm, the work usually covers:

1. **Project pages in plain text.** One page per notable project with type, size, location, delivery method, your role and the client (with permission), written as text rather than images or brochures.
2. **Discipline and market pages.** Clear pages for what you design (structural, MEP, civil, process) and who you design it for (healthcare, water, transportation, industrial).
3. **People and licenses.** Principal and project manager pages listing licenses by state and relevant project experience.
4. **Consistent firm facts.** The same name, offices, disciplines and certifications on your site, Google Business Profile, LinkedIn, association directories and government registrations.
5. **Independent coverage.** Award entries, trade publication features, conference papers and association rankings give an assistant outside sources to cite. Our article on [how small brands get recommended by AI](https://underneath.agency/resources/how-small-brands-get-recommended-by-ai) explains why outside sources matter.
6. **Reviews where clients look.** For firms that serve local private clients, profile reviews are one of the few signals we have seen move local recommendations. Other local professional services work the same way; recruiting agencies, for example, are named from local listings, reviews and specialty pages, as [our guide for recruiting agencies](https://underneath.agency/resources/recruiting-agencies-employer-clients-ai-search) explains.
7. **Crawl access and measurement.** Allow the documented search crawlers, then ask a fixed set of project questions across ChatGPT, Gemini, Perplexity, Copilot and Google’s AI features over time, and compare who is named with where invitations actually come from.

No engineering firm can be promised a place in an AI answer. What this work does is make your firm the easiest one for an assistant, and then a client or selection board, to check. Firms in other project-based fields face the same pattern, as our guides on [how IT services firms win projects from AI search](https://underneath.agency/resources/it-service-firms-leads-ai-search) and [how contract manufacturers get on supplier shortlists](https://underneath.agency/resources/contract-manufacturers-rfqs-from-ai-search) show.

## What is still unknown about AI and engineering firm selection?

How often clients use AI to find engineers, and how many projects it produces, has not been measured.

- **No client-side survey.** The AI adoption figures here describe engineering firms, not their clients. The only engineer-selection survey we found is from 2012.
- **Public procurement limits the effect.** Qualifications-based selection runs on formal submissions and board scoring. We infer AI matters more for private work, teaming and the research before a submission than for the scoring itself.
- **Sponsors have interests.** Deltek sells software to project-based firms, and ACEC advocates for the industry.
- **No engineering-specific ranking studies.** Our studies covered other local services and buyer questions. Applying them to engineering firms is our inference.

## Where should an engineering firm start?

Ask assistants the project questions your best clients and teaming partners would ask, and see whether your firm is named.

That first check usually shows whether you appear for your core disciplines and markets, whether your offices, licenses and project types are described correctly, which directories and publications the answers rely on, and which firms are named instead.

If your growth depends on more well-fitted invitations to propose, [have us check how AI describes your firm](https://underneath.agency/contact). We ask the discipline, market and location questions clients and teaming partners use, show which firms and directories are named in your place, and set out the project and license facts most likely to bring more qualified project inquiries. Our [generative engine optimization service](https://underneath.agency/services/generative-engine-optimization) page describes how that work is handled for a design firm, from plain-text project pages and licensed-staff profiles to consistent directory listings.

## Frequently asked questions

### Does AI search matter if most of our work comes from referrals?

Yes. Referred clients still check a firm before calling, and assistants are becoming part of that check. If the answer misdescribes your firm, the referral can stall.

### Can AI search help us win public contracts selected on qualifications?

Indirectly. Selection boards score formal submissions, but prime firms looking for partners and agency staff researching the field can use AI before a solicitation.

### Should individual engineers have their own pages?

For licensed leaders, yes. Pages that list licenses by state and relevant projects help clients and assistants confirm who will do the work.

### Do rankings like ENR’s Top 500 help?

They are independent sources that assistants and clients can find. A listing, an award or a feature gives outside confirmation that your own site cannot.

## Sources

- ACEC (2025-10-08), [New ACEC Research Shows Engineering and Design Services Industry Contributed $685 Billion to U.S. GDP in 2024](https://www.acec.org/news/last-word-blog/post/new-acec-institute-research-on-engineering-industrys-economic-impact/)
- Deltek (2025-05-13), [AI, Talent and Record Profits: What the 46th Annual Deltek Clarity A&E Study Reveals](https://www.deltek.com/en/about/media-center/press-releases/2025/what-the-46th-annual-deltek-clarity-ae-study-reveals-about-the-industry)
- Deltek (2026-05-12), [The Latest Deltek Clarity Industry Studies Highlight AI Challenges, Talent Strain, and Delivery Capacity Pressures](https://www.deltek.com/en/about/media-center/press-releases/2026/the-latest-deltek-clarity-industry-studies-highlight-ai-challenges)
- BL Companies (2026-06-12), [BL Companies Rises Up Rankings on 2026 Engineering News-Record Top 500 Design Firms](https://www.blcompanies.com/bl-companies-rises-up-rankings-on-2026-engineering-news-record-top-500-design-firms/)
- Legal Information Institute (n.d.), [40 U.S. Code § 1103: Selection procedure](https://www.law.cornell.edu/uscode/text/40/1103)
- Acquisition.gov (n.d.), [FAR Subpart 36.6: Architect-Engineer Services](https://www.acquisition.gov/far/subpart-36.6)
- NCEES (n.d.), [Licensure](https://ncees.org/licensure/)
- Canadian Consulting Engineer (2012-12-17), [Clients mostly use peer recommendations to choose engineers](https://canadianconsultingengineer.com/clients-mostly-use-peer-recommendations-to-choose-engineers)
- Gartner (2026-05-20), [Gartner Survey Finds 69% of B2B Buyers Turn to Sales Reps to Validate AI-Generated Insights](https://www.gartner.com/en/newsroom/press-releases/2026-05-20-gartner-survey-finds-sixty-nine-percent-of-b-two-b-buyers-turn-to-sales-reps-to-validate-ai-generated-insights)
- OpenAI Help Center (2026), [ChatGPT search](https://help.openai.com/en/articles/9237897-chatgpt-search)
- Google (2025-03-05), [Expanding AI Overviews and introducing AI Mode](https://blog.google/products/search/ai-mode-search/)
- Underneath (2026), [ChatGPT local recommendations: stable details, shifting lists](https://underneath.agency/research/chatgpt-local-recommendations-study)
- Underneath (2026), [Which Google Maps businesses does ChatGPT recommend?](https://underneath.agency/research/chatgpt-local-picks-google-profile-study)
- Underneath (2026), [Do AI answers match a business’s Google profile?](https://underneath.agency/research/ai-business-facts-accuracy-study)

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