Ask ChatGPT to recommend firms in your niche and one of two things happens: it names you, or it teaches you something unpleasant about your marketing. Either way, the answer was assembled from ingredients, and every ingredient is something you can change.
In how AI assistants decide which ERP firms to recommend, we covered the mechanics of why answers come out the way they do. This post is the workbench version: the specific, sequenced actions that move a firm from unmentioned to cited.
Nothing here is a trick. It is a checklist for becoming the kind of firm a cautious machine is willing to vouch for.
First, understand the two doors
There are two ways into a ChatGPT answer, and they respond to different work.
The memory door. The model’s training absorbed the public web. If your firm was described widely and consistently, the model “knows” you and can name you even without browsing. Influence here is slow and cumulative. It comes from months of consistent presence, and it pays off across every future model trained on a web that includes you.
The browsing door. For fresh or specific questions, ChatGPT searches the live web, reads a handful of results, and cites them. Influence here is faster and looks suspiciously like SEO: rank for the underlying query, be extractable when the machine arrives, get cited today. Industry coverage of AI search, from outlets like Search Engine Land, has converged on the same observation: retrieval-based answers lean heavily on whatever wins the classic results page.
The playbook below works both doors at once, in order of foundation to finish.
Step 1: Make your firm legible
Before anything clever, fix the boring layer: can a machine state plainly who you are?
Rewrite your homepage and About page so the first hundred words answer four questions in prose a twelve-year-old could parse:
- What you are
- What you do
- Who you do it for
- Where you do it
“IgnitX is a marketing agency for ERP consulting firms” is machine food. “We ignite growth journeys” is static.
Then propagate that exact description everywhere your firm exists: LinkedIn, Crunchbase, vendor directories, review profiles, podcast bios, everywhere. Consistency is not a branding nicety here. It is how a model becomes confident that all those mentions are one entity. Contradictory descriptions across the web literally dilute your existence.
Finish the layer with Organization markup using Schema.org vocabulary, including sameAs links tying your site to your profiles, so the connections are declared rather than inferred. The full implementation walkthrough is in our schema markup guide for consulting firms.
Step 2: Build citation-magnet pages
ChatGPT cites pages that make its job easy. When it browses on behalf of a buyer’s question, it is looking for pages that contain a confident, extractable answer to that question. Most consulting sites offer none, which is why assistants so often cite directories and listicles instead: those pages, whatever their flaws, commit to answers.
A citation magnet has three properties:
- It targets a real question your buyers ask, phrased the way they ask it.
- It answers with specifics. Names, numbers, criteria, comparisons, steps, not adjectives.
- It is structured for extraction. Descriptive headings, short definitional paragraphs, FAQ blocks, tables where tables help.
For an ERP consultancy, the highest-yield magnets are usually:
- Deep service pages that state exactly what you do and for whom (our guide to service page copywriting for consultancies covers the craft)
- Case studies rich with industry and outcome detail
- Honest cost and timeline explainers
- Comparison content
- Practitioner guides in your niche
Notice these are the same assets that win human buyers. The machine audience did not change the assignment, it raised the reward for doing it well.
One tactical note: put a clear, quotable summary near the top of important pages. A two-sentence “who this is for and what it covers” block is the part most likely to survive into an AI answer verbatim.
Step 3: Earn independent corroboration
A machine citing you is a machine taking reputational risk, and it hedges the way a careful human does: by preferring claims that someone other than you has confirmed.
The corroboration ladder, from most accessible up:
- Reviews on platforms like G2 and Clutch
- Complete profiles in your vendor’s partner directory
- Guest articles and podcast appearances in your niche’s publications
- Genuine participation in communities like r/Netsuite, where your firm gets mentioned by name in helpful contexts
- Coverage in industry press
At the top of the ladder sits the Wikipedia notion of notability, which almost no boutique firm reaches and none need to. The ladder’s lower rungs carry plenty of weight, and they compound with the on-site trust signals human buyers already look for.
Two rules keep this clean:
- Corroboration must be real. Incentivized reviews and planted mentions are detectable, punishable, and worse than nothing.
- It must be on-topic. A mention that ties your name to your platform, industry, and region strengthens exactly the associations that answer buyer questions. A generic “great company” mention strengthens almost nothing.
Step 4: Open the gates
None of the above matters if the machines cannot read you. Three checks, ten minutes total:
- Check robots.txt. Confirm you are not blocking the AI crawlers you want answering questions about you. Some security plugins and CDN defaults block them wholesale, and firms discover this only after months of invisibility. Infrastructure providers like Cloudflare now offer granular controls over which AI bots may crawl, which is genuinely useful, provided someone makes the choices deliberately rather than inheriting a blanket no.
- Consider an llms.txt file. This emerging convention, documented at llmstxt.org, points language models at a clean, curated summary of your site. Adoption by the models is still uneven, but the cost is an hour, and the file doubles as a useful exercise: writing it forces you to state what your site is about in exactly the plain terms Step 1 demands.
- Keep the pages technically clean. Fast, crawlable, sensibly structured, with valid structured data. Machine readers forgive even less than human ones.
Step 5: Win the searches underneath
Because the browsing door runs on search, your rankings are now dual-purpose. Every partner-intent and problem-intent query you win organically is a query where ChatGPT’s browsing finds you first. The keyword strategy does not change; the payoff doubles.
For the full organic approach we use with clients, see SEO for NetSuite partners, and for how the two disciplines relate, GEO vs SEO draws the map. If you would rather hand this layer to a specialist, it is exactly what our NetSuite Partner SEO service was built for.
The practical sequencing insight: if you are choosing between writing a new “GEO page” and improving the ranking of an existing high-intent page, improve the ranking. Retrieval visits the winners.
Step 6: Test, log, repeat
Finally, measure like it is a channel, because it is. Once a quarter, ask ChatGPT the questions your buyers ask, in several phrasings, in fresh sessions. Log:
- Whether you are named
- How you are described
- What gets cited
- Who else appears
Description errors are actionable: they tell you which sources the model is leaning on, and which of your pages or profiles is feeding it stale facts. The complete framework, including scoring and competitor mirroring, is in our AI visibility audit guide.
Expect the memory door to move slowly and the browsing door to move in weeks. A firm that fixes its entity layer, ships three citation magnets, earns a dozen genuine reviews, and wins two underlying queries will usually see itself start appearing in browsed answers within a quarter, and describe that appearance accurately, which is half the battle.
The 30-60-90 execution plan
Six steps can blur into “someday,” so here is the same work as a calendar.
Days 1 to 30: legibility and gates.
- Rewrite the homepage and About self-descriptions in plain language
- Propagate the exact wording to LinkedIn, Crunchbase, directories, and review profiles
- Ship the Organization schema with a complete sameAs array
- Check robots.txt and your CDN settings for accidental crawler blocks, and publish an llms.txt while you are in there
- Run the baseline measurement: your buyers’ questions, across the assistants, logged verbatim
Thirty days, mostly configuration, and the foundation for everything after.
Days 31 to 60: the first citation magnets.
- Pick the three questions from your baseline where being cited would matter most, usually one recommendation query, one cost or process question, and one comparison
- Build or rebuild the page for each: specific claims, quotable summary up top, FAQ block, clean structure, marked up
- In parallel, start the corroboration engine: ask your five happiest recent clients for reviews, and pitch two niche podcasts or publications where a genuine contribution is possible
Days 61 to 90: rank the substrate and re-measure.
- Push the underlying queries with internal links, refreshed content, and whatever technical debt the pages carry, because retrieval visits the winners
- Land the first outreach placements
- Rerun the exact baseline prompts and compare: presence, position, accuracy, sources
The delta is your proof of concept, and the source trails in the new answers are your next quarter’s list.
How you will know it is working
The channel reports in three signals, in the order they usually arrive:
- Description accuracy. When you ask the assistants about your firm directly, the answers stop being vague or wrong and start reciting your actual positioning, often in phrasing you recognize from your own pages. That means the entity layer landed.
- Browsed presence. Your firm and pages begin appearing, cited, in answers to the buyer questions you targeted.
- Referral evidence. Assistant domains show up as traffic sources in your analytics, and prospects on discovery calls say some version of “ChatGPT mentioned you,” which clients of ours now hear often enough that it has stopped being a novelty.
Log all three quarterly. A channel you measure is a channel you can defend budget for.
When ChatGPT gets you wrong
A special case worth its own playbook: the assistant names you but misdescribes you. Wrong specialty, wrong geography, a service you retired years ago. Treat it as a gift wrapped in an insult, because errors are traceable in a way absence never is.
Work the correction at the source, not at the symptom. Ask the assistant, in a browsing-enabled session, where its description comes from, and check the usual suspects yourself:
- Old directory profiles
- A stale Crunchbase entry
- An acquired-and-forgotten microsite
- A years-old guest bio
Fix the facts wherever they live, then strengthen the correct signal: make sure your current positioning is stated identically on every property you control, and that the pages carrying it are the ones winning the underlying searches. Where a platform offers feedback mechanisms on answers, use them, but treat that as a courtesy note, not the fix. The durable correction is a web that no longer contains the error.
Re-test in the next quarterly pass. Most misdescriptions clear within one or two cycles once their source dries up, and the exercise usually surfaces two or three other zombie profiles worth killing while you are in there.
The honest timeline
Getting cited by ChatGPT is not a campaign with a launch date. It is a byproduct of becoming unusually well-documented at being good at a specific thing. The firms winning this early are not the ones with a secret; they are the ones who did ordinary reputation work with uncommon consistency, then made it machine-readable.
If you want to know what the machines say about your firm today, and exactly which of these six steps would move your name into the answers, that is what our audit maps. The buyers are already asking. The only question is whose name comes back.
