DevTools
Is ChatGPT recommending your tool?
Your buyers stopped googling and started asking AI which tool to use. We test the questions they ask, show you where you land, and hand you a fix list.
Where AI looks before it names a tool
When someone asks an AI engine which tool to use, the engine assembles its answer from sources it already trusts. For developer tools, those sources are remarkably consistent.
Comparison and listicle pages carry most of the weight. Engines borrow "best tools for X" lists published by vendors and review sites, which means the list your competitor published last quarter is quietly deciding whether you exist in the answer.
Documentation, GitHub activity, and community discussion come next. A polished website alone gives the engines little to quote; the third-party traces around your product are what the answer is built from.
Patterns above are from CiteGap's own collection work across engine answers. Published case evidence: Rootly x AthenaHQ (vendor case study) and Gumlet x DerivateX (agency case study).
incident.io
Most-named in our audit
Rootly
Most-named in our audit
PagerDuty
Most-named in our audit
In our October 2026 audit of 11 tracked incident-management tools, these three names took most of the recommendation spots. The eight others split the remainder. Read the full audit: what AI recommends for incident management.
What the research shows
The published numbers on devtool visibility in AI answers. Each figure carries its sample and who published it, because a vendor case study and an independent survey travel different distances.
51%
of B2B software buyers now start research with an AI chatbot more often than with Google, up from 29% a year earlier. G2 surveyed 1,076 B2B software buyers; published April 2026.
3% → 30%
Rootly's citation rate across AI answers, a 10x increase, with the non-branded mention rate up 2.5x. Published by AthenaHQ, the vendor behind the work.
~20%
of Gumlet's inbound revenue traced to AI discovery. In one surveyed week, 25.96% of new users said AI sent them. Published by DerivateX with the client's CMO on record.
69%
of buyers said AI answers changed which vendor they chose, and one in three bought from a vendor they had never heard of before researching. Same G2 research, published April 2026.
Note on our own data: CiteGap's devtools benchmark tables are not published yet. They will be built from real client runs as those accumulate, and labeled as ours when they exist. Every number on this page is third-party research or a labeled vendor case study, with the sample and link attached.
What buyers ask AI
Example questions, illustrative"Which tool should we use to automate developer changelogs?"
"Best open source feature flag tools for a small team"
"What's a good self-hosted alternative to [commercial product]?"
"How do teams usually run end-to-end tests in CI?"
"Which monitoring tool is easiest to set up for a Node.js app?"
Constructed examples based on common selection patterns, not captured from real sessions. The Written Diagnosis replaces them with the questions your actual buyers ask.
How the answer looks when you're skipped
"For changelog automation, most teams use [Competitor A], [Competitor B], or [Competitor C]. All three have public roadmaps and active communities. I couldn't find enough third-party information about your tool to include it."
Pattern we see repeatedly: absent tools aren't criticized, they're just never assembled into the list, because the list is borrowed from sources you don't appear in.
Six checks you can run today
No tools needed beyond the engines themselves. Write down what you see each time; the pattern across answers is the finding.
Open a fresh ChatGPT session and ask how a buyer would: "Best [category] tool for [team size]?" Note whether your tool is named, and which competitors come up instead.
Ask "alternatives to [market leader]" and "[leader] vs [competitor]". These are the queries where challengers get named, or silently dropped.
Read the "best of" listicles that rank for your category. If competitors published them and you're absent, that absence is being quoted back to buyers.
Check what the engines can see beyond your website: docs indexed, GitHub activity, community threads. Sparse third-party traces starve the answer.
Look at the tools that do get named and list what they share: comparison coverage, documentation depth, community size. That list is your gap map.
Repeat the same question a week later. Single runs mislead; the pattern across engines and time is the real signal.
From free check to written diagnosis
The free check runs against your homepage and reports seven technical aspects that decide whether AI engines can read your site at all. It is not devtools-specific, and it says so on the result.
The Written Diagnosis ($490) answers the category question for your tool: 30 buyer-style questions across ChatGPT, Perplexity, and Gemini, run twice over two days, written up as a report of who gets named and cited in your category, where you stand, and what to fix first. The Fix Sprint ($1,250) executes that fix list for you. Monthly monitoring tracks how the answers shift as engines update.
Keep reading
Questions devtools ask
Do buyers really ask AI which devtool to use?
G2's research, published April 2026, surveyed 1,076 B2B software buyers: 51% now start their research with an AI chatbot more often than with Google, up from 29% a year earlier. Developer tools are bought by the same buyers, with the same habits: the selection question has moved into the chat window.
Which sources do AI engines cite for devtools?
The answers are assembled from sources the engines already trust: comparison and listicle pages, product documentation, GitHub repositories, and community discussion. Two consequences follow. If a competitor published the "best tools for X" list, you are in the answer only if they included you. And a polished website without third-party traces gives the engines little to quote.
Can a devtool actually grow its citation rate?
The published case studies say yes, with vendor caveats. AthenaHQ's Rootly case study reports the citation rate growing from 3% to 30% (10x) and the non-branded mention rate growing 2.5x. DerivateX's Gumlet case study reports close to 20% of inbound revenue traced to AI discovery, and 25.96% of new users in one surveyed week saying AI sent them. Both are vendor-published case studies, labeled as such.
Is this data specific to my category?
No. G2's survey covers B2B software buyers broadly, and the case studies cover two specific products. CiteGap's own devtools benchmark tables are not published yet; they will be built from real client runs as those accumulate. Our published audit of 11 incident-management tools shows what concentration looks like in one devtools category.
What does the free check cover?
The free check examines seven technical aspects of your homepage instantly and shows the result of each. It is not devtools-specific: it tells you whether AI engines can read your site at all, which is a precondition for being cited. No signup required.
See whether the engines can read your tool
The free check runs instantly against your homepage. No signup, no cost.
Run the free check