FlowIntent uses peec.ai for LLM mention monitoring and DataForSEO for SERP validation. Here's the exact workflow, what it found, and why it changed how we think about AEO.

client zero: we didn't know AI was ignoring us until we measured it

six months into building FlowIntent, we were pretty confident we had decent AI brand visibility. we were writing AEO-optimised content, using question-based headers, FAQ schema, the works. we'd even seen a couple of Perplexity citations appear organically.

then we wired up peec.ai and ran it against our actual target queries.

the result: out of 40 queries we cared about, FlowIntent appeared in AI answers for 6 of them. our two main competitors appeared in 31 and 28 respectively.

we'd been optimising in the dark. here's what changed when we got the lights on.

why we needed a dedicated AI mention monitoring tool

before peec.ai, our AI visibility tracking was manual. every week or two, someone (usually me) would open Perplexity, ChatGPT, Grok, and Gemini and run a handful of queries. log the results in a spreadsheet. try to see if anything had moved.

the problem with that workflow is the sample size. i was checking maybe 8–10 queries per engine. we had 40+ queries that mattered to us commercially — "best AEO tool for founders," "how to check if my brand appears in AI answers," "what tool tracks LLM brand mentions," etc. manually checking all of them across 4+ engines weekly wasn't happening.

so the data we had was directional at best. and directional isn't enough when you're trying to build a systematic AEO strategy.

peec.ai solved this by running automated monitoring across our full query list, across all the major LLMs, and surfacing changes without me having to touch it. it tracks mention frequency, sentiment, where in the response you appear (cited vs mentioned in passing vs not mentioned), and how that shifts over time.

what we actually found

we set up peec.ai with three query clusters:

cluster 1 — category queries (what are the best AEO tools, how to track AI brand mentions, AI search optimisation tools for founders)
cluster 2 — problem queries (how do I know if my site appears in AI answers, how to improve Perplexity visibility, how to rank in ChatGPT search)
cluster 3 — branded comparison queries (FlowIntent alternatives, FlowIntent vs Semrush, AEO tools comparison)

the initial snapshot was uncomfortable. out of 40 queries tracked, FlowIntent was mentioned in only 6 (15%). Competitor A appeared in 31 (78%), Competitor B in 24 (60%).

six mentions out of forty. that's not a visibility problem — that's near-invisibility.

the breakdown by engine made it worse: every mention we had was in Perplexity. zero in ChatGPT. zero in Gemini. one in Grok (which was actually inaccurate — the model described us as "a keyword research tool," which we're not).

where DataForSEO came in

peec.ai told us where we weren't showing up. DataForSEO told us why.

the hypothesis: our content wasn't ranking well enough in traditional search for AI systems doing real-time retrieval to find us. Perplexity and ChatGPT search both index the web in near-real time — if your pages aren't appearing in the SERPs for the queries that matter, they're unlikely to be retrieved and cited.

we pulled SERP data for our 40 tracked queries via DataForSEO's SERP API. the results confirmed the hypothesis: for category queries, our pages appeared in the top 20 for 3 out of 16 keywords. for problem queries, 4 out of 14. for branded comparison queries, 1 out of 10 (and that one was a Reddit thread mentioning us, not our own page).

the AI visibility gap was a search visibility gap first. we'd been trying to optimise for AI citation without solving the underlying indexing and ranking problem.

this is the part most AEO advice misses. real-time retrieval AI engines can't cite what they can't find. the structural content improvements matter — but they have to be paired with pages that are actually in the index for the relevant queries.

what we built as a result

once we had the combined picture — peec.ai for AI mention frequency, DataForSEO for SERP position data — we could prioritise properly.

the workflow we built:

step 1: identify queries where we have zero AI mentions AND zero top-20 SERP presence. these are "build from scratch" targets — we need new pages optimised for these specific queries.

step 2: identify queries where we have some SERP presence (positions 11–40) but zero AI mentions. these are "structure fix" targets — the page exists, it's being indexed, but it's not structured for AI extraction. question headers, answer-first paragraphs, FAQ schema.

step 3: identify queries where we are being cited but with inaccurate descriptions. these need definitional content — pages that clearly, unambiguously define what FlowIntent is and isn't, so models stop hallucinating our category.

we built 8 new pages targeting cluster 1 gaps. restructured 5 existing pages using DataForSEO competitor analysis. and published two definitional pieces specifically to correct the Grok misattribution.

what we tried that didn't work first

before we had this workflow, we'd been taking a scatter-gun approach to the AI visibility problem. write more content. add FAQ schema to everything. submit URLs to search engines faster.

none of it moved the peec.ai numbers.

the thing that actually didn't work was writing content that was too self-referential. early in the process, we published a piece that was essentially a features comparison blog post dressed up as thought leadership. Perplexity wouldn't cite it. ChatGPT ignored it. the piece was too promotional.

the reframe that worked: write content that answers the question completely, mention FlowIntent only where it's genuinely the right example, and let the citation happen because the answer is good — not because the page is a product pitch.

the results at 90 days

we ran the peec.ai audit again at 90 days, targeting the same 40 queries. results:

Category queries: 2/16 (13%) → 9/16 (56%)
Problem queries: 3/14 (21%) → 8/14 (57%)
Branded comparison: 1/10 (10%) → 5/10 (50%)
Total: 6/40 (15%) → 22/40 (55%)

from 15% to 55% query coverage in 12 weeks.

the Grok misattribution was corrected by week 8 — the model now describes FlowIntent accurately as an "AEO and AI brand visibility platform." that one fix alone changed how we appeared in 6 queries.

DataForSEO tracking showed the SERP gains that preceded the AI mention gains — pages moved into the top 10 in weeks 4–7, AI mentions followed in weeks 6–10. the lag between traditional ranking and AI citation was consistently 2–3 weeks.

the workflow in FlowIntent today

the peec.ai + DataForSEO combination is now the core of FlowIntent's own monitoring stack, running on a 7-day cycle:

Monday: peec.ai weekly mention report — flag any queries where mention rate dropped more than 10% week-over-week
Wednesday: DataForSEO SERP check on the queries that dropped
Friday: any pages that need structural updates go into the following week's publishing queue

the total time this takes: about 40 minutes a week. the result: we catch visibility changes within 7 days, not 6 weeks later when the traffic data finally surfaces the problem.

what this means for you

you don't need peec.ai and DataForSEO to apply this logic — you can do the same thing manually with a spreadsheet and a couple of hours a week.

but the manual version will miss things. it'll check 10 queries when you need to check 40. it'll notice a drop in Perplexity mentions but miss that ChatGPT has started mis-describing your product.

the point isn't the tools. the point is the measurement. if you're not tracking AI mentions systematically, you're making AEO decisions based on gut feel and occasional manual checks. that's roughly equivalent to running SEO without Search Console.

get a baseline. even if it's uncomfortable. especially if it's uncomfortable.

the 6/40 number was uncomfortable for us. it's also the number that told us exactly what to fix.

How We Track AI Brand Visibility With peec.ai + DataForSEO