u/Sarthak999gupta

3 weeks ago, i shipped a small side project from scratch.

it just crossed 100k visitors from SEO.

what's interesting is that this isn't my first SEO project. over the last few years i've worked across a bunch of consumer/AI companies and have seen a few very different growth curves.

some numbers i've worked on:

22k to 700k+ monthly organic clicks in 6 months
6k to 62k+ AI citations/month
0 to 100k SEO visitors in 3 weeks on my own new project

now i've decided to start my own SEO/AEO/GEO agency.

and weirdly, i'm finding the “get clients” part much harder than the actual SEO

for founders who have built a consulting business, agency or service business:

how did you land your first 1–3 clients?

did you rely on your existing network, cold outbound, content, referrals, communities, or something else?

i'm especially interested in what actually worked when you had results but no agency brand yet.

This version gives founders something they can actually answer, while your results establish credibility without making it read like an ad.

i've spent way too much time manually scrolling through influencer profiles.

over 1M+ profiles, with a small team doing the same thing.

and after doing this at scale, i realised there's a pretty annoying problem with influencer marketing:

you either play the quality game or the volume game.

quality = spend a lot of time finding the perfect creators.

volume = find hundreds/thousands of creators and let the numbers work.

i think there's a middle ground:

volume, but with a very specific quality filter.

for example:

• niche/category match
• audience/location
• engagement rate
• average views
• follower growth
• content style
• brand fit
• suspicious/fake engagement

instead of manually going through thousands of profiles, the tool would basically let you define what a “good” creator looks like and surface thousands of matches.

i've been thinking about building this properly rather than continuing to do it manually.

what would you actually pay for something like this?

$50/mo? $200/mo? $500/mo? more?

and if you've built/used an influencer discovery tool before, what are the things i should absolutely keep in mind before building it?

been going through a bunch of things people are building with Jev, and honestly the boring business use cases are more interesting than the flashy demos.

stuff like:

• classify and prioritize support tickets
• score inbound leads
• categorize customer feedback
• qualify prospects
• analyze reviews
• classify emails
• moderate user-generated content
• extract structured data from messy inputs

the interesting part is the economics.

if a model can make a small decision very quickly and cheaply, you can start putting it into workflows where you'd normally just use rules or avoid AI altogether.

for example:

100k leads → score each one → send only the good ones to the expensive workflow

or

50k support tickets → classify → prioritize → route

that's a very different use case from asking an AI to write an email.

i'm collecting these examples in one place because there's already a lot of interesting stuff getting buried in the Jev launch noise.

curious what business workflow you'd throw a cheap, fast model at first?

spent today going through 607 things people are building with Jev.

found a few that made me stop and think:

3,282 X posts analyzed - 8 questions each - 13 cents

500 emails classified - $0.035

browser agent searching for a flight - 7 seconds - $0.004

and this is where it gets interesting for growth.

imagine taking the same idea and running it across:

- 100k leads
- 50k support tickets
- 20k pieces of UGC
- every competitor review
- every ad creative
- every inbound message

without worrying about inference cost killing the experiment.

i've started collecting these builds in one place, but more importantly i'm tagging the ones that can actually be turned into growth workflows.

the goal isn't “look what Jev can do.”

it's “here's something you currently pay a human/tool/API to do thousands of times — and here's what it costs to automate it.”

that's the part i think growth people should be paying attention to.

607 builds so far.

not affiliated with TypeSafe.

if you're doing growth, i'd be curious which repetitive workflow you'd automate first.

Jev dropped today.

Within hours, my entire X feed was people building weird shit with it.

I started collecting the examples in one place because they were getting buried pretty quickly.

The interesting part about Jev isn't necessarily that it's “smarter” than GPT.

It's that it's extremely fast and cheap for small, structured decisions.

A few examples I've come across:

• 500 emails classified for $0.035
• a browser agent searching for a flight in 7 seconds for $0.004
• 3,282 X posts scored across 8 questions for 13 cents
• Doom
• trading bots
• fraud detection
• Postgres queries
• content moderation
• support routing

607 builds/use cases so far.

For each example, I'm trying to capture more than just the demo:

• what the model is being used for
• cost, when available
• latency, when available
• the actual workflow/use case
• links to the original build when available

I'm also adding guides for some of the use cases so someone starting from zero can actually reproduce them.

One use case I keep coming back to is the boring one:

support tickets > classify > prioritize > route

That's the kind of repetitive decision where the economics could get really interesting.

I'm also trying to separate impressive demos from things that could actually run in production — because a curl command working once isn't the same as processing thousands of requests reliably.

I built ShipWithJev .com to organize all of this. I'm not affiliated with TypeSafe.

Would love feedback on how to make the directory more useful — especially what metadata you'd want to see on each example.

If you've built something with Jev, feel free to add it.

jev dropped today.

within hours, my entire X feed was people building weird shit with it.

so i built a directory of the things people are actually building with Jev.

the interesting part about Jev isn't that it's “smarter” than GPT.

it's that it's ridiculously fast and cheap for small, structured decisions.

someone classified 500 emails for $0.035.

someone built a browser agent that searched for a flight in 7 seconds for $0.004.

someone scored 3,282 of their own X posts, 8 questions each, for 13 cents.

someone got it playing Doom.

and then things got even weirder:

trading bots.

fraud detection.

postgres queries.

browser agents.

games.

content moderation.

support routing.

the problem?

all of these demos were getting buried in the X timeline.

so i started collecting them.

607 builds/use cases in there right now, with cost + speed numbers where the builder shared them.

also added a few guides for people starting from zero.

not affiliated with TypeSafe — i just didn't want all the interesting stuff people were building with Jev to disappear into the timeline.

and honestly, the use case i keep coming back to is probably the boring one:

support tickets.

take hundreds of incoming tickets > classify them > prioritize them > route them to the right team.

that's exactly the kind of repetitive decision where a model like this gets interesting.

i've probably missed a bunch.

if you built something with Jev, submit it to ShipWithJev.

takes about a minute.

At 18 I had a small SEO agency out of my bedroom. 4 clients, local businesses, citations and on-page basics, charging peanuts because I didn't know better. Then the usual story: family said get a real job, I got one, stayed employed for years.

Never fully stopped doing SEO though. The last stretch I've been running growth in-house at a health-tech startup (US market, YMYL niche, hard mode), and the results finally convinced me to restart the agency properly:

• Organic clicks grew from ~222K to ~700K over the run
• AI-referred traffic (ChatGPT/Perplexity/AI Overviews) grew from ~6K to ~62.6K/mo, mostly from content built to be extractable and honestly hedged
• Built an internal-linking tool that weaves every article into one topical web automatically: orphan pages to zero, plus an outreach-ready map of which pages deserve backlinks, so link building starts with targets instead of guesses
• Cleaned ~$1,400/mo (₹1.2L+) of wasted ad spend via search-terms audits, blended CAC went $28 → $19

In-house numbers, not client numbers. Saying that upfront because agency posts die in comments over exactly that.

Question for those who've done the job to freelance/agency return trip here: what do you wish you'd done differently on pricing for international vs Indian clients in the first 90 days? And how did you handle the advance-payment conversation without losing the client? Genuinely asking, first calls start next week.

Follow-up to my results post. Enough DMs asking "how" that I'm writing it once. Solo operator, health niche, one frontier-model agent with tool access, human medical review on everything. Here's the system, in the order that mattered.

1. We wrote for the extraction, not the ranking.
Every page opens with a 60-90 word direct-answer block: the query answered completely, self-contained, keyword in sentence one. That block is what assistants lift. Then key takeaways, then sections where each H2 answers one sub-question with one citable fact per paragraph. AI search doesn't reward your page, it rewards your paragraphs. Write paragraphs that survive being quoted alone.

2. Honest hedging became our citation magnet. Not kidding.
Our most-cited pages say things like "the evidence is preclinical," "this hasn't been replicated," "you probably don't need this test." Assistants are tuned hard against overclaiming in health. Every competitor page screaming miracle cures is invisible to them. The compliance layer we built for FTC/YMYL reasons turned out to be the GEO strategy. The boring E-E-A-T religion everyone declared dead? That's literally the training target.

3. FAQ blocks, 8-12 per page, answers 20-70 words, varied lengths.
Each FAQ targets one long-tail phrasing of the intent. Assistants answer questions; a page that pre-answers twelve of them is twelve citation opportunities. Vary the answer lengths or the whole block pattern-matches as generated and gets skipped.

4. Zero hallucinated links, enforced by tooling.
Agent rule: fetch the live sitemap, link only verified URLs, flag anything unverifiable to me. Also killed orphan pages, every article enters a cluster with 2-4 internal links in and out. Assistants crawl like skeptical librarians; a site with dead ends and 404s reads as unmaintained, and unmaintained doesn't get cited in health.

5. Clusters, not articles.
We shipped in pods: 9-11 pieces per topic covering the head term, the comparisons (X vs Y), the timelines, the "is it safe," the "how long does it take." When an assistant assembles an answer, it prefers pulling three facts from one coherent source over stitching five random ones. Own the whole conversation, get cited as the whole conversation.

6. One canonical page per intent, enforced ruthlessly.
The agent caught two planned articles about to cannibalize each other and struck one, unprompted. AI search punishes intent-splitting worse than Google does: two half-answers compete against each other for one citation slot. We assign every keyword set to exactly one URL and compress overlapping sections into links.

7. De-AI'd the prose with hard constraints, not vibes.
"Sound more human" does nothing. What worked: zero em-dashes, forced sentence and paragraph length variance, one staked opinion per piece, a literal banned-phrase list, scene-led openings. Ironic loop: content that reads machine-generated gets cited less by machines. The detectors are in the pipeline.

8. Named entities and real citations in-text.
Hyperlinked primary sources (CDC, FDA, PubMed) woven into prose, specific study details (sample sizes, years, journals) instead of "studies show." Assistants ground answers in pages that are themselves grounded. A page citing the FDA briefing document by name outranks a page vibing about the same topic, in AI retrieval specifically.

9. Measurement, since everyone asks: referrer segmentation for the assistant domains + AI-crawler hits in logs + an AI-visibility tracker for mention share. Imperfect, directionally solid, and the caveat stands: AI-referred visitors convert differently than organic. Track it as its own channel with its own math.

What didn't matter: schema beyond the basics, freshness-faking with fake "updated" dates (we don't even use last-updated lines), word count for its own sake, and every "GEO hack" involving hidden prompts in your HTML, which is a great way to get your domain flagged.

The one-sentence version: we made the most extractable, most honestly-hedged, most internally-coherent resource in the niche, and the assistants did what they're built to do. There's no trick. The trick is that there's no trick, executed 35 times with a human holding the judgment layer.

AMA.

Opening limited editorial slots on a health site I manage. The unusual part first: it's genuinely multilingual — English plus 5 properly localized European editions (native rewrites, not machine translation), all on one strong domain. If your audience is DE/FR/IT/ES/TR, a placement here reaches markets most health sites can't touch.

The numbers (verified live in DM — Ahrefs + GSC screenshare before payment):
- 700K+ organic visits/month, growing
- Domain or Niche: Health 
- DA or DR: 55+
- Price: from $XXX (varies by format and language edition)
- Traffic split across US + major EU markets, not one-country dependent

Formats:
- Guest post — new article built around your target URL
- In-content insert on an existing topically relevant page
- Dofollow, permanent as long as the site lives, 2-5 day turnaround
- EN placement standard; localized-edition placement available for the EU markets

Won't accept at any price: casino, adult, crypto, payday loans, gray pharma, miracle cures. The review gate is why placements here keep their value.

DM with site + target URL + anchor. Pricing depends on format and language edition.

Opening limited editorial slots on a health site I manage. The unusual part first: it's genuinely multilingual — English plus 5 properly localized European editions (native rewrites, not machine translation), all on one strong domain. If your audience is DE/FR/IT/ES/TR, a placement here reaches markets most health sites can't touch.

The numbers (verified live in DM — Ahrefs + GSC screenshare before payment):

- 700K+ organic visits/month, growing

- DR 55+, 4K+ referring domains

- Traffic split across US + major EU markets, not one-country dependent

Formats:

- Guest post — new article built around your target URL, ours or yours, must clear medical review

- In-content insert on an existing topically relevant page

- Dofollow, permanent as long as the site lives, 2-5 day turnaround

- EN placement standard; localized-edition placement available for the EU markets

Won't accept at any price: casino, adult, crypto, payday loans, gray pharma, miracle cures. The review gate is why placements here keep their value.

DM with site + target URL + anchor. Pricing depends on format and language edition.

Managing a health site with spare editorial room, opening limited slots.

Metrics (exact numbers verified live in DM — Ahrefs + GSC before any money moves):

- DR 55+, UR 45+

- 700K+ organic visits/month

- 4K+ referring domains

- Multilingual: English + 5 European languages, properly localized (not auto-translate)

- Real editorial operation with in-house medical review — not a PBN, not a farm

On offer:

- Guest post: new article around your target URL (yours or written by us — either way it passes our medical review)

- Link insert: in-content placement on an existing relevant page

- Dofollow, stays live as long as the site does

- 2-5 day turnaround

Hard exclusions, any budget: casino, adult, crypto, payday, gray pharma, miracle-cure content.

DM site + target URL + anchor for pricing. Serious inquiries only.

Running a large YMYL content site (~790k organic/mo). Trying to get serious about AI visibility tracking and immediately hit this:

Clarity dashboard (screenshot): 512k total citations across tracked queries, 16.45% "share of authority" — apparently 1 in 6 citations in our query space are us, out of ~3.1M total.

Ahrefs AI citations view, same domain: ~62k/month.

That's an 8x gap and I don't know which number to believe, or if I'm comparing apples to engines. Questions for people deeper in this than me:

1. How is share of authority actually calculated? Is the 512k counting every appearance in every response every time the tool re-runs a query? That alone would explain most of the inflation vs Ahrefs
2. Is anyone using any of these as a real KPI yet, or is the honest answer "pick one tool, only trust the trend"?
3. The branded split reads weird — branded SoA is 27% but only ~339 citations, non-branded is 16% with 511k. I'm reading it as "we own our tiny branded space and the entire game is non-branded". Wrong?
4. There's a jump in the trend after Aug 24 (roughly 19k → 31k). Timing lines up with the August spam update that targeted citation manipulation. Anyone else see share move late August — possibly from competitors getting cleaned out?

Not selling anything, genuinely trying to decide what to track weekly before I build reporting around a number that turns out to be noise.

running a large YMYL site, ~790k organic/mo. Started tracking AI visibility properly this month and the tools disagree by 8x.

clarity says 512k citations across our tracked queries, 16.45% share of authority (out of ~3.1M citations in the query space). Ahrefs AI view says 62k/month for the same domain. Screenshot attached.

my guess is the 512k counts every appearance every time queries get re-run, while Ahrefs samples its own index, so they're basically polling different populations. But if anyone actually knows how SoA gets calculated I'd love the real answer.

also noticed our trend jumps from 19k to 31k right after Aug 24, which lines up suspiciously well with the spam update that targeted citation manipulation. Anyone else's share move late August? Wondering if competitors got cleaned out of the answer space.

Mostly trying to decide what's worth tracking weekly before I build reporting on a number that might be noise.

running a large YMYL site, ~790k organic/mo. Started tracking AI visibility properly this month and the tools disagree by 8x.

clarity says 512k citations across our tracked queries, 16.45% share of authority (out of ~3.1M citations in the query space). Ahrefs AI view says 62k/month for the same domain. Screenshot attached.

my guess is the 512k counts every appearance every time queries get re-run, while Ahrefs samples its own index, so they're basically polling different populations. But if anyone actually knows how SoA gets calculated I'd love the real answer.

also noticed our trend jumps from 19k to 31k right after Aug 24, which lines up suspiciously well with the spam update that targeted citation manipulation. Anyone else's share move late August? Wondering if competitors got cleaned out of the answer space.

Mostly trying to decide what's worth tracking weekly before I build reporting on a number that might be noise.

Running a large YMYL content site (~790k organic/mo). Trying to get serious about AI visibility tracking and immediately hit this:

Clarity dashboard (screenshot): 512k total citations across tracked queries, 16.45% "share of authority" — apparently 1 in 6 citations in our query space are us, out of ~3.1M total.

Ahrefs AI citations view, same domain: ~62k/month.

That's an 8x gap and I don't know which number to believe, or if I'm comparing apples to engines. Questions for people deeper in this than me:

1. How is share of authority actually calculated? Is the 512k counting every appearance in every response every time the tool re-runs a query? That alone would explain most of the inflation vs Ahrefs
2. Is anyone using any of these as a real KPI yet, or is the honest answer "pick one tool, only trust the trend"?
3. The branded split reads weird — branded SoA is 27% but only ~339 citations, non-branded is 16% with 511k. I'm reading it as "we own our tiny branded space and the entire game is non-branded". Wrong?
4. There's a jump in the trend after Aug 24 (roughly 19k → 31k). Timing lines up with the August spam update that targeted citation manipulation. Anyone else see share move late August — possibly from competitors getting cleaned out?

Not selling anything, genuinely trying to decide what to track weekly before I build reporting around a number that turns out to be noise.

Running a large YMYL content site (~790k organic/mo). Trying to get serious about AI visibility tracking and immediately hit this:

Clarity dashboard (screenshot): 512k total citations across tracked queries, 16.45% "share of authority" — apparently 1 in 6 citations in our query space are us, out of ~3.1M total.

Ahrefs AI citations view, same domain: ~62k/month.

That's an 8x gap and I don't know which number to believe, or if I'm comparing apples to engines. Questions for people deeper in this than me:

1. How is share of authority actually calculated? Is the 512k counting every appearance in every response every time the tool re-runs a query? That alone would explain most of the inflation vs Ahrefs

2. Is anyone using any of these as a real KPI yet, or is the honest answer "pick one tool, only trust the trend"?

3. The branded split reads weird — branded SoA is 27% but only ~339 citations, non-branded is 16% with 511k. I'm reading it as "we own our tiny branded space and the entire game is non-branded". Wrong?

4. There's a jump in the trend after Aug 24 (roughly 19k → 31k). Timing lines up with the August spam update that targeted citation manipulation. Anyone else see share move late August — possibly from competitors getting cleaned out?

Not selling anything, genuinely trying to decide what to track weekly before I build reporting around a number that turns out to be noise.

https://preview.redd.it/b1bihz9a8bph1.png?width=944&format=png&auto=webp&s=106766711c3af30f45d2fe1c4dd03fac9ef878e7

I posted the 22k -> 790k case study recently and the AI citations part got the most questions, so writing it up properly.

Where we're at: 60k+ citations/month across AI platforms (Ahrefs' AI citations view). Perplexity is ~29k of that, Google AI Mode ~12k. It's turning into a real acquisition channel and honestly most of it happened as a side effect of doing normal things properly.

What I think actually moved it:

1. Answer the question immediately. First 2-3 sentences, before any context-setting. Assistants don't scroll past your intro looking for the point. Neither do humans tbh.

2. Self-contained answers. Assistants lift blocks, not pages. If an answer starts with "as mentioned above" you've written yourself out of the citation. Every FAQ answer has to work on its own.

3. Phrase questions like humans ask them. "does X work for Y?" not "X Y effectiveness". We pull phrasing from PAA, Ahrefs question filters, autocomplete, and just asking ChatGPT/Perplexity what someone would want to know before choosing X.

4. 8-9 FAQs per post, FAQ schema on everything. Lots of directly-answered questions per page beats one perfect answer.

5. Real authors, expert review, sources. We're YMYL so no choice anyway, but my strong impression is assistants skip anonymous content everywhere.

Tracking: Ahrefs AI citations + referral traffic from perplexity.ai / chatgpt.com in analytics. Worth setting a baseline now since GSC's AI reports are rolling out.

My question: Clarity says 512K citations in a month, Ahref says 62K. Any context on this part?

Questions welcome.

Growth lead here — worked with 10+ consumer startups. This is the biggest organic run I've been part of, and the interesting part isn't "SEO worked." Two of the three arbitrages are still wide open, so writing it up.

Starting point: YMYL site stuck at ~22k organic visits/mo. Six months later: ~790k/mo.

Ad spend: $0 the entire time. (Anonymized Ahrefs/GSC screenshots in comments if people want proof.)

1. Language arbitrage. The same article that fights 50 competitors in English is often nearly uncontested in German. We took proven English pages and properly localized them. Germany is now our #1 market, ahead of the US. The cost of localizing a winning page vs the cost of winning an English SERP isn't even close, and almost nobody in our space was doing it. The crazy thing our 40% US traffic is in russian & polish.

2. AI citation arbitrage. We rewrote key pages around the questions people actually ask AI assistants, not just what they type into Google. The site now gets cited 60k+ times a month across AI platforms — Perplexity alone is ~29k.

3. Boring-work arbitrage. Before any content: fixed the technical plumbing so Google could actually find, crawl, and load everything fast. Nobody posts about this work, which is exactly why it's underpriced. Fastest-compounding month of the whole project.

What we did NOT do: no viral loops, no referral scheme, no paid, no PR stunts.

The stat that rewired my thinking: average ranking position barely moved all six months. The growth came from surface area — more pages, more questions answered, more languages — not from pushing #11 to #3 on hero keywords. Coverage beat rankings, by a lot.

YMYL caveat: real authors, expert review, primary sources. Skip the trust layer in a YMYL niche and none of the above matters.

Happy to break down any piece in comments.

have a health site i manage with spare editorial room. numbers up front:

Domain or Niche: Health ai
DA or DR: 58
price: 500 USD
traffic: ~796K organic visits/mo, 46.9K keywords
geo split: US ~60K, DE ~61K, TR ~54K, IT ~51K, FR ~48K (multilingual subfolders on one domain, not a translation farm, can show the breakdown)

format: in-content link insert on an existing relevant page, or a new article around your URL. dofollow, stays live as long as the site does, 2 to 5 day turnaround.

won't touch casino, adult, crypto, payday, or gray pharma regardless of budget.

DM site + target URL + anchor for pricing. can verify metrics live (Ahrefs, GSC) before any money moves. questions in comments are fine.