About
I used to optimize for rankings.
Now I optimize for being believed.
Why I started
My name is Mayank Jain. Technical SEO, answer engine optimization, and generative engine optimization all describe the same shift, really. Search engines quietly stopped being the only thing that decides what people find.
I'm also a SaaS builder and website developer — I don't just advise on fixing technical SEO problems, I build the sites and tools that fix them. Most of what's on this domain, from the site itself to the products in /products, I built end to end.
I started in SEO the way most people do. Crawl errors, redirect chains, keyword spreadsheets that never quite told the whole story. Around 2023, I noticed the work splitting in two directions. One side kept optimizing for a results page. The other side asked a harder question: what has to be true about a piece of content before a language model trusts it enough to cite it?
I chose the second question. I haven't gone back.
Timeline
- 2017
Started learning SEO from scratch, mostly by breaking my own small sites.
- 2019
Moved into technical SEO. Crawl budgets, redirect chains, indexation bugs.
- 2021
Led an in-house SEO team and shipped programmatic SEO systems at scale.
- 2023
Started tracking AI Overviews and answer engines as they began reshaping search.
- 2025
Started building software instead of only writing about what should exist.
- Today
Researching modern search full-time, AEO and GEO specifically.
How I think
Rankings were always a proxy
A number one ranking never mattered on its own. It mattered because people clicked it. These days, a citation inside an AI answer can matter more than any ranking, even one that used to sit at the very top of the page.
Most SEO advice is unfalsifiable
If a tactic can't be tested against a real result, it's a guess wearing a framework. I'd rather publish one tested finding than ten plausible-sounding tips.
Building beats explaining
Writing about what a tool should do is slower feedback than building it and watching people use it wrong. Most of what I've learned came from shipping something and being corrected.
Failures worth keeping
Case studies show what worked. These are the ones that didn't, kept here because they changed how I work.
Chased a algorithm update instead of a fundamental
Spent three weeks reverse-engineering a Google core update pattern that turned out to be noise in a small sample. The lesson stuck: single-site anecdotes aren't evidence, no matter how convincing the story feels.
Built a tool nobody asked for
An early rank tracker I built solved a problem I had, not one anyone else did. Nobody used it. That failure is part of why every product I build now starts as research first, and code second.
What I'm building
Right now that's Citeable, plus ongoing citation-rate experiments across AI Overviews and the major LLM answer engines. See the Now page for the current week's version of this, since it changes more often than this page does.
What's next
Rank tracking made SEO measurable twenty years ago. I want to do the same thing for AI search visibility: turn "did I get cited" from a guess into a number you can watch move, built by someone who spent years doing that measuring by hand first.