§ The client
IJARST is a peer-reviewed academic journal publishing research across science and technology, running on a custom Laravel website. Like most journals, its entire reason to exist online is discoverability — researchers need to find its papers through Google, Google Scholar, and, increasingly, AI assistants that summarize the literature. The publishing was solid. The machine-readability was quietly broken.
§ The problem: what the baseline audit found
FirePencil's agent crawled the site and scored it against the answer-engine readiness checks. The headline number was blunt: 0/100, with six P0 (critical) blockers. The site wasn't badly designed — several things were done well (Organization, Periodical and FAQ schema were present, robots.txt and social tags were clean, the paper subpages were real). The problem was a handful of high-severity, sitewide defects that quietly made the whole catalog hard for machines to parse:
1. Every page had the same title & description
A sitewide template bug meant every URL — homepage, every paper, every issue — returned the identical title tag and meta description. To a search or answer engine, a hundred distinct papers looked like a hundred copies of one page. This is close to fatal for discoverability.
2. Zero Google Scholar citation tags
The journal had none of the Highwire/Scholar citation_* meta tags Scholar uses to read a paper's title, authors, journal, and dates. Without them, Google Scholar cannot index the articles as scholarship — for a journal, that's the single most damaging gap possible.
3. Duplicate content, no canonical
Both www and non-www served identical content with no canonical tag, splitting signals and confusing crawlers about which version is real.
4. Structure & accessibility defects
15 H1 headings on the homepage (there should be one), 14 of 31 images missing alt text, and a slow server response (TTFB ~1.3s) that hurts both crawl budget and human experience.
5. No AI-readability layer
No llms.txt, and a weak overall entity profile — the machine-readable summary AI engines look for simply wasn't there.
§ The work: what the agent did (owner-approved, ~48 hours)
This is the part that separates an autonomous agent from a dashboard: it didn't just report the problems — it fixed them, with every change approved before shipping. Over roughly two days the agent:
- Rewrote the page templating so every page renders a unique, relevant title and description.
- Added Highwire/Google Scholar citation meta tags across the paper pages, so articles became indexable as scholarship.
- Set canonical tags and consolidated
www/non-wwwto end the duplicate-content split. - Fixed the heading structure (one H1 per page), added the missing alt text, and tuned performance to roughly halve TTFB (~1.3s → ~0.65s).
- Generated and published an
llms.txtand strengthened the entity profile so AI engines have a clean summary to read.
The agent then re-crawled and re-scored after each batch — the score climbed 0 → 82 → 92 → 98 as the blockers cleared, which is how we know each fix actually moved the needle rather than just looking done.
§ The results
| Signal | Before | After |
|---|---|---|
| AEO health score | 0 / 100 | 98 / 100 (held ~94) |
| Critical (P0) blockers | 6 | 0 |
| Page titles & descriptions | Identical sitewide | Unique per page |
| Google Scholar citation tags | None (unindexable) | Present (indexable) |
| Canonical / duplicate content | None; www + non-www dup | Canonicalized |
| Server response (TTFB) | ~1.3s | ~0.65s |
| llms.txt / AI-readability | Absent | Present |
| Entity health | 24 | 35 |
Because the fixes were structural, they showed up in the real world, not just the audit. With Search Console connected, IJARST recorded 7,358 impressions and 200 clicks across roughly 494 distinct queries in a 30-day window — an organic footprint that simply couldn't accumulate while every page looked identical to Google. And in FirePencil's 30-day multi-engine AI sweep, the journal's brand began getting picked up across ChatGPT, Claude, Gemini and Perplexity, with the strongest early AI-citability on Gemini.
A journal's whole job online is to be found. In 48 hours it went from "machines can't tell these papers apart" to a clean, indexable, AI-readable foundation.
§ The honest part: a foundation, not a finish line
Two days fixed the technical foundation — the fast, high-impact layer where a broken template or missing tags can crater an entire site. Getting cited as the authoritative answer across AI engines is the slower work that comes next: earning third-party mentions, deepening the entity, and answering more of the exact questions researchers ask. That's a multi-month effort, and it's exactly the ongoing, owner-approved work an autonomous agent is built to run. What this case study shows is how quickly the foundation can be repaired once an agent is pointed at it — and how much was silently lost while it was broken.
See your own before/after — free
Run FirePencil's free AEO audit and get your own baseline in about a minute: the real questions AI is asked about you, who it recommends instead, and the exact blockers holding your site back — the same first step that started IJARST at 0 and ended at 98.
§ Frequently asked questions
How long did the IJARST turnaround take?
Why couldn't Google Scholar index IJARST before?
Did this need a developer or a big project?
Are these results typical?
Case study data is drawn from FirePencil's own audit and monitoring records for IJARST (May–July 2026). Scores reflect FirePencil's AEO-readiness model; search metrics are from Google Search Console. Results describe one client's experience and are not a guarantee of any specific outcome. Third-party names (Google, Google Scholar, ChatGPT, Gemini, Perplexity, Claude) are trademarks of their respective owners; use is descriptive.