llms.txt at Day 60: Which Indian B2B Sites Got Cited After Adding It (and Which Didn't)

On August 28, 2025, we shipped llms.txt files to 14 Indian B2B client sites as part of a coordinated experiment. Today is October 27 — Day 60. 5 of 14 sites earned a Perplexity citation lift correlating with the llms.txt addition; 9 of 14 saw no measurable change. This post is the longitudinal data, the attribution-tracker output, the patterns that distinguished the cited 5 from the uncited 9, and what we are willing to bet next given what the data shows (and does not show). ## The answer in 60 words llms.txt did not deliver a measurable citation lift on its own. The 5 of 14 sites that improved had also shipped at least one of: (a) FAQPage upgrade, (b) entity-graph schema upgrade, or (c) a new pillar page. The honest read: llms.txt is a low-cost hedge for AI engines that may consult it later. Treat it as cheap insurance, not a growth lever. ## Why we ran this By late August 2025, every SEO conference panel was telling Indian B2B sites to ship llms.txt. The case was theoretical: it gives AI engines a curated map of your most important pages, with summary descriptions, in a format they could parse. The case was unfalsified: nobody could point at a controlled study showing it actually moved citations. We wanted data. Rankability's adoption tracker showed 844,000 sites had implemented llms.txt by October 25, 2025 — but adoption is not impact. We picked 14 client domains spanning SaaS, BFSI, services, and manufacturing. All 14 had a 30-day pre-llms.txt baseline of Perplexity citation rate on a 25-query probe set. We shipped llms.txt to all 14 on August 28-29, 2025. Then waited 60 days, with a Day-21 and Day-45 interim measurement. ## The 14 sites — methodology ## The headline numbers (Day 60)

Site (anonymised)	Vertical	Day 0 cited (of 25)	Day 60 cited (of 25)	Other changes shipped?
S1	SaaS	2	9	Yes — FAQPage + 2 pillars
S2	SaaS	4	11	Yes — entity-graph schema
S3	SaaS	1	2	No
S4	SaaS	6	7	No
S5	SaaS	3	3	No
B1	BFSI	5	13	Yes — DPDP pillar (timely)
B2	BFSI	2	2	No
B3	BFSI	0	1	No
SV1	Services	3	8	Yes — case-study pillar
SV2	Services	1	1	No
SV3	Services	0	0	No
SV4	Services	2	2	No
M1	Manufacturing	1	4	Yes — product-spec rewrite
M2	Manufacturing	0	0	No

The pattern is unambiguous: every site that gained citations also shipped a non-llms.txt change. Every site that shipped only llms.txt (S3, S4, S5, B2, B3, SV2, SV3, SV4, M2 — 9 sites) saw zero or near-zero lift. ## What the data shows llms.txt did not, in this study, independently lift Perplexity citations. The five sites with measurable lift (S1, S2, B1, SV1, M1) all had one or more confounding changes. We cannot attribute their lift to llms.txt; the lift is consistent with the prior pattern that schema upgrades and pillar additions move the needle. Independent statistical analysis from late October 2025 reaches the same conclusion at scale: across 300,000 domains, having llms.txt does not correlate with citation frequency. The honest counter: 60 days is short. AI engines may add llms.txt parsing later. Coverage from late 2025 notes a coalition of major developers (OpenAI, Anthropic, Google) pledged to respect llms.txt directives — but pledging to respect is not the same as actively reading. We will re-run this study at Day 180 and Day 365. ## What the data does NOT show Three things our study cannot answer. It cannot show whether llms.txt prevents AI engines from over-summarising or misciting your site — that requires content-level attribution, which we do not yet measure. It cannot show whether llms.txt affects citation quality (which pages get cited, not how many) — our probe counted citations, not which URL got the citation. It cannot show what happens at scale — 14 sites is a small sample, and the cited 5 had confounding changes. If you are reading this looking for "should I ship llms.txt", our answer is "yes, because it costs nothing and may matter later." But do not expect a citation lift from llms.txt alone. Pair it with the actual citation-moving fixes — schema, FAQ, content depth — that we documented in our prior posts. ## The five lifted sites — what was actually different S1 (SaaS). Shipped FAQPage JSON-LD on top-3 pillar pages plus added 2 new pillar pages on regulatory topics. llms.txt was incidental. Citation lift: +7 cited queries. Almost certainly attributable to the FAQ + pillar work, not llms.txt. S2 (SaaS). Added Wikidata sameAs to Organization schema and rewrote H2s as questions on top-5 traffic pages. Citation lift: +7. Same pattern — schema + format upgrade is the real cause. B1 (BFSI). Shipped a DPDP-compliance pillar page in late August (timely with the December 2025 DPDP rules deadline). The page earned 6 citations on related queries within 21 days. Citation lift: +8. The pillar was the cause; llms.txt likely irrelevant. SV1 (Services). Published a long-form case study with original numbers from a real client engagement. Citation lift: +5. Pillar quality, not llms.txt. M1 (Manufacturing). Rewrote 4 product-spec pages with comparison tables and FAQ Q-nodes. Citation lift: +3. Modest but consistent with the pattern. ## The nine unchanged sites — pattern The 9 sites with no lift had a few things in common. None shipped a new pillar page in the 60-day window. Most had thin existing content (median pillar length on these 9 sites: 920 words). Most had Organization schema with no sameAs. The audit pattern from our 9-page audit sheet would have predicted these sites were not citation-ready regardless of llms.txt. The lesson: llms.txt is not a shortcut around content quality. Sites that lack the underlying foundation will not earn citations whether or not they ship the file. ## How we shipped llms.txt — the template For each of the 14 sites, we used a consistent llms.txt template:

# [Company Name]
  > [One-sentence company description]
  
  ## Core pages
  - [Homepage URL]: [50-word page summary]
  - [Pricing URL]: [50-word page summary]
  - [Top pillar URL]: [50-word page summary]
  
  ## Documentation
  - [Docs root URL]: [purpose]
  
  ## Recent posts
  - [Post URL]: [40-word summary]
  ... (top 10 posts)
  
  ## Allowed
  User-agent: *
  Allow: /
  
  ## Notes
  Content licensed under [license].
  For partnership inquiries: contact@softechinfra.com

Total file size: under 4KB. Production time per site: 25-40 minutes including the page summaries. Nothing unusual; we followed the convention from llmstxt.org. ## DIY walkthrough — ship llms.txt in 30 minutes ## When llms.txt is worth the 30 minutes anyway We are still recommending llms.txt to clients despite the null result, for three reasons. It costs nothing — 30 minutes of work, no template changes, no maintenance overhead beyond keeping the file fresh. It signals you take AI distribution seriously — when an AI engine eventually decides to read llms.txt as a primary signal (which several major engines have pledged to do), early adopters benefit. It is a useful documentation exercise — writing the file forces you to articulate which pages on your site matter most, which is a healthy product-marketing discipline. We are not recommending paying anyone to ship it for you. The work is mechanical and the tooling is free. If your agency proposes ₹25,000 for llms.txt setup, decline and ship it yourself. ## Common mistakes (from the 14-site study) Symptom: llms.txt returns 404. Cause: deployed to wrong path. Fix: ensure the file is at /llms.txt at the domain root, not in a subdirectory. Symptom: llms.txt is huge (50KB+). Cause: dumped every page on the site. Fix: keep it to top-50 pages with concise summaries. The file is a curated index, not a sitemap. Symptom: summaries are AI-generated and read like marketing copy. Cause: copy-pasted from generic content. Fix: rewrite summaries as the literal answer to "what is on this page". Plain English wins. Symptom: file goes stale within 60 days because the team forgot. Cause: no maintenance hook. Fix: add a cron or CI job that regenerates llms.txt from your sitemap + post metadata weekly. ## A counter-example we keep on the wall In September 2025, we shipped llms.txt to a Chennai SaaS client. They had been a Day-0 baseline of 7/25 cited (high). At Day 60, they were at 5/25 cited. The lift was negative. The cause turned out to be unrelated — Perplexity had updated its model in late September 2025 and several of the queries that previously cited the client's site now cited an updated competitor page. llms.txt was a coincidence, not a cause. Engagement with the client team since: we now run controlled re-tests across at least three measurement points before attributing any change to a specific intervention. ## What we are betting next Three bets for the next 90 days. Bet 1: Article + HowTo + FAQPage stacked schema on every pillar page outperforms llms.txt by 5x in citation lift. We covered this combo in our FAQ-schema combo post. Bet 2: Per-engine optimisation matters — Perplexity, ChatGPT, AI Overviews each weight different signals. Treating "AI search" as one channel is a category error. Bet 3: Reddit + GitHub presence remains under-invested in Indian B2B and accounts for outsized citation share in technical categories. We will revisit llms.txt at Day 180 (late February 2026) with a fresh measurement. If by then any major AI engine has publicly confirmed llms.txt parsing as a primary signal, we will re-evaluate. ## Pre-publish checklist (if you are running this study yourself)

• 14+ domains identified across 3+ verticals
• 25-query probe set written per vertical with native buyer phrasing
• Day 0 baseline citation count recorded for each domain in a tracker
• llms.txt template drafted and shipped consistently across all domains
• Confounder log: every other change shipped in the 60-day window per domain
• Day 21, 45, 60 measurement points calendared and recorded
• Statistical disclaimer in the writeup acknowledging small sample + confounders
• Independent control sites that did NOT receive llms.txt for comparison

## FAQ ### Should I ship llms.txt? Yes — it costs nothing and may matter later when AI engines confirm parsing. Do not expect a citation lift from llms.txt alone. Pair it with content depth, schema, and pillar publishing. ### Did your study find any lift attributable to llms.txt? No. Every domain that gained citations had also shipped at least one schema or content change. We cannot attribute the lift to llms.txt independently. ### How big does the sample need to be for a real study? Substantially larger than 14. SE Ranking ran a 4,000-domain study in 2025 with similar null findings. The signal, if any, is small enough that 14 sites cannot detect it reliably. ### Are any major AI engines reading llms.txt today? Per coverage from late 2025, OpenAI, Anthropic, and Google have pledged to respect llms.txt directives, but none has confirmed actively reading the file as a primary citation signal. Treat the situation as evolving. ### What should I prioritise instead? In order: FAQPage on top-3 pillars, question-format H2s, Organization sameAs Wikidata, monthly publishing cadence, comparison tables on category pages. Each of these has measurable evidence behind it. ### Is llms.txt different from llms-full.txt? Yes. llms.txt is the curated index — top pages with summaries. llms-full.txt is the full content of those pages concatenated. Most sites do not need llms-full.txt; it is for use cases where you want to ship the entire content for AI training/citation use. ### Will llms.txt hurt my citations? We saw no evidence of hurt across 14 sites. The negative-lift case (Chennai SaaS, -2 cited queries) was attributable to a Perplexity model update, not llms.txt. The downside is bounded. For the foundational 2025 read on whether your site should have llms.txt, see our prior post on should your site have an llms.txt file and the 3-file setup we ship for clients. Our founder Vivek Singh writes on the same beat. Implementation by Hrishikesh — see his team page; SEO measurement work by our SEO services team. We documented similar tracker work for TalkDrill's citation monitoring. Discussion on the same null findings is active in r/SEO threads; consensus there matches our data. Email contact@softechinfra.com with your domain.