When someone asks ChatGPT for a recommendation, be the name it gives.

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Why ChatGPT recommends your competitor instead of you

When someone asks an assistant for a recommendation in your category, it answers from sources it has learned to trust. Reviews platforms, directories, forums, editorial coverage, and your own site. If those sources describe your competitor clearly and consistently, and describe you either inconsistently or not at all, the model names them. It is not a ranking you can appeal.

The work is rebuilding that evidence trail across the web — which is slow, unglamorous, and exactly what we do.

What is holding you back	What we do about it
AI crawlers blocked at the firewall	We open GPTBot, PerplexityBot, ClaudeBot and Google-Extended, and publish llms.txt
Your details differ across directories and profiles	Coherence work: same name, same claims, same numbers everywhere
No presence on the platforms models trust	We build genuine presence on the review sites, directories and forums in your sector
Nothing quotable on your own site	Citation blocks of 130–170 words answering the questions buyers actually ask
No editorial mentions anywhere	Digital PR and contribution programmes that earn real coverage
No idea what assistants currently say	A quarterly query panel with transcripts, so you can see it for yourself

Ask ChatGPT for a women’s travel group in India and Jugni is the first name it gives. That is a documented result, not a claim — and a Miami law firm ran the same playbook to 5× qualified leads.

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What’s included

How a brand gets into an AI’s answer.

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Where AI assistants actually get their answers

Every assistant blends two knowledge sources: what the model learned in training, which is slow-moving and favours brands with consistent web-wide presence, and what it retrieves live, which favours pages that rank and read cleanly. ChatGPT’s web mode and Perplexity lean on retrieval — which is why traditional SEO still matters — while their recommendations draw heavily on review platforms, directories, forums and editorial coverage.

Reddit and Quora punch far above their weight in training data. Our source-level work targets exactly this stack, weighted by how much each platform is trusted in your sector.

The consistency principle

Language models are pattern machines. When your business appears as the same name, with the same claims, the same specialisation and compatible numbers across fifty sources, the pattern strengthens and the model’s confidence in naming you rises. When your directory listing says one thing, your website another, and a 2021 profile a third, the pattern dissolves.

Much of LLM SEO is unglamorous coherence work. It is also why fake-review shortcuts backfire: models cross-reference at a scale humans do not.

What you actually receive each quarter

A documented panel of real buyer questions, run across ChatGPT, Perplexity, Gemini and AI Overviews, with screenshots and transcripts of what each said — before and after. Plus the source-level work log: which platforms were fixed, which mentions were earned, what changed on your site. When a model names you, you will have the receipt.

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How much of this you need depends on your market

LLM visibility is worth very different amounts depending on how your buyers decide.

Your situation	How we handle it
High-consideration services (legal, medical, financial)	Full programme — buyers research heavily and value per client is high
B2B and SaaS	Review platforms and comparison content carry most of the weight
Travel, education, hospitality	Recommendation-style queries dominate; forum and directory presence matters most
Local services	Profile, review and citation coherence delivers most of the benefit
Commodity retail	Rarely worth a standalone programme — we would point you elsewhere

We would rather scope you into the right one of these than sell the largest.

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How it works

The same playbook that got Jugni cited.

What you get, month by month

	What we do	What you see
Month 1	Crawler access, llms.txt, baseline query panel, entity coherence audit	A documented picture of what AI says about you today
Month 2	Source-level fixes across directories and platforms, citation blocks written	Your web-wide description starts agreeing with itself
Month 3	Mentions and contributions earned where models look	First movement on retrieval-driven surfaces
Months 4–6	Expansion, multilingual coverage where relevant, quarterly reporting	Citations appear and accumulate

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LLM SEO in India vs the USA

For US clients the platform stack is deep: Avvo, Healthgrades, Zillow, G2, Clutch — vertical authorities models cite by name — plus a mature podcast and digital-PR ecosystem. India’s stack is thinner, which cuts both ways: fewer authoritative sources to optimise, but each one you own carries more weight, and bilingual presence is an edge almost nobody exploits. Our Miami client’s eleven Spanish-language citations show how much room second-language AI visibility offers — the same logic applies to Hindi.

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What does it cost?

LLM SEO is ₹35,000/month ($699) standalone, or integrated in Scale plans — details on the pricing page. The one-time AI Visibility Audit at ₹20,000 ($399) is the right starting point: it establishes your baseline across every major assistant before you spend anything on optimisation.

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Who this works for — and who should not buy it

We would rather scope you out than take money for something that will not work.

Strong fit

• High-consideration purchases where buyers research for weeks
• Average client value that justifies owning the recommendation
• A real business with genuine credentials that can be corroborated
• Willingness to invest over quarters, not weeks

Poor fit

• You need enquiries this month — this is not that channel
• Your fundamentals are broken; fix SEO first
• You expect a guarantee of being named — nobody controls model outputs
• You are hoping to shortcut it with fake reviews or spun mentions

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Questions

Everything clients ask before starting.

How is LLM SEO different from AI SEO?

AI SEO targets Google’s AI features (AI Overviews) where your website’s structure matters most. LLM SEO targets conversational assistants like ChatGPT and Perplexity, where your reputation across the whole web matters most. They overlap and reinforce each other; serious brands eventually want both.

How do you measure something that happens inside ChatGPT?

By asking it — systematically. We run a fixed panel of realistic customer questions across the major assistants every quarter and document the answers. You see the actual transcripts, not a made-up ‘AI score’.

How long does it take to get cited?

Longer than a rankings change — models update their knowledge and retrieval sources on their own schedules. Perplexity and ChatGPT’s web-search modes can reflect improvements within weeks; deeper model knowledge takes months. We set that expectation honestly upfront.

Can you guarantee ChatGPT will recommend us?

No, and anyone promising specific citations is guessing on your budget. We control the inputs — crawler access, content structure, source presence, coherence — and we measure outputs quarterly with transcripts. Two named case studies show the method landing.

How long before we see our name in an AI answer?

Retrieval-driven surfaces like Perplexity and ChatGPT’s web mode can reflect improvements in 4–10 weeks. Training-data presence compounds over quarters. We set the expectation by market in the audit rather than after you have signed.

Does LLM SEO conflict with traditional SEO?

They reinforce each other. The structure, schema and authority that earn citations also improve rankings — Jugni gained 34% organic traffic as a side effect of citation work.

What happens when the models update?

Answers shift, like rankings do. Diversification is the hedge: presence across many trusted sources, in multiple languages, on both retrieval and training pathways. Single-source visibility is fragile; web-wide coherence survives updates.