LLMO agency · Large language model optimization · US, UK, Canada, Australia
LLMO Agency: Digital Magma’s LLM Optimization for How AI Describes Your Brand
Digital Magma is an LLMO agency in Noida, India. We check what ChatGPT, Gemini, Claude, Perplexity and Copilot say about your business, correct the sources behind wrong or missing answers, and make your brand facts consistent everywhere large language models look.
- Rated 5.0 on Google (100+ reviews)
- Founder-led
- Works from official crawler documentation
- No promises about training data
Discuss your project
Tell us your goals, required services and budget. Agency engagements start at ₹40,000; scope, deliverables and fees are agreed before work begins.
Scope and fees agreed firstYour details stay private
- 10+ yearsof SEO and digital marketing
- 100+ projectsacross 10+ industries
- 6 countrieswith clients, including the USA
- 500+ keywordson Google page one, 100+ at #1
- 5.0★ Googlerated in 100+ Google reviews
LLMO agency: what large language model optimization means
An LLMO agency works on what large language models know and say about your brand. LLMO, or large language model optimization, covers the facts AI assistants repeat about you, whether they recommend you in your category, and the sources they draw on, from training data to live web search. Digital Magma’s LLMO agency audits those answers, fixes the sources behind them and tracks accuracy every month.
The short version
The brand is the unit of work
Large language model optimization is judged brand by brand, not page by page: does an assistant describe your company correctly, include it when someone asks for options in your category, and frame it fairly? The model’s answer is built from everything it has read about you, much of it on sites you don’t own.
So Digital Magma’s LLMO work spans your own pages, your profiles and the third-party sites that talk about you. An old press release with last year’s prices can surface in a chat answer as easily as your home page.
LLMO is a practitioner term. Marketers started using it once buyers began asking chat assistants for recommendations, and there is no formal academic definition. GEO is different: it was named in a 2023 research paper by Aggarwal and colleagues. The names overlap, and the section below shows where they split.
Where it fits
LLMO agency vs GEO, AEO and AI SEO: the brand-knowledge layer
An LLMO agency asks one question the other disciplines don’t put first: what do language models believe about this brand, and is it true? The quickest way to see the difference is to compare the question each one is trying to answer.
| Discipline | The question it asks | Typical work | Where to read more |
|---|---|---|---|
| AI SEO | How does the whole programme win visibility across AI search? | The umbrella: technical, content, entity, mentions and measurement together | AI SEO services |
| AEO | Is our page the answer that gets extracted and quoted? | Question research, answer-first writing, snippets, voice | AEO agency |
| GEO | How often are we cited across generative engines, compared with rivals? | Citation-share research and content changes tested against it | Covered inside our AI SEO programme |
| LLMO | What do models believe and say about us, and is it right? | Brand audits, source correction, entity facts, crawler policy | This page |
LLMO vs GEO
The two meet on live retrieval, where an assistant searches the web before answering. LLMO adds the training-data side, factual accuracy about the brand and how the brand is framed against competitors.
LLMO vs SEO
SEO ranks pages in search results. LLMO depends on it, because assistants that search can only read pages that are crawled and indexed. Weak SEO usually means a thin picture of your brand in AI answers.
LLMO vs AEO
AEO shapes individual answer passages on your pages. LLMO looks at the brand story across every source. A perfect answer box won’t help if the assistant thinks you closed in 2023.
Our opinion, from running these audits: if AI tools get your basic facts wrong, fix that before you chase more citations. More mentions of an incorrect story only spread the error further.
How models learn
LLMO explained: the two ways a language model learns about your brand
LLMO works on two pathways. A model learns some things during training, before it is released, and it can look other things up through live web search when someone asks a question. Each pathway has different crawlers, different timescales and different fixes.
Pathway 1: training data
- Public web: pages, articles, profiles and forums that crawlers can reach
- Training run: AI companies collect text and train the model on it
- Knowledge cutoff: the model’s built-in knowledge stops at a date
- Answer without search: the assistant replies from memory, with no citations
Crawlers tied to this use include GPTBot (OpenAI) and ClaudeBot (Anthropic), and the Google-Extended token controls whether Google may use your content for future Gemini models. Nobody can edit a model that has already been trained. You can only influence what later training runs may see.
Pathway 2: live retrieval
- Public web: the same pages, crawled into a search index
- Search at answer time: the assistant runs queries when it needs current facts
- Grounding: it reads the pages that come back and writes from them
- Answer with citations: links point to the sources it used
This is retrieval-augmented generation in plain terms. ChatGPT search, Perplexity, Microsoft Copilot, Gemini and Claude can all work this way. OAI-SearchBot, Claude-SearchBot, PerplexityBot, Bingbot and Googlebot are the crawlers that feed it.
A third route: user-initiated fetches
When a user asks an assistant to open a page, a separate agent visits it: ChatGPT-User, Claude-User or Perplexity-User. OpenAI says robots.txt rules may not apply to ChatGPT-User, and Perplexity says its user fetcher generally ignores robots.txt. Anthropic says all its bots honour robots.txt.
Why both pathways matter
The same assistant can give two different answers about you. With web search off, it answers from training and may repeat something years old. With search on, it can correct itself from your current pages, if those pages are crawlable and clear.
Full scope
LLMO services from Digital Magma: what’s included
Digital Magma’s LLMO services sit in six groups. Every item produces something you can check: a log entry, a changed page, a corrected listing or a number in the monthly report.
Brand audit
What assistants say about you today.
- Five-assistant fact checkChatGPT, Gemini, Claude, Perplexity and Copilot, each with and without web search
- Category prompts“Best X for Y” questions where you should appear
- Competitor framingHow rivals are described next to you, and why
- Misattribution checkFacts borrowed from a similarly named company
Source correction
Fix what the models read.
- Facts pageOne public page with your confirmed company facts
- About and service pagesRewritten where they are vague, dated or inconsistent
- Outdated pagesUpdated, merged or redirected so old claims stop circulating
- Third-party correctionsRequests to publishers, directories and profile sites, logged with status
Entity facts
One identity, stated the same way everywhere.
- Canonical fact sheetName format, address, founding year, leadership, services
- Organization schemaPlus Person and Service markup with sameAs links
- Profile consistencyGoogle Business Profile, Bing Places, LinkedIn, industry directories
- Name disambiguationClear wording when another business shares your name
Crawler policy
Decide who may read what.
- robots.txt reviewTraining bots and search bots handled separately
- CDN and firewall checkBot rules that block crawlers without anyone noticing
- Written AI crawler policyOptions laid out, you approve the final choice
- Change logEvery rule change dated, with the reason
Content and mentions
Tie your brand to your category.
- Category definition pagesPlain statements of what you are and who you serve
- Comparison pagesHonest “you vs them” and alternatives content
- Digital PRBrand-plus-category mentions in publications your buyers read
- Content writingHuman-written pages, checked against the fact sheet
Measurement
Numbers that show whether answers improved.
- Mention rateHow often you appear across the prompt set
- Accuracy scoreFacts correct divided by facts checked
- Sentiment and share of voiceTone of answers and your share against named rivals
- First-party dataBing AI Performance report and GA4 referrals from AI tools
Seen something wrong in an AI answer about you?
Send us the prompt and the answer, and we’ll tell you where it most likely came from.
Process
LLMO agency process: 6 steps at Digital Magma
The LLMO agency process at Digital Magma runs in six steps, and the first one involves you directly, because nobody outside your company can confirm which facts are true.
Confirm the fact sheet
You approve the canonical facts: legal and trading name, founding year, locations, leadership, services, and only the prices you already publish.
Run the brand audit
An agreed prompt list goes through each assistant several times, with web search on and off. Errors, omissions and competitors named are logged.
Trace the sources
For cited answers we record the URLs. For uncited ones we search the claim to find its likely origin, labelled as likely, never as proven.
Correct and consolidate
Your own pages and profiles are fixed first, then third parties are asked to correct theirs, and schema with sameAs links goes live.
Strengthen the signal
Content and earned mentions connect your brand to your category in the places assistants already read.
Re-test and report
The same prompts are re-run monthly, and you get accuracy, mention rate, sentiment and share of voice, with the raw answers attached.
Brand audit
LLMO brand audit: what ChatGPT, Gemini, Claude, Perplexity and Copilot say about you
An LLMO brand audit records what each major assistant says when someone asks about your company or your category, then scores every answer for presence, accuracy and tone. Digital Magma uses it to map your brand narrative as the models tell it.
Six prompt families, in your buyers’ words:
- Identity: “What is [brand]?” and “Who owns [brand]?”
- Trust: “Is [brand] legit?” and “[brand] reviews”
- Commercial: “[brand] pricing” and “Does [brand] offer [service]?”
- Category: “Best [category] for [use case] in [market]”
- Comparison: “[brand] vs [competitor]” and “alternatives to [competitor]”
- Problem: the questions a buyer asks before they know any brand names
Each prompt runs with web search on and off where the assistant allows it, because the two settings draw on different pathways. An answer that is wrong only with search off points to old training data. Wrong with search on points to a live page you can find and fix.
A brand audit on its own is a custom quote, because prompt count and markets vary. A technical baseline is our SEO audit at USD 500 one-time, and the Discuss Your Project page shows what that covers.
Corrections
LLMO fixes for wrong AI answers: correcting the sources models read
LLMO can’t edit ChatGPT or Gemini directly, and nobody outside those companies can. What an agency can do is correct the material the models read and give them one clear, current source that beats the stale ones.
The correction workflow
- 1. Isolate the claim. Write down the exact wrong sentence, the assistant, the date and whether search was on.
- 2. Find the source. Cited URL if there is one. If not, search the claim word for word and note the likely origin.
- 3. Fix your own pages first. Old blog posts, PDFs and forgotten landing pages are often the culprit.
- 4. Ask third parties to correct. Directories, publishers and review platforms, with evidence attached.
- 5. Publish a clear facts page. The current version, in plain text, linked from your About page.
- 6. Request re-crawling. URL Inspection in Google Search Console, and IndexNow for Bing.
- 7. Re-test. Same prompt, same settings, logged next to the original.
Illustrative example
Not a client case. A typical pattern we look for.
A software company dropped its free plan a year ago. With search on, an assistant still says “offers a free plan”. The cited source is a comparison article on a review site that was never updated. The company’s own pricing page is correct, but an old help-centre article still mentions the free plan too.
The fix: update the help article, ask the review site to correct its listing, add a dated line to the pricing page, request re-crawling and re-test monthly. With search off, the old claim may persist until a newer model is trained.
Why re-testing is part of the job
OpenAI’s own ChatGPT search help article warns that citations can be incomplete, outdated or incorrect. Assistants also vary between runs. One clean answer is not proof of a fix. Some errors are AI hallucinations with no source at all, and for those the best fix available is a clear, consistent statement of the fact everywhere you control.
Legal and safety issues
Defamatory or dangerous claims go to the platform’s own reporting channel and your lawyer. We document the evidence and help clean up sources through our online reputation management team, without promising removal.
Entity work
LLMO entity work: one consistent set of brand facts everywhere
LLMO entity work makes sure every source describes your business the same way, so a model meets one story instead of five. In search terms, your brand is an entity, and consistency is how machines connect the mentions into one node in a knowledge graph.
- Canonical fact sheet: the same name format, address, founding year and service names on every page, profile and press release. “Ltd” on one site and “Inc.” on another can be enough to split a brand in two.
- Schema that matches the page: Organization schema for the company, Person for the founder, Service for what you sell, and sameAs links to official profiles. Google says structured data must match the visible text.
- Profiles: Google Business Profile, Bing Places, LinkedIn, industry directories and review platforms. Bing’s February 2026 webmaster post names Bing Places as the way to keep address, hours and contact details current for AI answers.
- Text, images and video in agreement: the same Bing post advises aligning all three so they represent the same entities and products.
- Name collisions: when another company shares your name, the About page and the schema say plainly which one you are, with location and founding year.
Crawler policy
LLMO and AI crawlers: training bots vs search bots
LLMO crawler policy starts from one fact that many sites miss: the major AI companies run separate bots for training and for search. Every row in this table comes from each company’s own documentation, checked in October 2026.
| Company | Training | Search and retrieval | User-initiated | What the docs say |
|---|---|---|---|---|
| OpenAI | GPTBot | OAI-SearchBot (ChatGPT search) | ChatGPT-User | Settings are independent. Robots.txt changes take about 24 hours to reach search. Robots.txt rules may not apply to ChatGPT-User. OpenAI crawler docs |
| Anthropic | ClaudeBot | Claude-SearchBot | Claude-User | All three honour robots.txt, and Anthropic supports the Crawl-delay extension. Anthropic help article |
| Google-Extended token | Googlebot (Search, including AI Overviews and AI Mode) | None listed here | Google-Extended covers future Gemini training and grounding in Gemini Apps and Vertex AI. It does not affect inclusion or ranking in Google Search. Google crawler docs | |
| Perplexity | None (PerplexityBot is not used for foundation models) | PerplexityBot | Perplexity-User | Changes can take up to 24 hours. The user fetcher generally ignores robots.txt. Perplexity crawler docs |
| Microsoft | Not covered here | Bingbot (Bing and Copilot) | Not covered here | Bing says it respects robots.txt and other supported controls. Bing Webmaster Blog |
To appear in AI search answers
Allow the search crawlers: OAI-SearchBot, Claude-SearchBot, PerplexityBot, Bingbot and Googlebot. Then check your CDN and firewall, because a bot rule there can block them even when robots.txt says yes.
Training crawlers are a business choice
Blocking GPTBot, ClaudeBot or Google-Extended is a decision about licensing, competition and your content’s value. It has trade-offs either way. We write the options down, and you decide.
Crawler names and rules change, so Digital Magma re-reads the official pages before every policy review and dates the version it relied on.
llms.txt
LLMO and llms.txt: what it is, who reads it and whether you need one
For LLMO, llms.txt is an optional extra: a proposed markdown file at your site root that gives AI agents a short, curated map of your key pages. It helps some agents, costs little to publish and has no proven effect on whether assistants recommend you.
The proposal comes from Jeremy Howard. It was first published on 3 September 2024, and version 2 was updated on 10 August 2026. Version 2 also suggests offering clean markdown copies of important pages at the same URL with .md added.
The author reports that thousands of sites publish one, that Chrome’s Lighthouse checks for it in agentic browsing audits, and that OpenAI, Anthropic and Gemini publish llms.txt files for their developer docs. Those are his statements, passed on as his.
Google’s guidance for AI Overviews and AI Mode says you don’t need new machine-readable files, AI text files or special markup to appear in them.
On-site content
LLM optimization content: pages a language model can understand and repeat
LLM optimization content states plainly what your company is, who it serves and what it costs, in words a model can repeat without guessing. How to write each answer passage is AEO work, covered on our AEO agency page.
A plain category statement
Near the top of your home, About and main service pages: “[Brand] is a [category] for [audience] in [market].” If a stranger can’t name your category after one sentence, a model may not either.
A facts page
Founding year, locations, leadership, what you do and don’t offer, and the prices you publish. Dated, linked from the footer, written as text rather than a graphic.
Honest comparison pages
“[Brand] vs [competitor]” and alternatives pages that say who each option suits. Assistants answer comparison prompts constantly, and a fair page gives them something to work from.
One name per product
If the same service is “Growth Plan” on one page, “Pro tier” on another and “Business package” in a PDF, a model has three things to reconcile. Pick one name and use it everywhere.
Specific, sourced evidence
Numbers with a named source beat adjectives. The 2023 GEO study by Aggarwal and colleagues found that adding citations, quotations and statistics improved visibility in generative answers.
Off-site
LLM SEO off-site: mentions in the sources language models rely on
LLM SEO off-site work earns independent mentions of your brand next to your category, in the publications, lists and communities that assistants read. An assistant with search usually summarises several sources at once, so what others say about you carries real weight.
- Digital PR: stories, data and expert comment that put your brand name beside the category phrase buyers use. Our digital PR services team runs this.
- Expert commentary and podcasts: your specialists quoted on the topics you sell.
- Industry lists and directories: accurate entries on the lists your market actually consults.
- Review platforms: real customer reviews, answered politely, on the platforms your country uses.
- Communities: real participation by named people from your team, where it’s welcome.
Unlinked brand mentions still count. A link helps rankings, but a model reading an article picks up the association between your name and your category either way, which is why Digital Magma tracks LLM SEO mentions with or without links.
Every month
LLMO agency deliverables: what you get every month
Each month, Digital Magma’s LLMO agency team sends a fixed set of deliverables in a shared tracker, so every answer change traces back to the work behind it.
- Brand-answer report: prompt, assistant, search on or off, mentioned or not, accuracy, sentiment, competitors named and sources cited
- Error log with status: found, source fixed, re-tested, resolved
- Fact-sheet and schema changes: what changed, where and why
- Crawler-policy log: robots.txt and firewall changes with dates
- Mentions earned: new coverage, listings and profile corrections
- First-party summary: Bing AI Performance and GA4 referrals from AI tools
- Next month’s plan and a review call booked in your time zone
To see what a good AI search report should contain, read our guide to AI SEO agency reporting and deliverables.
Measurement
LLMO measurement: mention rate, accuracy, sentiment and share of voice
LLMO measurement uses four answer metrics plus first-party data. Each metric is defined here, so you can check our maths and compare it with anyone else’s report.
Mention rate
Prompts where your brand appears, divided by prompts run, tracked separately for identity prompts and category prompts.
Accuracy score
Facts stated correctly, divided by facts checked against your approved sheet. This is our own metric, and it is the one we watch first. A brand mentioned often but wrongly is worse off than one mentioned less.
Sentiment and framing
Each answer marked positive, neutral or negative, with the quote saved and framing words such as “budget” or “new” noted.
Share of voice
Your mentions as a share of all brand mentions on category prompts, compared with the competitors you name at the start.
First-party data
Bing Webmaster Tools’ AI Performance report shows citations, cited pages and grounding queries for Copilot and Bing AI answers. Google Search Console shows branded queries. GA4 shows sessions from chatgpt.com, perplexity.ai, copilot.microsoft.com, gemini.google.com and claude.ai.
The honest limit
Answers vary between runs, accounts and locations, so we report ranges from repeated runs. GA4 referrals undercount too, because some apps don’t pass a referrer.
Brand-visibility foundations
LLMO foundations: three Digital Magma case studies
LLMO builds on the brand footprint a company already has, and these three anonymised Digital Magma projects show that footprint being built. They are SEO and brand-visibility results. We don’t claim AI-mention figures for them.
Mobile app development company
A wider footprint across markets
- Starting point
- Little organic presence outside its home market, despite selling into six countries.
- Work
- International SEO, country-specific content and steady brand building on LinkedIn.
- Result
- 500+ keywords on page one, 100+ at #1, and LinkedIn followers up from 4K to 28K. We present this as SEO and brand growth, not as an LLMO result.
Tele-oncology startup
A clear entity from day one
- Starting point
- A brand-new company in a field where wrong information carries real risk.
- Work
- Site built for search from scratch, moving from React to Next.js so every page ships readable HTML, with consistent company facts from launch.
- Result
- SEO content planning and service-page optimization
Software development company
Brand tied to a category phrase
- Starting point
- A crowded “top companies” search where the brand didn’t appear.
- Work
- Nine posts on searches that were only beginning to trend.
- Result
- Position one for “top software development companies in noida”, the kind of brand-plus-category association LLMO work tries to build.
Client names are withheld. All three programmes were led by founder Ankit Sachan. More projects are on the case studies page.
Industries
LLMO services by industry: where AI answers shape trust
LLMO services matter most where a wrong AI answer costs money or trust. The facts that go wrong differ by sector, so Digital Magma builds each prompt list around them.
SaaS
Pricing, plan names, integrations and “X vs Y” prompts, where assistants often repeat retired tiers. SaaS marketing
Healthcare
Wrong claims about treatments, qualifications or locations are high-risk, so named reviewers and accurate clinic details come first. Healthcare marketing
Ecommerce
Product specs, shipping countries and return policies. A wrong “doesn’t ship to Canada” costs Canadian orders. Ecommerce marketing
Education
Accreditation, course fees, intake dates and eligibility. Education marketing
Real estate
Developer names, project status and locations, often confused between similarly named projects. Real estate marketing
Local services
Hours, address and service area, drawn largely from Google Business Profile and Bing Places. Local business marketing
Working together
LLMO agency for US, UK, Canadian and Australian brands and agencies
Digital Magma runs its LLMO agency work remotely from Noida, India, Monday to Saturday, 9 am to 9 pm IST. For US, UK, Canadian and Australian clients, that gives a predictable window for calls and a full working day for audits.
Hours that overlap
Our day covers US Eastern mornings until 11:30 am (10:30 am after US clocks change on 1 November), most of the UK working day and Australian Eastern afternoons from 2:30 pm. West Coast clients get one fixed slot.
Prompts for your market
Buyers in each country phrase questions differently and trust different review sites. Prompt lists, spelling and source checks follow your market, which is how we run LLM SEO services for USA clients and the rest.
For agencies
White-label LLMO audits and reports under your brand, unbranded or partner-branded, with an NDA on request. We have no contact with your client unless you ask for it, and you set your own price.
You keep Admin access to your own Search Console, Bing Webmaster Tools and GA4 accounts throughout. Digital Magma has one office, in Sector 137, Noida, and serves other countries remotely. Country pages: USA · UK · Canada · Australia.
Pricing
LLMO pricing: what an LLM optimization agency costs
LLMO pricing at Digital Magma is a custom quote for both audits and monthly programmes, because the scope depends on how many assistants, markets and errors are involved. The SEO work that supports it has published prices.
SEO audit
A technical baseline under your brand facts.
USD 500 one-time
- Crawl, index and bot-access check
- Schema and profile review
- Prioritised fix list
Ongoing SEO
The search base that retrieval reads from.
From USD 600 /month
- Technical and on-page SEO
- Content and internal links
- Monthly report and call
LLMO programme
Brand audit to monthly re-tests.
Custom quote
- Fact sheet and brand audit
- Source correction and entity work
- Monthly accuracy reporting
Prices are plus applicable taxes. Clients billed in India see rupee pricing, where the SEO audit is ₹40,000 plus GST. Full plans are on the SEO packages page.
What drives LLM optimization cost
- Number of assistants and markets tested
- Size of the prompt set and runs per prompt
- How many errors the audit finds
- Profile and directory clean-up needed
- Digital PR scope for new mentions
- Expert review in regulated fields such as health or finance
A note on market rates
We found no independent survey of LLMO pricing. The ranges online come from agencies and vendors pricing their own services, so Digital Magma gives a scoped quote after the brand-answer check instead of repeating them.
How your service quote is scoped
₹40,000 is our minimum paid engagement, not a fixed price for every service or a promise that every deliverable is included. For this service, the quote depends on website size, technical complexity, locations and languages, content production, authority work and SEO or AI-search reporting.
Recurring work is quoted per month; a standalone audit, build or production project is quoted separately. Your proposal confirms deliverables, quantities, timelines, revisions, applicable taxes and third-party costs before work begins. Larger programmes are custom quoted with no upper price limit.
Options
LLMO agency vs in-house team vs monitoring software
An LLMO agency audits answers and fixes the sources behind them. Monitoring software spots problems but fixes nothing, and an in-house marketer can do the work if they have the time and access.
| Option | Audits answers | Fixes sources | Entity and profiles | Crawler policy | Cost type | Best for |
|---|---|---|---|---|---|---|
| LLMO agency | Yes, by hand, search on and off | Yes | Yes | Yes, with your approval | Audit plus monthly fee | Brands with errors to fix and no spare specialist |
| In-house marketer | If trained | Yes, if they have access | Often | Needs a developer | Salary | Companies with a dedicated brand or SEO lead |
| Monitoring software only | Automated sample | No | No | No | Subscription | Teams that already have people to act on alerts |
| Do nothing | No | No | No | No | Free today | Brands with almost no AI-search exposure yet |
Software can tell you an assistant called your product discontinued. Someone still has to find the page that says so and get it changed.
Buyer’s guide
LLMO agency checklist: how to choose one and the red flags
A good LLMO agency starts from facts you confirm, cites official crawler documentation and shows you raw answer logs. Use this list on every provider, Digital Magma included, and to judge how to choose an LLM SEO agency in general.
Green flags
- Asks you to approve a fact sheet before auditing
- Separates training and retrieval in its reports
- Cites the vendors’ own crawler pages
- Shows full answer text, with dates and settings
- Defines accuracy in writing
- Explains what you keep if you cancel
Red flags
- “We can get you into ChatGPT’s training data”
- “We submit your brand to OpenAI”
- Writing your Wikipedia page for a fee
- Fake reviews or forum posts
- Guaranteed AI recommendations
- Blocking every AI bot by default without discussing the trade-off
Questions to ask any LLM optimization agency
- Which assistants do you test, with and without web search?
- How many runs per prompt, and how do you report variation?
- How do you define and calculate accuracy?
- What will you change in month one?
- What do I own if I cancel?
What to expect
LLMO results timeline: retrieval moves in weeks, training is out of anyone’s control
LLMO results arrive at two speeds. Answers that use live search can change within weeks of a corrected source being re-crawled, while built-in model knowledge changes only when a company trains and releases a new model.
Facts, audit and access
Fact sheet approved, brand audit logged, crawler issues fixed, first corrections made. OpenAI says robots.txt changes reach its search systems in about 24 hours, and Perplexity says up to 24 hours.
Sources re-indexed
Corrected pages picked up by Google and Bing, answers with search on begin to reflect them, and new mentions start to appear.
Steady re-testing
Accuracy and share of voice tracked over time, remaining third-party errors chased, new prompts added as your offer changes.
AI companies don’t publish when future models will be trained or on what data, so no agency can schedule a change in built-in knowledge.
Myths and limits
LLMO myths and limits: what no agency can promise
LLMO attracts confident claims that don’t survive a read of the official documentation. Digital Magma hears these five most often.
“An llms.txt file makes ChatGPT recommend you”
It is an optional proposal. Google says AI Overviews and AI Mode don’t need it, and nobody has shown it changes recommendations.
“Blocking GPTBot removes you from ChatGPT”
OpenAI treats GPTBot (training) and OAI-SearchBot (search) as separate settings. Blocking one leaves the other untouched.
“Google-Extended hurts your rankings”
Google says Google-Extended does not affect inclusion in Google Search and is not a ranking signal.
“LLMO replaces SEO”
Retrieval runs on search indexes. A page that isn’t crawled and indexed can’t be retrieved, so SEO stays underneath everything.
“We can fix ChatGPT’s memory”
Nobody outside the AI company can edit a trained model. Corrections work through sources and future training, not a request form.
Limits that apply to everyone
No one controls model outputs. Answers vary by run, account and location. Citations can be wrong, as OpenAI itself notes. Results differ by market.
When not to hire an LLMO agency yet
- Your brand has almost no web footprint. There is little for models to read. Start with SEO services and PR.
- No one can approve facts. Without a confirmed fact sheet, there is nothing to correct against.
- The budget only covers monitoring. Reports that nobody acts on change nothing.
What we suggest instead
Build the base first: an indexed site, consistent profiles and a few earned mentions. B2B teams can use our post on hiring an AI SEO agency for B2B leads to weigh the decision.
People
LLMO expertise at Digital Magma: founder, related services and reviews
LLMO work at Digital Magma is led by founder Ankit Sachan, whose background is technical SEO, schema and keeping brand facts consistent across the web.
Ankit Sachan
Founder & Head of SEO, Digital Magma
Ankit has worked in digital marketing since 2016 and founded Digital Magma in 2020. He is certified in Google Ads, GA4 and Google Business Profile. His focus includes technical SEO, international SEO and AI search visibility through AEO and GEO.
His work covers technical and international SEO, schema and entity consistency, and how brands appear in AI assistants. Digital Magma keeps its own llms.txt and schema consistent with the facts on this site, which you can check for yourself.
“What stood out most was their communication and transparency. They provide regular updates and explain performance in simple terms.”
“They explain strategies clearly and focus on long-term business growth instead of short-term promises.”
Read all 100+ reviews on Google
Related services
FAQs
LLMO agency FAQs
LLMO agency questions from founders, marketing leads and agency owners, answered by Digital Magma in plain terms.
What does an LLMO agency do?
An LLMO agency checks what AI assistants say about your brand, finds and corrects the sources behind wrong or missing answers, makes your company facts consistent, sets an AI crawler policy and re-tests monthly. Digital Magma reports accuracy, mention rate, sentiment and share of voice.
What does LLMO stand for?
LLMO stands for large language model optimization, sometimes written LLM optimization or called LLM SEO. It means improving what models such as ChatGPT, Gemini, Claude and Perplexity know and say about a brand. It is a practitioner term, with no formal academic definition.
What is the difference between LLMO and GEO?
GEO measures and improves how often generative engines cite your content across many prompts. LLMO focuses on the brand: whether models describe you accurately, include you in category answers and frame you fairly, including answers given from training data with no search. They overlap on live retrieval.
Is LLMO different from SEO?
LLMO builds on SEO without replacing it. SEO ranks pages in search results. LLMO looks at the brand picture a model forms from many sources, including third-party sites. Assistants that search can only read indexed pages, so weak SEO limits LLMO results.
How do I find out what ChatGPT says about my brand?
Ask it identity, pricing, category and comparison questions about your brand, with web search on and off, a few times each, and save the full answers. Digital Magma does this across five assistants and scores every answer against your confirmed facts.
ChatGPT says something wrong about my company. Can you fix it?
We can fix the sources it reads, which is the only route anyone has: trace the claim, correct your own pages, ask third parties to update theirs, publish a clear facts page and re-test. Answers with search on can change in weeks. Answers from training data may lag until a newer model.
Can an LLMO agency get my brand into ChatGPT’s training data?
No agency can schedule or guarantee that. OpenAI doesn’t publish what future models will train on or when. What you can control is whether GPTBot may read your site and how accurately you are described on public sources, which is what later training may see.
Should I block GPTBot, ClaudeBot or Google-Extended?
That depends on your licensing and competitive concerns, and it is your call. Those three relate to training. Blocking them does not remove you from ChatGPT search, Claude search or Google Search, which use OAI-SearchBot, Claude-SearchBot and Googlebot. Digital Magma writes up the options so you can decide.
Do I need an llms.txt file?
An llms.txt file is optional. It is a proposal that helps some AI agents find your key pages, and Google says it doesn’t need one for AI Overviews or AI Mode. It is cheap to add, so we often include it, but we never sell it as a ranking lever.
How do you measure LLMO results?
We track mention rate, an accuracy score (facts correct divided by facts checked), sentiment and share of voice against named competitors, from repeated prompt runs. We add Bing’s AI Performance report, branded queries in Google Search Console and GA4 referrals from AI tools.
How long does LLM optimization take?
Fact sheet, audit and crawler fixes take the first month. Corrected sources usually show up in search-enabled answers over the following one to three months, depending on re-crawling. Built-in model knowledge changes only when a new model is released, and nobody can time that.
How much does an LLMO agency cost?
Digital Magma quotes LLMO audits and programmes individually, based on assistants, markets, prompt count and how much correction is needed. Our published anchors are an SEO audit at USD 500 one-time and ongoing SEO from USD 600 a month, plus applicable taxes.
Can you write a Wikipedia page for my company?
We won’t. Wikipedia’s conflict of interest guideline strongly discourages editing about your own organisation and requires paid editors to disclose their clients. Articles need independent coverage to meet notability. We help you earn that coverage through digital PR and fix the sources you legitimately control.
Can a US company or agency hire an LLMO agency in India?
A US company or agency can work well with an LLMO agency in India once hours and reporting are agreed. Digital Magma works 9 am to 9 pm IST, which overlaps US Eastern mornings. We test prompts as US buyers phrase them, write in US English and leave you with Admin access to your accounts.
Do you work with agencies on a white-label basis?
Digital Magma offers white-label LLMO work for agencies: audits and reports under your brand or unbranded, an NDA on request and no contact with your client unless you ask. You keep the client relationship and set your own price.
Next step
LLMO agency next step: see what AI says about your brand
Digital Magma will run a sample of brand prompts before we speak, so the first call starts with what the assistants actually said about you.
What happens after you enquire
- We test first: identity, pricing and category prompts in the main assistants
- Short call: what is right, what is wrong and where the errors likely come from
- Written proposal: assistants, prompts, runs, reporting and price, with no obligation
- Call: +91 79823 19697
- Email: contact@digitalmagma.com
- WhatsApp: Chat with us
- Mon–Sat, 9 am–9 pm IST · Sector 137, Noida, India
If AI assistants describe your company wrongly, or leave it out of answers it belongs in, talk to the LLMO agency team at Digital Magma. Agencies can ask about white-label brand audits too.
Get my free brand-answer check
Takes under a minute. We never share your details.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.