LLM Optimization Services
Let your brand be the answer inside ChatGPT, Gemini, Perplexity, and Copilot.
We optimize your entities, sources, citations, and structure so public LLMs can discover, trust, and quote you—ethically and at scale.
LLMO is like SEO for answer engines: we make your brand easier for LLMs to find, verify, and cite by aligning your web presence to the signals models rely on—entities, authority, structure, and evidence. No app rebuilds, no private infra.
Hybridize Your LLM with Leading Open & Closed-Source Large Language Models & AI Tools
Large Language Model Optimization (LLMO) for Public LLMs
LLMO for public LLMs is the discipline of making your brand the reliable, citable answer inside ChatGPT, Gemini, Perplexity, Copilot, and other “answer engines.” Instead of optimizing solely for blue-link rankings, we align your public web footprint to the exact signals models use to decide what to say and whom to cite: entity clarity, source authority, structured evidence, and consistent facts. Think of it as SEO’s next chapter—part content strategy, part data hygiene, part knowledge-graph engineering—implemented ethically so models can find, verify, and quote you with confidence.
Practically, that means we map and harden your entity graph (organization, people, products, and aliases), publish canonical facts, and reinforce them with JSON-LD schema (Organization, Product, FAQPage, HowTo, Article, Review) that includes SameAs, about, and mentions to tie your brand to credible nodes across the web. We reshape key pages into answer-first content—definitions, comparisons, FAQs, and methodology notes—so a model can lift accurate, self-contained explanations without guessing. We build citable evidence (case studies, data notes, lightweight datasets, third-party reviews) and run neutral, editorial PR to place those facts on trusted sources models prefer to quote (.gov, .edu, standards bodies, reputable media and directories). On the technical side, we clean up robots/sitemaps for AI crawlers, add stable anchors for claim-level linking, and eliminate inconsistencies that cause hallucinations or outdated blurbs to persist.
What does this do for you? It increases your Answer Share (how often you’re named in relevant responses) and your Citation Rate (how often the model links or attributes you), corrects misinformation faster, and pushes a consistent narrative into the knowledge ecosystems LLMs learn from. We monitor model-by-model coverage, accuracy, and freshness, then iterate—adding evidence where your claims need stronger corroboration, expanding Q&A coverage where users ask, and tightening schema as standards evolve. No tricks, no gray-hat manipulation—just durable signal building that helps public LLMs treat your brand as the source of record for your category.
Improve LLM Visibility
Garner greater visibility in public LLMs with proper strategy.
Improve LLM Visibility Quality
Expand the quality of existing visibility to show not just for terms, but expand for digital real estate.
Expand LLM Citations
Once you obtain target citations, expand as a reference for more. Rise and repeat to infinity.
Why LLMO Matters
Large Language Model Optimization (LLMO) helps you capture exposure on the most popular public large language models for greater organic visibility. Here's why LLMO matters for your brand:
Own the answer box: Win brand mentions when users ask LLMs for recommendations.
Fix misinfo: Reduce hallucinations or outdated facts about your company.
Attribution & traffic: Increase the rate at which LLMs name and link your brand.
Moat: Entrench your entity authority across the open web and knowledge graphs.
Traffic: With search patterns changing, your business and brand need to be where people are searching.
Answer Engine Surfaces We Target
We don’t “game” models; we strengthen the sources and signals they already trust.
General answer engines: ChatGPT, Perplexity, Gemini, Copilot
Search-integrated answers: Bing, Google (AI Overviews where applicable)
Open-web sources LLMs mine: Wikipedia/Wikidata, news, journals, gov/edu, standards bodies, datasets, docs, GitHub, reputable directories
LLM Visibility Audit
Upon initial engagement with our team, we establish a clean, measurable baseline for how public LLMs encounter, interpret, and cite your brand. We map your entity graph, inspect the sources models already trust, evaluate your content for “answer-first” lift, and verify that your technical signals make you easy to crawl, parse, and quote. The result is a focused, 30–45 day plan that prioritizes high-impact, low-effort moves to increase Answer Share and Citation Rate across ChatGPT, Gemini, Perplexity, and Copilot.
Entity Audit
We identify name collisions (brand vs. person/product), normalize official names and abbreviations, and compile a canonical SameAs graph linking your site to authoritative profiles (e.g., Crunchbase, GitHub, LinkedIn, Wikidata). We review and fix JSON-LD types (Organization/Person/Product), verify key facts (founding date, HQ, leadership), and flag inconsistencies that cause LLM confusion or outdated summaries to persist.
Source Audit
We inventory the high-trust surfaces LLMs lean on—Wikidata/Wikipedia, gov/edu sites, standards bodies, reputable media, industry directories, documentation portals, and GitHub—and score your coverage, authority, and freshness. We highlight missing or weak entries, link rot, outdated facts, and propose editorial placements and page upgrades that strengthen your citable footprint.
Content Audit
We review key pages for direct, self-contained answers, robust FAQs, definitions, and comparisons, checking that claims are properly cited and anchored for snippet-level linking. We assess evidence (case studies, data notes, tables, downloadable CSV/JSON), E-E-A-T signals, internal linking, and identify net-new Q&A topics and comparison pages most likely to win mentions in model responses.
Tech Audit
We validate schema correctness (FAQPage/HowTo/Article/Product), canonical tags, and conflict resolution; review robots.txt and AI crawler allowances (e.g., GPTBot, PerplexityBot), sitemap freshness, and stable anchor IDs for claim-level citations. We flag performance and rendering issues (Core Web Vitals, JS-dependent content) that hinder crawl/parse reliability and recommend fixes to improve model accessibility.
LLM Benchmarking
Using a curated prompt bank for your category, we test across leading models and log brand mentions, link attribution, factual accuracy, and recency adoption. We compute baseline Answer Share, Citation Rate, and hallucination incidence, highlight your top opportunity prompts, and note model-specific quirks (e.g., source preferences, freshness lag) to guide targeted improvements.
LLMO Playbook
You get a sequenced plan with Quick Wins, High-Leverage Projects, owners, dependencies, and due dates—plus a schema bundle, entity graph updates, target source list, and a Q&A/editorial calendar. We include a simple tracking dashboard so you can monitor Answer Share/Citation Rate gains by model as changes roll out.
Our LLM Framework for Public LLM Visibility
We follow a time-tested, internally-built process for maximizing visibility in the most popular large language models (LLMs). Our process continues to evolve.
Our framework strengthens the exact signals public answer engines rely on—entity clarity, source authority, structure, and evidence—so your brand is easier for ChatGPT, Gemini, Perplexity, and Copilot to find, verify, and cite. We don’t “game” models; we make you measurably more citable by fixing identity hygiene, publishing proof, structuring content for lift, and monitoring answer share over time.
Entity Graph & Identity Hygiene
We establish a canonical identity for your organization, people, and products so models can disambiguate you on sight. That includes defining official names and abbreviations, resolving collisions with similarly named entities, and building a robust SameAs graph to verified profiles (Wikidata, GitHub, Crunchbase, LinkedIn, etc.). We normalize key facts—founding year, HQ, leadership, descriptions—and prepare community-friendly entries where notability is met. The outcome is a clean, machine-readable map that ties every mention back to the right you.
Evidence & Citation Building
Models prefer neutral, third-party proof, so we create and distribute citable assets and corroboration. Expect case studies, research notes, and lightweight datasets (CSV/JSON) with clear provenance, plus editorial placements on reputable outlets (.gov, .edu, standards bodies, respected media/directories). We add claim-level anchors and permalinks so specific statements can be referenced precisely, and we standardize your citation style to signal care, neutrality, and reliability.
LLM-Readable Content Structure
We reshape key pages into “answer-first” formats that models can safely lift: concise definitions, direct step-by-steps, and transparent comparisons. Q&A libraries are mapped to real user prompts, each answer self-contained and source-backed. Comparison pages (“X vs. Y”) use clear criteria and tables, while glossaries and methodology notes make your reasoning explicit. Where useful, we attach model-friendly annexes (Markdown, JSON, CSV) that reduce guesswork and increase citation odds.
Schema & Technical Signals
We strengthen machine readability with JSON-LD (Organization, Product, FAQPage, HowTo, Article, Review) and thoughtful use of sameAs, about, and mentions. Robots and sitemaps are tuned for reputable AI crawlers (e.g., GPTBot, PerplexityBot) as you prefer, with fresh, canonical sitemap coverage. We enforce stable anchor IDs for claim-level linking, avoid JS-only rendering for core facts, and address performance/CWV issues that can break parsing or suppress crawl frequency.
Knowledge Source Coverage
We ensure you show up where models already look: editorial directories and industry bodies (not pay-to-play lists), updated newsrooms, clean factsheets and leadership bios, and public documentation with versioned URLs and changelogs. For code-adjacent brands, we enrich GitHub with clear READMEs, license/status badges, release notes, and a security policy. A gap analysis prioritizes which profiles and placements will most influence model citations fastest.
Reputation & Consistency Control
Inconsistent facts cause hallucinations, so we align names, domains, addresses, and descriptions across your major profiles. Sensitive facts—leadership, locations, pricing ranges, headcount—are refreshed and deprecated claims removed. We promote neutral, balanced language that models are more likely to trust and repeat, and we maintain a correction queue to track outreach, re-crawls, and the replacement of stale knowledge across the web ecosystem.
Measurement & Evals (Answer Share)
We prove lift with model-specific testing and track how quickly updates propagate. Baselines include Answer Share (how often you’re named), Citation Rate (attributed/linked mentions), accuracy (hallucination incidence), and freshness (time to adoption). A curated prompt bank per model enables repeatable evaluations, and monthly reports highlight what moved, which claims need stronger evidence, and where to expand Q&A or tighten schema next.
Frequently Asked Questions
- Can you guarantee we’ll be “the” answer? No. We don’t manipulate models—we strengthen signals they already prefer (trusted sources, structured data, consistency). This approach is durable and ethical.No.
- Is this different from traditional SEO? Yes. SEO optimizes for search engines; LLMO optimizes for answer engines—prioritizing entity clarity, evidence, and citations over blue-link ranking alone.
- What about “ai.txt”? We’ll focus on robots.txt and crawler allowances for major AI bots; if you choose to publish an AI usage policy page, we’ll include it in your entity graph.
- Do we need Wikipedia? Helpful, not mandatory. We’ll assess notability and propose alternatives if community acceptance is unlikely.
