BLV LLM Optimization Agency
Your brand should be first class in AI search. We engineer how large language models understand your entity so you appear, get cited, and get chosen inside assistants and AI answers. Less guessing. More structured signals. Measurable lift in assistant traffic and assisted revenue.
3 to 6 months
Typical window to capture durable LLM presence for priority entities
120 percent plus
Median growth in assistant sourced visits after structured rollout
95 plus
Target LLMO score across core entity pages and citations
Zero fluff
Everything tracked to conversions and revenue influence
What is LLMO
LLMO is the discipline of shaping how large language models understand and retrieve your brand as an entity. It blends structured data, knowledge graph alignment, reputation and source grounding so assistants can confidently surface you inside answers, summaries, and product picks.
Entity centric
We optimize your entity and the graph around it. People. Products. Locations. Offers. Policies. Proof.
Grounded sources
Assistants favor verifiable sources. We seed and align citations that LLMs can point to without hallucination risk.
Outcome tracked
We track assistant exposure, citations, branded answer share, and assisted conversions. No black boxes.
BLV LLMO framework
Discovery
- Assistant landscape and query intent mapping
- Entity inventory and disambiguation audit
- Baseline LLM retrieval tests and score
Schema and content
- JSON LD across services, products, reviews, policies
- Answer first content that assistants can quote
- FAQ and how to patterns for PAA and assistants
Graph and citations
- Knowledge graph alignment and sameAs footprint
- Third party profiles and review ecosystems
- Author and organization signals of experience and trust
Evidence and media
- Original data points LLMs can ground to
- Media objects with machine readable context
- Safety and compliance pages for trust
Experimentation
- Prompt and retrieval tests inside major assistants
- Entity merges, redirects, and synonym handling
- A B testing for summaries and selections
Measurement
- Assistant impressions and answer share
- Citation mentions and span extraction tracking
- Assisted revenue and attribution views
How assistants pick brands
Signals we strengthen
- Structured data coverage and correctness
- Entity uniqueness and disambiguation
- Citations that agree with your claims
- Answerable content with concise proofs
- Freshness and recency for time sensitive prompts
- User proof such as reviews and outcome data
Risks we reduce
- Hallucination because of weak grounding
- Brand confusion with near name entities
- Policy or safety gaps that block exposure
- Dead sources that break citations
Selected case studies
Ecommerce beauty
Product entities aligned and cited across brand site and third party sources. Assistant share for priority terms increased. Add to cart lift reported.
Highlights include structured product data, review markup, and consistent brand authority pages.
Healthcare services
Service pages rebuilt with medical citations and clear eligibility logic. Assistants surfaced the clinic in localized queries with answer snippets.
Outcomes measured with form submits and booked consultations.
Local services
Entity disambiguation and review ecosystem clean up. Assistants switched from generic listings to branded recommendations in common scenarios.
Focus on NAP consistency, policy pages, and staff expertise bios.
We can include redacted screenshots and full numbers under NDA. Ask on the call.
LLMO services and deliverables
Foundation sprint
- Entity inventory and LLMO baseline
- Priority schema deployment and fixes
- Five core pages rebuilt for assistant answers
- LLMO score with next step roadmap
Expansion
- Product and services scale out
- Knowledge graph and sameAs footprint
- Citations and review system playbook
- Assistant test suite and monitoring
Ongoing optimization
- Monthly experiments and content releases
- Assistant presence and share tracking
- Technical upkeep and regression checks
- Quarterly insights with revenue linkage
BLV vs traditional SEO
| Dimension | Traditional SEO | BLV LLMO |
|---|---|---|
| Primary goal | Rank pages in classic SERPs | Win assistant answers and citations that drive action |
| Unit of optimization | Keywords and pages | Entities and verified sources with pages |
| Proof | Backlinks and content volume | Grounded evidence, policy trust, user outcomes |
| Measurement | Sessions and rankings | Assistant exposure, answer share, assisted revenue |
FAQ
How long until we see movement
Foundational fixes can shift retrieval tests within weeks. Durable assistant share usually takes a few months because models and sources need to crawl, ground, and stabilize.
Do we need to change our CMS or stack
No. We work with your current stack. We add structured data, content improvements, and graph alignment without breaking your site.
Is LLMO replacing SEO
They complement each other. Assistants borrow from web signals and your classic SEO wins still matter. LLMO ensures you do not vanish when users skip the SERP and ask an assistant.
What do you measure
Assistant impressions, answer share for target intents, citations of your pages, branded selection rate, and assisted conversions. We also track LLMO score across critical entities.
Request your LLMO plan
Use the form below. If the widget does not load, email info@blv.gr.