LLM-O · AI Assistants
When users ask AI, be the recommendation.
Millions now ask ChatGPT, Claude, and Gemini "what should I buy, use, or hire?" and act on the answer. LLM Optimization shapes how these assistants understand and recommend your brand.
- ChatGPT
- Get Suggested
- Claude
- Get Described Right
- Gemini
- Get Recommended
- Always-on
- AI Sales Rep
- ChatGPT
- Claude
- Gemini
- Perplexity
- Copilot
- Training Data
- Citations
- Reviews
- Wikidata
Key Takeaways
- LLM-O shapes how ChatGPT, Claude, Gemini, and Perplexity describe and recommend your brand.
- Parix Digital audits how AI assistants currently portray you, then strengthens the public signals they learn from and retrieve.
- It turns AI assistants into a channel that recommends you, accurately, to buyers.
The Reality
AI assistants are the new word-of-mouth.
Never Recommended
Buyers ask AI for vendor shortlists, and you're not on them.
Described Incorrectly
The assistant confidently describes services you don't offer, or misses the ones you do.
Invisible Buying Journeys
AI-assisted research happens off your analytics. You never see the deals you lost.
No Control Over the Narrative
Whatever the public web says about you, accurate or not, is what assistants repeat.
Thin Public Footprint
Sparse, inconsistent public information gives models nothing reliable to learn.
Unmeasured Blind Spot
Nobody on the team knows what AI says about your brand this month.
What We Deliver
Shape the signals AI assistants learn from
LLM Perception Audit
What every major assistant says about you today.
- Multi-model Prompting
- Accuracy Scoring
- Competitor Benchmark
- Misinformation Map
Authoritative Content
The clear, factual content models prefer to cite.
- Fact Pages
- Comparison Assets
- Definition Content
- Consistent Claims
Structured & Retrievable Data
Clean semantics for retrieval-augmented assistants.
- Schema & JSON-LD
- Semantic HTML
- Structured Specs
- Crawlable Facts
Knowledge-Base Signals
Presence in the sources models trust.
- Wikidata & Directories
- Industry Citations
- Profile Consistency
- Entity Reinforcement
Reputation & Accuracy
Correct the record, strengthen the truth.
- Review Signals
- Source Tracing
- Correction Content
- Sentiment Improvement
Recommendation Tracking
Measure how assistants describe and suggest you.
- Monthly Model Testing
- Recommendation Frequency
- Accuracy & Sentiment
- Trend Reporting
Tools & Technology
The stack behind the work
Assistants
ChatGPT, Claude, Gemini, Perplexity, Copilot
Signals
Wikidata, Schema.org, Reviews, Citations
Content
Comparison assets, Fact pages, Structured specs
Monitoring
Multi-model prompting, Sentiment tracking
Process
How we get you live
- 01
Probe
Audit what assistants currently say.
- 02
Strengthen
Build authoritative, structured signals.
- 03
Influence
Reinforce entity and reputation sources.
- 04
Monitor
Track recommendations monthly.
Why Parix Digital
We understand how these models think. We build with LLMs every day.
- AI engineers, not just marketers
- Signal strategy grounded in how retrieval works
- Consistent-facts methodology across your web presence
- Misinformation traced to its sources and corrected
- Monthly multi-model recommendation reports
- Works alongside SEO and GEO as one program
Proven Results
Related case studies
FAQ
Frequently Asked Questions
What is LLM-O?
LLM Optimization: shaping the public signals AI assistants learn from and retrieve, so ChatGPT, Claude, and Gemini describe your brand accurately and recommend it when US, UK, and Indian buyers ask for suggestions.
Can you really influence what ChatGPT says about us?
You can't edit the model directly, but you can strongly influence it through public training signals and the live retrieval sources assistants consult: structured facts, authoritative citations, reviews, and consistent claims.
How is LLM-O different from GEO and SEO?
SEO targets the search results page. GEO targets AI-generated search answers. LLM-O targets standalone AI assistants like ChatGPT and Claude. Together they cover every place buyers now ask questions.
What if an AI says something wrong about us?
We identify the misinformation, trace its likely sources, and build accurate, authoritative signals to correct it over time. This matters commercially in trust-sensitive US and UK markets.
How do you measure results?
Recommendation frequency, accuracy, and sentiment tracked across assistants monthly, benchmarked against competitors.
Ready to build your LLM-O (LLM Optimization)?
Tell us about your goals and current setup. We'll get back to you within 24 hours with a tailored plan.
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