GEO
Get Cited by AI
Generative Engine Optimization from the people who build the AI. We deploy LLMs and architect RAG systems — so we know exactly what makes a source citable.
The Shift
Search Is Changing
The era of “ten blue links” is ending. A growing share of search queries now return AI-generated answers — synthesized from sources the model deems authoritative. If your brand is not in those answers, you are invisible to a rapidly growing audience.
GEO is the discipline of ensuring your brand appears in AI-generated answers. It requires understanding how AI models retrieve, evaluate, and cite sources — knowledge that comes from building these systems, not just optimizing for them.
Our Edge
GEO From the People Who Build the AI
We Deploy LLMs
We run large language models in production every day. We understand from the inside how they process queries, evaluate sources, and generate citations — because we build and maintain these pipelines.
We Architect RAG
Retrieval-Augmented Generation is how AI search works. We design RAG systems for enterprises — so we know exactly what makes a source rank higher in retrieval, get selected for context, and earn a citation.
Technical + Strategic
We combine deep technical execution — structured data, schema markup, LLMS.txt, crawler configuration — with strategic content optimization. Both layers are required for sustained AI visibility.
Process
How It Works
Audit
Timeline: 1–2 weeks
We measure your current AI visibility — how often you are cited, where you are missing, and what your competitors are doing. This establishes the baseline everything else builds on.
Deliverables
- AI visibility baseline across 5+ engines
- Citation gap analysis
- Structured data audit
- Competitor benchmarking report
Optimize
Timeline: 2–4 weeks
We restructure your digital presence so AI models can find, parse, and trust your content. Schema markup, entity signals, machine-readable summaries, and content formatting that aligns with how retrieval systems actually work.
Deliverables
- Schema markup implementation
- Entity optimization
- LLMS.txt configuration
- Content restructuring for AI retrieval
Amplify
Timeline: 2–4 weeks
We build authority signals that make AI models prefer your content over competitors. Citation-friendly assets, digital PR, and strategic content placement across the sources AI models trust most.
Deliverables
- Citation-friendly content creation
- Digital PR campaigns
- Authority signal building
- Knowledge graph optimization
Monitor
Timeline: Ongoing
AI search evolves constantly. We track your citation performance across every major engine, identify new opportunities, and continuously refine the strategy to maintain and grow your visibility.
Deliverables
- Monthly citation reports
- Competitive tracking dashboard
- Continuous optimization
- Quarterly strategy reviews
Techniques
What We Optimize
Eight core techniques that determine whether AI models cite your brand or your competitors.
AI Visibility Audit
Track how and where your brand appears in answers from ChatGPT, Perplexity, Claude, Gemini, and Copilot.
Structured Data
JSON-LD schema markup and Knowledge Graph signals that give AI models machine-readable context about your business.
Entity Optimization
Consistent brand identity across knowledge bases, ensuring AI models correctly associate your brand with your expertise.
Citation-Friendly Content
Restructure content so AI retrieval systems can extract, attribute, and cite your expertise in their answers.
LLMS.txt & Crawler Access
Machine-readable site summaries and crawler permissions that help AI models understand and index your content.
FAQ Schema & Q&A
Question-answer formatting optimized for AI extraction — the format AI models prefer when sourcing answers.
Digital PR
Build citation-worthy authority signals across the web — the references AI models use to decide who to trust.
Monitoring & Reporting
Ongoing citation tracking, trend analysis, and competitive benchmarking across all major AI engines.
For Your Team
Built for Both Sides of the Table
For Technical Leaders
- JSON-LD structured data implementation
- LLMS.txt and robots.txt configuration
- AI crawler access management
- Schema.org markup optimization
- Knowledge Graph signal engineering
- Technical audit with code-level recommendations
For Marketing Leaders
- Brand visibility tracking across AI engines
- Citation share-of-voice analysis
- Content strategy for AI retrieval
- Competitive benchmarking reports
- Digital PR and authority building
- Monthly performance dashboards
FAQ
Common Questions
What is Generative Engine Optimization (GEO)?
GEO is the practice of optimizing your brand's digital presence to be cited by AI-powered search engines like ChatGPT, Perplexity, Claude, and Gemini. While traditional SEO focuses on ranking in Google's blue links, GEO focuses on being included in AI-generated answers — the way a growing number of people now find information.
How is GEO different from traditional SEO?
SEO optimizes for keyword rankings and click-through rates on traditional search engine results pages. GEO optimizes for citation in AI-generated answers. The signals are different: AI models prioritize structured data, entity consistency, authoritative sourcing, and content that is easy to extract and attribute. GEO and SEO are complementary — strong SEO foundations support GEO, and GEO techniques often improve traditional rankings too.
Which AI engines does GEO target?
We optimize for all major AI search and answer engines: OpenAI's ChatGPT and SearchGPT, Perplexity, Anthropic's Claude, Google's Gemini and AI Overviews, Microsoft Copilot, and emerging platforms. Each engine has different retrieval patterns, and our approach covers them all.
How do AI models decide what to cite?
AI models use retrieval-augmented generation (RAG) to find and cite sources. They evaluate content based on structural clarity, entity authority, factual density, source reputation, recency, and how easily information can be extracted and attributed. We know this because we build these exact systems for our clients.
Why does Tilkal have a unique advantage in GEO?
Most GEO providers are marketing agencies learning about AI from the outside. Tilkal deploys LLMs and builds RAG systems — we understand from the inside how AI models retrieve, rank, and cite sources. We have built the exact pipelines that power AI search, so we know precisely what makes a source citable.
How long does it take to see results?
Initial improvements in structured data and technical optimization are visible within 2–4 weeks. Measurable citation improvements typically appear within 6–12 weeks, depending on your starting point and competitive landscape. AI search indexes update more frequently than traditional search, so changes can propagate faster than with SEO.
What is LLMS.txt and why does it matter?
LLMS.txt is a machine-readable file (similar to robots.txt) that provides AI models with a structured summary of your site's content, services, and expertise. It helps AI crawlers understand what your organization does and where to find key information. Having a well-structured LLMS.txt significantly improves your chances of being cited accurately.
Can GEO work alongside our existing SEO?
Absolutely. GEO and SEO are complementary strategies. Many GEO optimizations — structured data, content clarity, entity consistency, FAQ schemas — also improve traditional search rankings. We design our GEO implementations to strengthen your existing SEO rather than compete with it.
Get Started
Ready to Be Cited by AI?
Start with a GEO audit. We will measure your current AI visibility, identify gaps, and build a roadmap to get your brand into AI-generated answers.