How Kornit Digital Increased LLM Mentions and AI Traffic by 120% Within 6 Months

The Angora Media search team partnered with Kornit Digital to design and execute a GEO (Generative Engine Optimization) strategy aligned with how large language models (LLMs) retrieve, prioritize, and synthesize information.

Using the LLM visibility software Chatopic to measure brand visibility across models like ChatGPT, Gemini, and Claude, we combined prompt design, content transformation, technical tuning, and off-site strategies to improve Kornit’s discoverability within LLM-generated answers.

Phase 1: Strategic Prompt Research & Competitor Mapping

The team began by conducting comprehensive prompt research using the query fan-out technique, expanding core prompts into natural variations that reflect different user intents, verticals, and phrasings.

This mirrors how LLMs are trained and optimized: by learning from a wide variety of semantically similar questions across different contexts. By ensuring broad coverage of real-world prompt diversity, we increased the likelihood of alignment with both training patterns and retrieval behavior, maximizing the chances of brand inclusion in generated answers.

We then analyzed how LLMs responded to these prompts and whether the client or its competitors were being cited. From this, we developed a prompt-entity visibility matrix to:

• Identify which topics consistently triggered brand mentions.
• Detect citation patterns that favored competitors.
• Reverse-engineer retrieval pathways (including RAG-style indexing behaviors).
• Uncover semantic gaps in entity recognition, phrasing, and context windows.

This analysis allowed us to benchmark the client’s visibility across LLMs and pinpoint areas of weakness in both content footprint and external references, laying the groundwork for targeted optimization.

Phase 2: Content Optimization with LLM Semantics in Mind

Once we identified the “content-response delta”, we ran a full audit of client’s assets for retrievability by LLMs trained on public web data.

Key techniques included:

• Entity-First Writing: We restructured product/services pages and blog posts so that each clearly established machine-readable entity definitions (e.g., “DTG printing,” “roll-to-roll printing,” “on-demand textile manufacturing”) and reinforced these with co-occurring concepts.
• Structured Q&A Formatting: We incorporated question-answer blocks using real prompt variants to improve match rates with training data and assist retrieval algorithms in aligning prompt → answer → source.
• LLM-Optimized Glossary Pages: Glossary entries were rewritten to include high-confidence definitions, authoritative tone, and internal linking, increasing the chance of being surfaced as a source for hallucination-safe lookups in models trained using Attribution Modeling heuristics.
• Key Takeaways Sections: Content was enriched with bulleted “takeaways,” which models often extract or paraphrase during generation. These act as high-signal vectors during summarization.
• Vector Embedding Alignment: We ensured terms with close semantic meaning were used consistently across pages, improving the likelihood of pages being grouped together in vector space during retrieval-by-similarity operations.
• Schema Markup & Structured Data: Implemented robust schema (e.g., Product, Organization, FAQ, HowTo) to clarify entity relationships and support token-level attribution.

Phase 3: Strengthening External Signals

LLMs often learn trustworthy brand-entity associations through a combination of training-time co-occurrence and retrieval-based exposure where a brand is mentioned alongside relevant concepts within high-authority, crawlable sources.

To influence these associations, we used Chatoptic’s prompt analysis feature to identify domains that were frequently surfaced in LLM responses favoring competitors. These domains were likely influential either because they appeared in the models’ training data or are prioritized in retrieval pathways.

We then strategically contributed content to those sources, reinforcing the brand’s presence in the ecosystem LLMs tend to reference. In doing so, we followed a deliberate framework:

• High-Authority Thought Leadership: Published articles on reputable, crawlable sites that explicitly linked Kornit Digital with high-value industry terms (e.g., “sustainable DTG printing”, “on-demand textile production”).
• Brand-Entity Linking Strategy: Ensured consistent co-mentioning of the brand with relevant entities (e.g., “textile innovation”, “digital fashion”) across editorial, partner, and third-party content, helping strengthen latent semantic associations.
• Citation Diversity: Secured mentions across a broad range of domains, TLDs, content formats, and geographic regions to reduce reliance on any single citation source and mitigate potential model bias toward dominant publications.

This approach helped expand the brand’s external footprint in high authority sources that matter for LLMs.

Phase 4: Monitoring, Attribution, and Continuous Iteration

Using Chatoptic, Kornit’s visibility across LLMs is now continuously monitored through a structured set of GEO metrics, including:

• Prompt Coverage: The percentage of tracked prompts that include the brand in the LLM’s answer.
• Model Consistency: Measuring how consistently the brand appears across different models such as ChatGPT, Gemini, Claude, Perplexity, and Grok.
• Sentiment Analysis: Evaluating the tone, context, and framing used when the brand is mentioned from neutral to promotional or negative.
• Attribution Tracing: Identifying which specific pages are being cited, linked to, or inferred as source material in model outputs (when visible).
• Traffic from LLMs: Monitoring the volume of web sessions driven by AI-generated answers that include clickable citations.

This feedback loop informs Angora Media’s GEO-SEO hybrid strategy, enabling agile adjustments to entity coverage, tone of voice, content formats, and source diversification, ensuring sustained brand visibility in the evolving AI-powered discovery landscape.

Final Notes

This is not a traditional SEO project. Generative engines require a blend of:

• Structured content that fits the vector retrieval layer
• Clear entity framing optimized for training data patterns
• Authority signals spread across public, crawlable ecosystems

GEO is about preparing your brand for a world where LLMs are the new interface and Kornit Digital is now part of that conversation.