# kromber.ai — Full Documentation > kromber.ai is Krombera's AI center of excellence. We architect custom AI systems that listen, learn, and act on behalf of brands, and we produce AI-generated creative at scale. --- ## Company Overview kromber.ai is a subsidiary of Krombera (www.krombera.com), a creative and digital advertising agency founded in 2011 in Istanbul, Turkey. Having earned Turkey's most prestigious advertising and marketing awards and managed digital communications for numerous national and international brands, Krombera now combines 15 years of industry expertise with the limitless possibilities of artificial intelligence. kromber.ai operates two complementary divisions: 1. **kromber.ai Creative** — AI-powered film, video, visual and campaign production 2. **kromber.ai Technologies** — Custom AI infrastructure, multi-agent systems and workflow automation **Tagline:** From Prompt to Impact. **Mission:** Transforming ideas into AI-powered content, and business needs into autonomous AI systems. --- ## kromber.ai Creative kromber.ai Creative replaces traditional production with AI-generated visuals, videos and campaign assets. Brand-consistent, infinitely iterable, produced without physical shoots. Budget fuels creativity, not operations. ### Creative Services #### Hyper-Realistic Commercials and Promotional Films Unlimited locations and production without building sets or renting locations. Whether it is the surface of Mars, 18th-century Istanbul, or an entirely abstract universe, we create promotional films from scratch. Corporate storytelling that brings company history and vision to life, restoring archival footage with AI or crafting never-before-seen visuals in cinematic language. #### AI-Powered Music Videos and Visual Spectacles Audio-reactive visuals that shift second by second in response to rhythm, frequency and emotion. Style transfer that takes real footage and transforms it into a Van Gogh painting, an anime world, or a futuristic cyberpunk universe. #### Digital Casting and Character Design Custom digital brand ambassadors that are ageless and fluent in any language. AI dubbing and lip-sync that localizes a single shoot into 40 different languages, seamlessly adapting lip movements to each language. #### Viral-Focused Short-Form Content for Social Media Trend-driven motion design for TikTok, Reels and YouTube Shorts. Product-focused videos where static product photos transform into dynamic ad content powered by AI. #### The Future of Post-Production Visual effects revolution: background replacement, object removal, wardrobe or age alteration in seconds without green screen. AI upscaling and restoration of old, low-resolution brand archives to 4K/8K quality. #### AI-Generated Visual and Campaign Design Photorealistic image creation: studio-quality product shots, lifestyle imagery and campaign visuals without photo shoots. Brand-consistent asset production trained on the brand's visual identity, ensuring consistency across every touchpoint at any scale. ### Creative Capabilities Summary - Strategic planning and consulting - Digital marketing - Social media management - Channel-focused creative - Digital media buying --- ## kromber.ai Technologies Born from over 15 years of deep operational experience within the digital ecosystem, kromber.ai Technologies is an AI technology venture with a singular mission: to make artificial intelligence function not as an abstract concept, but as a living, breathing layer of business operations. At the core lies a modular, enterprise-grade AI agent architecture designed to embed intelligence into every node of the value chain. LLM integration, multi-agent orchestration, RAG-powered knowledge management, and end-to-end workflow automation operate as a single unified system, configured precisely to each business. The architecture is sector-agnostic by design. Finance, retail, healthcare, real estate, FMCG, manufacturing: kromber.ai adapts to any industry's data structures, regulatory frameworks, and operational workflows. ### Technology Services #### AI Transformation Consulting From strategic discovery to full-scale deployment: digital maturity audits, AI readiness mapping across departments, custom agent ecosystem architecture, and continuous optimization frameworks delivering complete end-to-end AI infrastructure. #### AI Agent Systems Autonomous AI agent systems that integrate into every stage of the value chain. Each agent is trained on brand data, assigned to a specific business function, and operates in real-time coordination with every other agent in the ecosystem, learning, adapting, and self-optimizing with every interaction. #### LLM Integrations and Orchestration Integration of large language models (GPT-4o, Claude, Gemini, open-source alternatives) into business processes through fine-tuning, prompt engineering, and model routing layers. Multiple AI agents coordinate through orchestration frameworks. #### RAG-Powered Knowledge Management Transformation of institutional knowledge (documents, databases, historical records) into a vectorized, real-time retrievable intelligence layer using Retrieval-Augmented Generation. Every AI output is grounded in verified data, eliminating hallucination. #### End-to-End Workflow Automation Manual processes transformed into self-running automation pipelines with Make.com, n8n, Zapier, and custom APIs. Trigger-based flows, conditional logic, webhook architectures, and feedback loops engineered to execute without human intervention. #### AI Analytics and Performance Intelligence Real-time monitoring dashboards and predictive analytics layers that track every AI agent's performance, measure ROI across automated workflows, and surface actionable insights. ### Technologies Track Record - 15+ years of experience - 50+ AI projects - 7 service areas - 20+ brand partners --- ## GEO (Generative Engine Optimization) GEO is a new service offered by kromber.ai. Generative Engine Optimization is the discipline of structuring a brand's digital presence so that large language models (ChatGPT, Perplexity, Gemini, Copilot, Claude) recognize, reference, and recommend the brand in their responses. SEO belonged to the era of the search bar. GEO is writing the new rules for AI-powered discovery. ### How GEO Works 1. **AI Visibility Audit** — Querying ChatGPT, Perplexity, Gemini and Copilot with industry-critical questions to map exactly where a brand appears and where it does not. 2. **Competitor Mention Analysis** — Benchmarking how often AI engines cite competitors versus the brand, identifying content gaps. 3. **Content and Schema Optimization** — Restructuring website content, metadata, structured data and authority signals so that large language models can parse, trust and reference the brand. 4. **Continuous Monitoring and Iteration** — Tracking citation frequency over time and refining strategy with every model refresh. ### Our 10-Step GEO Methodology kromber.ai operates a proprietary 10-step GEO methodology, publicly documented at https://www.kromber.ai/services/geo/#geo-methodology (English) and https://www.kromber.ai/tr/services/geo/#geo-methodology (Turkish). The methodology is also exposed as Schema.org HowTo structured data on both pages. 1. **AI Crawl Mapping** — Mapping which AI engines visit the site, how often, and which pages they reach. Surfacing unvisited critical pages and resolving access gaps. 2. **Baseline Visibility Measurement** — Designing dozens of industry-specific query sets and recording the brand starting score across every AI engine as the reference point for all future progress. 3. **Schema Markup Deployment** — Building the structured-data layer that lets AI engines read content correctly, translating brand, services, FAQs, and products into machine language. 4. **LLM.txt Integration** — Publishing a dedicated document that tells AI engines which parts of the site to prioritize, becoming a direct communication layer between brand and generative AI. 5. **Entity and Knowledge Graph** — Registering the brand as a verified entity across global knowledge networks including Wikidata, ensuring AI engines have a definitive answer to who the brand is. 6. **Citation Engineering** — Analyzing the type of sources AI engines cite, then running the authority work that places the brand inside that citation circle. 7. **Answer-Ready Content** — Producing new content in the formats and structures AI engines prefer: short definitions, concrete lists, numbers, comparisons. Each is a separate citation opportunity. 8. **AI Monitoring Infrastructure** — Building an automated system that tracks brand presence in ChatGPT, Perplexity, Gemini, and Copilot answers daily. Every mention, loss, and competitor takeover is reported. 9. **Measurement Dashboard** — Consolidating every AI visibility metric into one executive dashboard: mention rate, citation accuracy, share of voice, query coverage, all visible in real time. 10. **Continuous Improvement Loop** — AI engines shift every month, and the strategy shifts with them. New query sets, new schema updates, new content formats run in a continuous loop. ### What GEO Measures - **Brand Mention Rate** — How often AI engines cite the brand in relevant queries - **Citation Accuracy** — Whether AI responses describe the brand correctly - **Competitor Share of Voice** — Brand visibility versus competitors in AI-generated answers - **Query Coverage** — Percentage of industry queries where the brand appears --- --- ## Technical GEO Capabilities kromber.ai operates a modern GEO stack engineered for the retrieval and ranking mechanics of Large Language Models. Our optimization work is informed by how transformer-based retrieval-augmented generation systems actually chunk, embed, score, and cite content, not by classic keyword heuristics. ### AI Crawler Optimization We optimize content delivery for the major AI training and retrieval crawlers, each of which has distinct user-agent strings, fetch intervals, JavaScript rendering capabilities, and citation behaviors: - **GPTBot** (OpenAI training corpus) and **OAI-SearchBot / ChatGPT-User** (ChatGPT Search live retrieval) - **ClaudeBot, anthropic-ai, claude-web** (Anthropic training and Claude live retrieval) - **PerplexityBot** (index build) and **Perplexity-User** (on-demand retrieval for Pro queries) - **Google-Extended** (Gemini and Bard training consent) and **GoogleOther** (AI Overviews retrieval) - **Bingbot** with Copilot-aware headers, **Applebot-Extended** (Apple Intelligence) - **Amazonbot, Bytespider (ByteDance/Doubao), Meta-ExternalAgent, FacebookBot, CCBot (Common Crawl), DiffBot, YouBot, Cohere-AI, AI2Bot** for broader AI dataset coverage Our robots.txt, canonical configuration, server-side rendering rules, and content availability policies are tuned per crawler. We monitor server access logs to verify fetch frequency and adjust caching and TTL headers accordingly. ### AI-Readable Content Layer Every optimized site exposes a machine-readable content plane alongside the human UI: - **llms.txt / llms-full.txt** following the emerging llms.txt specification (originated by Jeremy Howard / Answer.AI), placed at the site root for AI crawler discovery - **Structured data in JSON-LD** using the Schema.org vocabulary: Organization, Service, HowTo, FAQPage, BreadcrumbList, Person, Article, ItemList, with nested sameAs and mainEntity references - **OpenGraph and Twitter Card** tags fully populated so social-sourced AI pipelines get clean metadata - **hreflang and canonical alignment** so AI engines do not fragment brand signal across language variants - **Meta AI declarations** (ai-content-declaration, ai-readable, ai-context) signaling explicit AI-friendly content policy ### Entity Graph and Knowledge Linking GEO visibility depends on AI engines recognising a brand as a real-world entity, not a string of text. We build and maintain that entity graph on open knowledge bases that LLM training pipelines over-index on: - **Wikidata** entity with Q-number identifier, founder (P112), inception (P571), headquarters (P159), industry (P452), parent organization (P749), official website (P856), and ISNI / VIAF identifiers where relevant. Wikidata is directly ingested by Google Knowledge Graph, Apple Intelligence, and referenced by Wikipedia. - **Wikipedia** article creation or inclusion in related articles when editorial criteria are met, because Wikipedia text appears in nearly every major LLM training corpus (Common Crawl, The Pile, RedPajama, C4) - **sameAs linking** across LinkedIn, Crunchbase, Bloomberg, PitchBook, X, GitHub, Hugging Face, Instagram, Meta, YouTube, and industry registries, declared in schema.org Organization markup so AI engines can cross-validate identity across sources - **DBpedia, Freebase successor indexes, Google Knowledge Graph API** presence where accessible - **Named entity reinforcement** in canonical positions (title tags, h1, schema name fields, first paragraph, image alt text) for stronger NER (Named Entity Recognition) anchoring ### Content Structuring for Retrieval-Augmented Generation AI engines retrieve brand information through RAG pipelines that chunk content, compute vector embeddings (typically 768 to 3072 dimensions with models in the text-embedding-3 / Cohere Embed / BGE / E5 family), and rank passages via cosine similarity or hybrid BM25+dense retrieval. We structure client content so the right passages win retrieval: - **Semantic chunking** with self-contained paragraphs of 200 to 500 tokens, because common retrieval chunk sizes in production RAG systems fall in that band - **Explicit subject-verb-object structure** so a chunk retrieved out of context still carries the full fact - **Question-answer blocks** with FAQPage schema, because LLMs are fine-tuned on Q&A format and over-surface it in generated answers - **Fact-first lead sentences** because LLM summarizers weight the first 1 to 3 sentences of a passage more heavily - **Entity-dense copy** with founder names, locations, dates, and relationship verbs (founded, acquired, launched, partners with) distributed across body copy for stronger entity grounding - **Consistent terminology**, using the same canonical term every time, because synonym dilution hurts both BM25 lexical matching and dense embedding clustering - **Heading hierarchy** that mirrors a hypothetical user intent tree, aligned with how LLMs chunk by markdown structure ### Citation Engineering Our goal is not just to be read, we want to be cited. This requires: - **Source-worthy formatting**: numbered methodologies, named frameworks, dated claims, original data points, and statistics that AI engines prefer to quote verbatim - **Attribution-friendly structure** with clear authorship (schema.org Person), publication and modification dates, and organization linking - **Primary-source positioning** so client content is the primary source on its own topics, with third-party mirrors reinforcing the primary - **Stable canonical URLs** with long-term content commitments, because retraining runs prefer sources unlikely to 404 - **Internal linking density** sufficient for crawl prioritization but not diluted with boilerplate - **Freshness signals** (dateModified, explicit "last updated" text) because retrieval rankers decay older content ### AI Visibility Measurement We monitor AI engine outputs as an ongoing signal, not a one-time audit. Core metrics: - **Brand Mention Rate** across ChatGPT (GPT-4o, GPT-4.1, o-series), Claude (3.5 and 4 families), Perplexity (Sonar), Gemini (1.5 and 2.x), Copilot, Grok, and DeepSeek for a defined query set - **Citation Accuracy** whether the AI response describes the brand correctly across founder, year, services, headquarters - **Share of Voice** against named competitors in AI-generated answers - **Query Coverage** percentage of industry-relevant queries where the brand surfaces at all - **Position within Answer** whether the brand is the lead citation, a supporting citation, or a passing mention - **Sentiment and Context** whether the brand is framed positively, neutrally, or negatively - **Cross-engine variance** how much visibility shifts between engines, which reveals training data gaps ### Industry Frameworks We Align With - **E-E-A-T** (Google's Experience, Expertise, Authoritativeness, Trustworthiness) still informs AI Overviews ranking - **llms.txt specification** (Answer.AI, 2024) for content discovery - **Schema.org** (W3C-backed vocabulary) for structured data - **IAB Tech Lab ads.txt and app-ads.txt** analog thinking applied to AI consent signaling ### Technical Partners and Infrastructure GEO delivery runs on a mix of commercial APIs, open frameworks, and internal tooling. We do not disclose proprietary pipeline composition. Publicly acknowledged capability areas include AI engine output monitoring, schema validation, entity graph management via open knowledge bases, crawler log analysis, vector-based competitive intelligence, and AI citation tracking across the major generative search engines listed above. --- ## Contact Information - **Website:** https://www.kromber.ai - **Parent company:** Krombera (https://www.krombera.com) - **Location:** Istanbul, Turkey - **Email:** info@krombera.com - **LinkedIn:** https://www.linkedin.com/company/krombera - **Instagram:** https://www.instagram.com/krombera/ --- ## Additional Links - Creative services: https://www.kromber.ai/creative - Technology services: https://www.kromber.ai/technologies - GEO service: https://www.kromber.ai/services/geo - 10-Step GEO Methodology (EN): https://www.kromber.ai/services/geo/#geo-methodology - 10-Step GEO Methodology (TR): https://www.kromber.ai/tr/services/geo/#geo-methodology - GEO service (Turkish): https://www.kromber.ai/tr/services/geo/ - About: https://www.kromber.ai/about - Contact: https://www.kromber.ai/contact