The Role of Embeddings in Semantic Search

- What if your TM became a recommendation engine? - What if your glossaries powered your search infrastructure? - What if your localization team could help build the next layer of enterprise AI? 🔥🔥🔥⬇ I sat down with Filip Makraduli from Superlinked for one of the most insightful interviews I’ve had this year. We covered everything from semantic search and cross-lingual personalization to what it really means to build AI-ready language systems. SuperLinked is quietly building the infrastructure that allows machines to understand meaning across languages, formats, and data types. At the center of their work is vector-native technology, specifically, semantic embeddings that replace keyword matching with context-aware, language-agnostic understanding. Filip, who bridges ML-AI engineering and developer relations at SuperLinked, is helping organizations move from brittle string-based systems to flexible architectures where multilingual content can be retrieved, recommended, and repurposed based on intent, not just language. For localization professionals, this is a glimpse into the future: a world where glossaries, translations, and domain-specific content become part of an intelligent, searchable infrastructure and where localization isn’t just about output, but about powering AI systems that actually understand global users. Filip explains how vector embeddings allow us to move beyond strings and keywords to build systems that understand nuance, context, and user intent across languages, formats, and use cases. This is more than a technical upgrade. It’s a call to reimagine how we prepare language data for a world where search becomes a conversation and content must be understood, not just translated. Discover: - Why traditional keyword search is collapsing under user expectations - How vector embeddings create a shared space for meaning across languages - How LangOps breaks from tradition and requires us to treat language as infrastructure, not just output - How you can gradually roll out embedding-based systems without ripping out what already works And this gem: “You don’t have to build separate search systems for German, Arabic, or Spanish anymore. One language-agnostic vector space does it all.” This is not about replacing translators. It’s about a new opportunity to give multilingual content a structure that machines can understand, scale, and personalize without losing meaning. 📖 If you are a language tech geek like me, I am almost sure you'll dig the Full article below. Let me know what you think...

Google is translating all of your content into a numeric value. Let's talk about Vector Embeddings and why it's one of the most important concepts in SEO: Search engines are tasked with the incredibly complex task of understanding the web at scale. They're not only charged with the task of discovering the content on the web, but also understanding it and determining if it's relevant to the query users are searching for. This means that Google needs to find a reliable and inexpensive way that they can understand content. Since machines operate better off numeric values, that's exactly what vector embeddings seek to do. Vector embeddings allow search engines to translate textual content and convert them into numeric representations that machines can better understand. Once your content is translated it's then plotted in a multi-dimension vector space. Once the numerical values are plotted, machines can then directly see semantic relationships between them. So this means that eventually all the content on your website is translated into a numeric value and scored through the vector embedding. When someone performs a search, Google uses the "distance" between the query and the scores of the documents in it's index to determine the articles that are most relevant for the query. This means that when you're optimizing content, you should be always thinking of how search engines would "score" you content when run through an embedding model. This means that you should be: - Ensuring key headings have strong scores associated with the query - Focus at the beginning of a page to ensure you have strong associations with the query at the top of the document - Rewrite and rephrase content to improve query relevance - Eliminate content that would be "off topic" and skew the embedding models interpretation of your content - Think about vector embeddings at a site-wide level and create multiple articles that have strong matches for parent queries you want to rank for. Even if this doesn't change your approach, it's still extremely important to understand how search engines works and why you're doing the things you are. When people talk about "topical authority" and "on-page optimization" it all comes back to vector embeddings. Google is translating your content into numeric values and comparing it against the target query.

𝗧𝗵𝗶𝘀 𝗶𝘀 𝗵𝗼𝘄 𝗚𝗲𝗻𝗔𝗜 𝗳𝗶𝗻𝗱𝘀 𝗺𝗲𝗮𝗻𝗶𝗻𝗴 𝗶𝗻 𝘂𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝘁𝗲𝘅𝘁. ⬇️ And yes it all starts with vector databases — not magic. This is the mechanism that powers AI Agent memory, RAG and semantic search. And this diagram below? Nails the entire flow — from raw data to relevant answers. Let's break it down (the explanation shows of how a vector database works — using the simple example prompt: “Who am I): ⬇️ 1. 𝗜𝗻𝗽𝘂𝘁: ➜ There are two inputs: Data = the source text (docs, chat history, product descriptions...) and the query = the question or prompt you’re asking. These are processed in exactly the same way — so they can be compared mathematically later. 2. 𝗪𝗼𝗿𝗱 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴 ➜ Each word (like “how”, “are”, “you”) is transformed into a list of numbers — a word embedding. These word embeddings capture semantic meaning, so that for example "bank" (money) and "finance" land closer than "bank" (river). This turns raw text into numerical signals. 3. 𝗧𝗲𝘅𝘁 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 ➜ Both data and query go through this stack: - Encoder: Transforms word embeddings based on their context (e.g. transformers like BERT). - Linear Layer: Projects these high-dimensional embeddings into a more compact space. -ReLU Activation: Introduces non-linearity — helping the model focus on important features. The output? A single text embedding that represents the entire sentence or chunk. 4. 𝗠𝗲𝗮𝗻 𝗣𝗼𝗼𝗹𝗶𝗻𝗴 ➜ Now we take the average of all token embeddings — one clean vector per chunk. This is the "semantic fingerprint" of your text. 5. 𝗜𝗻𝗱𝗲𝘅𝗶𝗻𝗴 ➜ All document vectors are indexed — meaning they’re structured for fast similarity search. This is where vector databases like FAISS or Pinecone come in. 6. 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 (𝗗𝗼𝘁 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 & 𝗔𝗿𝗴𝗺𝗮𝘅) ➜ When you submit a query.: The query is also embedded and pooled into a vector. The system compares your query to all indexed vectors using dot product — a measure of similarity. Argmax finds the closest match — i.e. the most relevant chunk. This is semantic search at work. - Keyword search finds strings. - Vector search finds meaning. 7. 𝗩𝗲𝗰𝘁𝗼𝗿 𝗦𝘁𝗼𝗿𝗮𝗴𝗲 ➜ All document vectors live in persistent vector storage — always ready for future retrieval and use by the LLM. This is basically the database layer behind: - RAG - Semantic search - Agent memory - Enterprise GenAI apps - etc. 𝗜𝗳 𝘆𝗼𝘂’𝗿𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗟𝗟𝗠𝘀 — 𝘁𝗵𝗶𝘀 𝗶𝘀 𝘁𝗵𝗲 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 𝘆𝗼𝘂’𝗿𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗼𝗻. --- Need an AI Consultant or help building your career in AI? Message me now

SEO Insight: We found a correlation between Google's meta description rewrites and the cosine similarity between your target keywords and sentences on your page. Ever notice Google rewriting your carefully crafted meta descriptions? You're not alone. An article by Michal Pecánek on Ahrefs reports that Google changes over 60% of meta descriptions. What's fascinating is that Google typically uses sentences already on your page. But how does Google choose these sentences? There is a good chance they do so by leveraging vector embeddings, Google measures semantic relevance, pinpointing the best match. Why does this matter for SEO? • Boost CTR: Optimize sentences Google is likely to use, improving click-through rates. Or ensuring there is a sentence on your page that could be a meta description for a keyword variation you're ranking for. • Enhance Relevance: Align your content closely with target keywords to improve ranking potential. • Identify Optimization Opportunities: Understand how Google views your content to guide strategic edits. Here's a quick process we use to predict Google's choices for Meta Descriptions: 1 - Pull top keywords from Google Search Console. 2 - List your page's sentences in Google Sheets. 3 - Calculate cosine similarity scores between keywords and sentences using embeddings for the keywords and sentences (we scale it to a 10-point scale for easy reading) using our internal Google App Script & Google's text embedding engine. 4 - Apply conditional formatting to highlight top matches for each keyword. Once identified, strategically optimize these key sentences for SEO and to best drive additional clicks. Have you tested how Google picks your meta descriptions? Did you see any other correlations?