[{"data":1,"prerenderedAt":204},["ShallowReactive",2],{"svc-detail-rag-systems":3,"sub-work-rag-systems":158,"ill-services\u002Frag-systems":203},{"slug":4,"short":5,"title":6,"tagline":7,"answer":8,"summary":9,"icon":10,"subServices":11,"useCases":61,"benefits":77,"process":83,"tools":99,"integrations":112,"industries":121,"faq":128,"relatedWork":147,"relatedServices":150,"seo":155},"rag-systems","RAG Systems","RAG (Retrieval-Augmented Generation) development","Answers with sources, from your own documents and data.","Retrieval-Augmented Generation (RAG) connects a language model to your private documents, wikis, tickets and databases so it answers with citations instead of guessing. We build private knowledge chatbots, PDF chat, enterprise and semantic search, embedding pipelines and vector databases for organisations that need accurate, access-controlled answers over their own information.","Private company knowledge chatbots, PDF chat, document intelligence and enterprise search built on embedding pipelines and vector databases such as Pinecone, Weaviate, Qdrant and pgvector, with citations, access control and freshness.","search",[12,17,21,25,29,33,37,41,45,49,53,57],{"slug":13,"name":14,"text":15,"page":16},"private-knowledge-chatbot","Private company knowledge chatbots","Ask questions across policies, wikis, contracts and tickets and get cited answers respecting user permissions.",true,{"slug":18,"name":19,"text":20,"page":16},"pdf-chat","PDF chat & document Q&A","Upload or connect PDFs, reports and manuals and query them conversationally with page-level citations.",{"slug":22,"name":23,"text":24},"document-intelligence","Document intelligence","Extract, compare and summarise across large document sets: contracts, filings, technical manuals.",{"slug":26,"name":27,"text":28,"page":16},"enterprise-search","Enterprise search","One search box over SharePoint, Google Drive, Confluence, Slack, CRM and databases with synthesised answers and links.",{"slug":30,"name":31,"text":32},"semantic-search","Semantic search","Meaning-based search for products, content and support articles that understands intent, not just keywords.",{"slug":34,"name":35,"text":36},"vector-database-setup","Vector database setup","Selection, deployment and tuning of Pinecone, Weaviate, Qdrant, Milvus, ChromaDB, FAISS or pgvector.",{"slug":38,"name":39,"text":40},"embedding-pipelines","Embedding pipelines","Chunking, embedding, metadata and incremental refresh pipelines that keep the index accurate as content changes.",{"slug":42,"name":43,"text":44},"hybrid-search","Hybrid search","Combine keyword, vector and reranking for precise retrieval on technical, legal and product content.",{"slug":46,"name":47,"text":48},"knowledge-graphs","Knowledge graphs & GraphRAG","Entity and relationship graphs alongside vectors for questions that span many documents.",{"slug":50,"name":51,"text":52},"internal-ai-knowledge-base","Internal AI knowledge bases","Curated, governed knowledge bases for support, engineering and operations teams with ownership and freshness rules.",{"slug":54,"name":55,"text":56},"ai-research-assistants","AI research assistants","Assistants that search, read and synthesise across internal and external sources with traceable citations.",{"slug":58,"name":59,"text":60},"rag-evaluation","RAG evaluation & tuning","Retrieval and answer quality measured with test sets; chunking, embeddings and prompts tuned against them.",[62,65,68,71,74],{"title":63,"text":64},"Support knowledge assistant","Agents and customers get accurate answers from the help centre and internal notes, with links to the source.",{"title":66,"text":67},"Policy and compliance Q&A","Staff ask about policies, procedures and regulations and get the current answer with the clause cited.",{"title":69,"text":70},"Contract and document review","Find clauses, compare versions and summarise obligations across thousands of documents.",{"title":72,"text":73},"Engineering knowledge search","Search code docs, runbooks, tickets and Slack in one place and get answers instead of links.",{"title":75,"text":76},"Research and due diligence","Synthesise reports, filings and news into cited briefs in minutes.",[78,79,80,81,82],"Answers with sources instead of searching","Permissions respected per user and document","Content stays fresh through incremental indexing","Measured retrieval and answer quality","Data stays inside your boundary",[84,87,90,93,96],{"step":85,"text":86},"Content and access audit","Inventory sources, formats, owners and permissions; decide what belongs in the index and who may see it.",{"step":88,"text":89},"Retrieval design","Chunking, embeddings, metadata, hybrid search and reranking chosen and tested on your documents.",{"step":91,"text":92},"Ingestion pipeline","Connectors and refresh jobs so the index tracks the source of truth without manual uploads.",{"step":94,"text":95},"Answer layer and evaluation","Prompting, citations, refusal behaviour and a scored test set built from real questions.",{"step":97,"text":98},"Rollout and governance","Launch to a pilot group, monitor unanswered questions and quality, and set content ownership rules.",[100,101,102,103,104,105,106,107,108,109,110,111],"Pinecone","Weaviate","Qdrant","Milvus","ChromaDB","FAISS","PostgreSQL + pgvector","LangChain","LlamaIndex","OpenAI","Anthropic Claude","Cohere Rerank",[113,114,115,116,117,118,119,120],"SharePoint","Google Drive","Confluence","Notion","Slack","Zendesk","Salesforce","GitHub",[122,123,124,125,126,127],"saas-technology","professional-services","finance","healthcare","manufacturing","real-estate-construction",[129,132,135,138,141,144],{"q":130,"a":131},"What is RAG and why not just fine-tune a model?","RAG retrieves relevant passages from your documents at question time and gives them to the model, so answers are current, cited and access-controlled. Fine-tuning changes how a model behaves, not what it knows, and cannot keep up with changing content. For knowledge questions RAG is the right tool; the two are sometimes combined.",{"q":133,"a":134},"How do you handle document permissions?","We store permission metadata with every chunk and filter retrieval by the requesting user, so nobody sees an answer built from a document they cannot open. Permissions are synced from the source system (SharePoint, Drive, Confluence) rather than copied once.",{"q":136,"a":137},"How accurate will the answers be?","That depends mostly on retrieval quality and content quality. We build a test set of real questions with expected sources, measure retrieval hit rate and answer correctness, and tune chunking, embeddings and reranking until the numbers are good. We report those numbers rather than promising percentages up front.",{"q":139,"a":140},"Which vector database should we use?","For most teams pgvector in an existing Postgres is enough and simplest to operate. Pinecone or Weaviate suit larger scale and managed hosting; Qdrant and Milvus are strong self-hosted options; ChromaDB and FAISS fit prototypes and local use. We recommend based on scale, hosting and existing skills.",{"q":142,"a":143},"Can it stay up to date automatically?","Yes. Connectors watch sources for changes and re-embed only what changed, so the index tracks the source of truth. Freshness and failed-sync alerts are part of the pipeline. For high-change sources such as ticketing systems we index near real time; for policy documents a nightly sync is usually enough and cheaper.",{"q":145,"a":146},"Can we keep everything on-premises?","Yes. The full stack (embeddings, vector store and an open-source LLM) can run inside your cloud account or on your own hardware. Quality is somewhat lower than the best hosted models but often sufficient, and we help you decide with a side-by-side test.",[148,149],"enterprise-rag-knowledge-base","saas-support-rag-bot",[151,152,153,154],"generative-ai-llm","ai-chatbots","open-source-ai","nlp",{"title":156,"description":157},"RAG Development: Enterprise Search & PDF Chat | AIYatch","RAG development: private knowledge chatbots, PDF chat, enterprise search, embedding pipelines and vector databases: cited, access-controlled answers.",[159,176,189],{"path":160,"title":161,"description":162,"industry":163,"services":164,"results":165,"cover":175,"coverAlt":175},"\u002Fwork\u002Fecommerce-support-assistant","ChatGPT-powered support assistant for an online retailer","A GPT-based assistant grounded in order data and help-centre content that now handles the majority of first-line customer inquiries.","Retail & E-commerce",[152,4,151],[166,169,172],{"value":167,"label":168},"80%","of inquiries resolved without an agent",{"value":170,"label":171},"60%","faster first response",{"value":173,"label":174},"+40%","customer satisfaction (CSAT)",null,{"path":177,"title":178,"description":179,"industry":180,"services":181,"results":182,"cover":175,"coverAlt":175},"\u002Fwork\u002Fsaas-onboarding-assistant","Onboarding assistant for a B2B SaaS product","An in-app assistant that guides new users through setup, answers product questions and nudges toward activation.","SaaS & Technology",[152,4,151],[183,186],{"value":184,"label":185},"+27%","14-day activation rate",{"value":187,"label":188},"-35%","onboarding-related support tickets",{"path":190,"title":191,"description":192,"industry":193,"services":194,"results":196,"cover":175,"coverAlt":175},"\u002Fwork\u002Fenterprise-rag-knowledge-base","RAG knowledge base for an enterprise services firm","A retrieval-augmented assistant over thousands of internal documents, with citations and role-based access.","Professional Services",[4,195,151],"ai-integration",[197,200],{"value":198,"label":199},"~70%","faster answers to policy and process questions",{"value":201,"label":202},"100%","answers with source citations","\u003Csvg xmlns=\"http:\u002F\u002Fwww.w3.org\u002F2000\u002Fsvg\" viewBox=\"0 0 640 480\" role=\"img\" aria-hidden=\"true\">\u003Cg fill=\"none\" stroke=\"currentColor\" stroke-width=\"5\" stroke-linecap=\"round\" stroke-linejoin=\"round\">\u003Cpath d=\"M55 275H70M55 275V145H137L155 163V160\" stroke-width=\"3.5\" stroke-opacity=\"0.45\"\u002F>\u003Cpath d=\"M70 290H85M70 290V160H152L170 178V175\" stroke-width=\"3.5\"\u002F>\u003Cpath d=\"M85 175H167L185 193V305H85Z\" stroke-width=\"3.5\"\u002F>\u003Cpath d=\"M167 175V193H185\" stroke-width=\"3.5\"\u002F>\u003Cline x1=\"103\" y1=\"205\" x2=\"159\" y2=\"205\" stroke-width=\"3.5\" stroke-opacity=\"0.45\"\u002F>\u003Cline x1=\"103\" y1=\"221\" x2=\"167\" y2=\"221\" stroke-width=\"3.5\" stroke-opacity=\"0.45\"\u002F>\u003Cline x1=\"103\" y1=\"237\" x2=\"151\" y2=\"237\" stroke-width=\"3.5\" stroke-opacity=\"0.45\"\u002F>\u003Ccircle cx=\"103\" cy=\"253\" r=\"7\" fill=\"var(--accent, #0E7490)\" stroke=\"none\"\u002F>\u003Cline x1=\"118\" y1=\"253\" x2=\"160\" y2=\"253\" stroke-width=\"3.5\" stroke-opacity=\"0.45\"\u002F>\u003Ccircle cx=\"305\" cy=\"220\" r=\"85\"\u002F>\u003Cline x1=\"370.1\" y1=\"274.6\" x2=\"416.1\" y2=\"313.2\" stroke-width=\"11\"\u002F>\u003Cpath d=\"M255.5 202A52.7 52.7 0 0 1 295.8 168.1\" stroke-opacity=\"0.45\"\u002F>\u003Cline x1=\"400\" y1=\"200\" x2=\"432\" y2=\"200\" stroke-width=\"3.5\"\u002F>\u003Cpath d=\"M421.6 206L432 200L421.6 194\"\u002F>\u003Crect x=\"440\" y=\"130\" width=\"140\" height=\"190\" rx=\"16\" stroke-width=\"3.5\"\u002F>\u003Cline x1=\"462\" y1=\"165\" x2=\"558\" y2=\"165\" stroke-width=\"3.5\" stroke-opacity=\"0.45\"\u002F>\u003Cline x1=\"462\" y1=\"185\" x2=\"542\" y2=\"185\" stroke-width=\"3.5\" stroke-opacity=\"0.45\"\u002F>\u003Cline x1=\"462\" y1=\"205\" x2=\"558\" y2=\"205\" stroke-width=\"3.5\" stroke-opacity=\"0.45\"\u002F>\u003Cline x1=\"462\" y1=\"225\" x2=\"522\" y2=\"225\" stroke-width=\"3.5\" stroke-opacity=\"0.45\"\u002F>\u003Ccircle cx=\"470\" cy=\"280\" r=\"7\" fill=\"var(--accent, #0E7490)\" stroke=\"none\"\u002F>\u003Ccircle cx=\"494\" cy=\"280\" r=\"7\" fill=\"var(--accent, #0E7490)\" stroke=\"none\"\u002F>\u003Cline x1=\"514\" y1=\"280\" x2=\"556\" y2=\"280\" stroke-width=\"3.5\" stroke-opacity=\"0.45\"\u002F>\u003C\u002Fg>\u003C\u002Fsvg>",1792973685581]