ZTABS builds RAG systems for Lisbon businesses — AI that retrieves and generates answers from your company data. Portuguese-language document understanding, knowledge bases, and intelligent search.
RAG (Retrieval-Augmented Generation) development in Lisbon serves Portuguese businesses building AI systems grounded in their own data — the architecture that enables an AI to answer "What were our sales in the Algarve last quarter?" by actually looking up the answer in your data rather than generating a plausible-sounding fiction. RAG is the bridge between powerful large language models and the specific knowledge that makes those models useful for your business. Without RAG, an LLM is smart but uninformed about your company. With RAG, it becomes a knowledgeable assistant that can cite its sources. Lisbon's RAG context: why Portuguese businesses need grounded AI. The hallucination problem: LLMs without RAG generating confident but incorrect answers — a critical risk for business applications. Example: ask GPT-4 about your company's return policy → it will generate a plausible-sounding return policy that may have nothing to do with your actual terms. Business risk: employees or customers making decisions based on fabricated information — legal, financial, and reputational consequences. RAG solution: before generating an answer, the system retrieves relevant documents from your knowledge base → LLM generates a response grounded in actual company data → response includes citations showing where the information came from. Portuguese language RAG: RAG with Portuguese content requiring specific technical considerations. Portuguese embeddings: converting Portuguese text to vector representations — embedding models needing strong Portuguese language representation. Challenge: many embedding models trained primarily on English — Portuguese text potentially producing lower-quality embeddings. Solution: multilingual models (BGE-M3, multilingual-e5-large) or Portuguese-specific fine-tuned models performing significantly better for Portuguese document retrieval. Portuguese tokenisation: how text is split into chunks affects retrieval quality — Portuguese sentence structure, paragraph length, and document formatting differing from English patterns. Portuguese-specific: chunking strategies respecting Portuguese sentence boundaries, paragraph structures, and document conventions. Diacritics: Portuguese characters (ã, õ, ç, á, é, í, ó, ú, â, ê, ô) needing proper handling throughout the RAG pipeline — encoding, indexing, and search. Example: a search for "informação" must also match "informaçao" (common misspelling) — fuzzy matching calibrated for Portuguese. GDPR and data processing: RAG systems processing company data under GDPR frameworks. Data classification: understanding what data enters the RAG system — personal data requiring GDPR compliance, confidential data requiring access controls, and public data having fewer restrictions. Processing records: Article 30 records of processing activities — documenting what data the RAG system processes, why, and how. Data minimisation: RAG systems only retrieving relevant documents — not loading entire databases into LLM context. Right to erasure: if a customer exercises right to erasure, their data must be removable from the RAG knowledge base — requiring proper data lineage. Portuguese CNPD: CNPD actively enforcing GDPR — Portuguese businesses needing documented AI data processing compliance. Lisbon's enterprise data: Portuguese businesses sitting on vast amounts of unstructured data. Typical: contracts and agreements (many in Portuguese legal language), customer correspondence (email, chat, and letters in Portuguese), product documentation (manuals, specifications, and guides), financial reports (quarterly, annual, and regulatory), employee knowledge (policies, procedures, and training materials), and industry regulations (Portuguese and EU regulatory documents). The problem: this knowledge existing in PDFs, Word documents, emails, and wikis — accessible to whoever knows where to look, invisible to everyone else. RAG value: making all of this institutional knowledge instantly searchable and answerable — "What does our contract with [supplier] say about delivery penalties?" answered in seconds, with the exact contract clause cited.
Lisbon's RAG development demand: (1) Banking and financial services: Portuguese finance. Financial RAG: regulatory knowledge (Portuguese banking — Banco de Portugal, CMVM, and ASF regulations. RAG: compliance officers asking questions about regulatory requirements and getting answers with specific regulation citations. Knowledge base: Portuguese and EU financial regulations — thousands of pages indexed and retrievable. Value: from hours of manual regulation searching to instant, cited answers), client advisory (wealth management and private banking — advisors needing instant access to investment research, market analysis, and client portfolio information. RAG: "What is our current view on European utilities?" → retrieval from research database → synthesised answer with analyst citations), product knowledge (banking product information — rates, terms, features, and eligibility across hundreds of products. RAG: customer-facing teams accessing accurate, current product information. Compliance: ensuring product information provided to customers is accurate and up-to-date — RAG pulling from approved product documentation), and audit (audit support — auditors querying historical records, policies, and procedures. RAG: "What was our lending approval process in Q2 2024?" → retrieval from policy archive → documented answer with version and date). (2) Legal: Portuguese law firms. Legal RAG: legal research (Portuguese legal research — searching Portuguese legislation, case law, and legal doctrine. RAG: lawyer asking "What is the current jurisprudence on non-compete clauses in Portugal?" → retrieval from legal database → synthesised answer with case citations. Portuguese law: Código do Trabalho, Código Civil, and Portuguese court decisions — indexed and searchable), contract analysis (contract review assistance — RAG system having ingested the firm's contract templates, client agreements, and clause library. Query: "Find all contracts with price escalation clauses" → retrieval across hundreds of contracts → structured results with contract identification), due diligence (M&A due diligence — vast document rooms indexed for intelligent querying. RAG: "What environmental liabilities are mentioned in the target company documents?" → retrieving relevant sections from thousands of due diligence documents), and knowledge management (firm knowledge — precedent research, partner expertise, and institutional knowledge. RAG: connecting associates with the firm's collective knowledge — "Has the firm handled similar cases?" → retrieval from case management system). (3) Healthcare: Portuguese health sector. Healthcare RAG: clinical (clinical decision support — RAG system connected to medical literature, treatment protocols, and institutional guidelines. Query: "What is the recommended treatment for stage II breast cancer per current ESMO guidelines?" → retrieval from guideline database → cited answer. Portuguese: Portuguese medical guidelines (DGS — Direção-Geral da Saúde) indexed alongside international guidelines), pharmaceutical (drug information — pharmaceutical company knowledge bases containing product information, clinical trial results, and regulatory submissions. RAG: medical information teams answering healthcare professional queries from grounded data. INFARMED: Portuguese pharmaceutical regulatory requirements for information accuracy), and patient (patient information RAG — healthcare providers answering patient questions from approved information resources. Portuguese: patient information in European Portuguese — accessible, accurate, and appropriately simplified). (4) Technology: Lisbon tech companies. Tech RAG: documentation (developer documentation RAG — software companies making documentation searchable through conversational AI. Query: "How do I configure authentication with our API?" → retrieval from documentation → step-by-step answer with code examples. Lisbon tech: OutSystems, Unbabel, and growing SaaS companies making documentation AI-accessible), support (customer support RAG — support teams using RAG to find solutions from knowledge bases, previous tickets, and documentation. Query: customer reports error → RAG searches similar previous incidents → provides resolution steps with confidence score. Multilingual: Portuguese and English support knowledge searchable in both languages), and internal (internal knowledge management — company wikis, runbooks, and process documentation searchable through RAG. Value: new employees getting answers to questions without interrupting colleagues — institutional knowledge democratised). (5) Government: Portuguese public sector. Government RAG: citizen service (government service knowledge — RAG systems helping citizen service agents answer questions about government services, requirements, and procedures. Portuguese: understanding citizen queries in Portuguese and retrieving from Portuguese government documentation), policy (policy research — government analysts querying legislative databases, policy documents, and committee reports. RAG: "What were the parliamentary discussions about digital taxation in 2024?" → retrieval from Assembleia da República records), and procurement (government procurement — RAG systems searching tender requirements, evaluation criteria, and contract terms across hundreds of procurement documents).
Our Lisbon RAG development follows a structured methodology: (1) Data assessment (week 1): understanding your knowledge. Document inventory: cataloguing all data sources for the RAG system — documents, databases, wikis, emails, and external sources. Portuguese documents: assessing Portuguese document quality — OCR quality for scanned PDFs, text extraction accuracy, and encoding. Data classification: classifying data by sensitivity — public (can be processed by external LLM API), confidential (requires private LLM deployment), and personal (requires GDPR compliance measures). Volume: measuring total knowledge base size — determining infrastructure requirements. Quality: assessing document quality — well-structured documents producing better retrieval than poorly formatted content. (2) RAG architecture (weeks 1-2): designing the system. Architecture: designing the RAG pipeline components. Document processing: PDF extraction, OCR for scanned documents, HTML parsing, and structured data extraction. Portuguese processing: handling Portuguese PDF layouts, Portuguese OCR (particularly for older documents with Portuguese typography), and Portuguese table extraction. Chunking: splitting documents into retrievable chunks. Strategy: semantic chunking (splitting at topic boundaries), fixed-size with overlap (simpler but effective), or hierarchical (parent-child chunks for context). Portuguese chunking: respecting Portuguese paragraph structures, sentence boundaries, and document formatting conventions. Chunk size: typically 500-1,000 tokens — balancing retrieval precision with context completeness. Embeddings: converting chunks to vector representations. Model selection: multilingual-e5-large (strong multilingual performance), BGE-M3 (excellent for Portuguese), or Portuguese-fine-tuned models for maximum Portuguese retrieval quality. Portuguese testing: evaluating embedding quality with Portuguese query-document pairs — ensuring Portuguese queries retrieve relevant Portuguese documents. Vector store: storing embeddings for fast similarity search. Options: Pinecone (managed, scalable), Weaviate (open-source, flexible), Qdrant (performant, open-source), pgvector (PostgreSQL extension — simplest for smaller deployments), or Chroma (lightweight, good for development). Portuguese: vector store supporting Portuguese text metadata for hybrid search (vector + keyword). LLM: generating answers from retrieved context. Model: GPT-4o (strong Portuguese), Claude (good reasoning + Portuguese), Llama 3 (open-source, deployable locally for data sensitivity), or Mistral (European, multilingual). Portuguese generation: system prompt establishing European Portuguese output, citation format, and response structure. (3) Development (weeks 2-4): building the pipeline. Ingestion pipeline: automated document processing — new documents added to the knowledge base automatically processed, chunked, embedded, and indexed. Portuguese pipeline: handling Portuguese PDF extraction (including scanned documents with Portuguese OCR), Portuguese text cleaning (removing headers/footers, page numbers), and Portuguese metadata extraction (titles, dates, authors). Retrieval: building the retrieval system. Hybrid: combining vector search (semantic similarity) with keyword search (exact term matching) — hybrid retrieval outperforming either alone for Portuguese. Reranking: retrieved candidates reranked by a cross-encoder model — improving precision by re-evaluating relevance with full query-document attention. Portuguese reranking: cross-encoder trained on Portuguese data or multilingual model with strong Portuguese performance. Generation: building the answer generation pipeline. Context injection: retrieved documents injected into LLM prompt — formatted for readability with source identification. Answer generation: LLM generating answer grounded in retrieved context — instructed to cite sources and indicate confidence. Citation: every claim in the answer linked to the source document — enabling verification. Portuguese generation: answers in European Portuguese with proper grammar, vocabulary, and register. (4) Evaluation (weeks 3-4): measuring quality. Retrieval evaluation: measuring retrieval quality — precision (are retrieved documents relevant?), recall (are all relevant documents retrieved?), and MRR (is the best document ranked first?). Portuguese benchmark: creating Portuguese evaluation dataset — 200+ question-document pairs covering representative queries. Generation evaluation: measuring answer quality — accuracy (factually correct per source documents), completeness (answering the full question), faithfulness (not adding information not in the sources), and language quality (natural European Portuguese). Human evaluation: Portuguese-speaking evaluators assessing answer quality — verifying factual accuracy and Portuguese language naturalness. End-to-end: measuring the complete system — answer accuracy, response time, and user satisfaction. Iteration: improving based on evaluation — adjusting chunking, tuning retrieval, and refining generation prompts. (5) Deployment (weeks 4-5): launching the system. Interface: deploying the RAG interface — chat interface (conversational Q&A), search interface (document search with AI summary), API (integration into existing tools), or Slack/Teams bot (accessible within existing workflow tools). GDPR: data processing documentation, privacy notices, and user consent where required. Access control: role-based access — different users seeing different document subsets based on their permissions. Monitoring: tracking system performance — query volume, response quality, retrieval relevance, and user satisfaction. Portuguese monitoring: monitoring Portuguese language quality in responses — flagging Brazilian Portuguese output or grammatical errors. Knowledge base maintenance: processes for keeping the knowledge base current — automated document ingestion for new content, version management for updated documents, and deletion for removed content.
Insurance / Knowledge Management / Multilingual
A Portuguese insurance company (€2.1B GWP — life, health, and non-life insurance, 3,200 employees, 180 branches across Portugal) needed a RAG system for institutional knowledge management. (1) Knowledge fragmentation: the company having accumulated 28 years of documents — policy wordings, underwriting guidelines, claims procedures, regulatory circulars, training materials, and compliance documentation. Volume: approximately 45,000 documents totalling 2.8 million pages. Location: scattered across SharePoint (40%), shared network drives (30%), email (15%), and legacy document management system (15%). Accessibility: finding the right document requiring institutional knowledge — "Maria in Lisbon claims knows where the 2019 flood guidelines are." When Maria is unavailable or retires, the knowledge is effectively lost. (2) Claims processing: claims handlers needing to reference policy wordings, procedure manuals, and regulatory guidance for every claim. Manual lookup: average 22 minutes per claim spent searching for relevant documentation — policy terms, coverage limits, and applicable procedures. 450 claims handlers: 22 minutes × 15 claims/day × 450 handlers = 2,475 hours/day spent searching (equivalent to 309 full-time employees just searching for information). Accuracy: claims handlers sometimes missing relevant policy provisions or procedure updates — using outdated versions of documents. Impact: 4% of claims decisions later overturned due to information that was available but not found — costing an average €8,500 per overturned decision. (3) Compliance: ASF (Autoridade de Supervisão de Seguros e Fundos de Pensões) issuing regular circulars, guidelines, and regulatory updates. Volume: 300+ regulatory documents with ongoing updates. Challenge: compliance team needing to ensure every claims handler, underwriter, and branch employee is operating under current regulatory guidance. Current state: regulatory updates communicated via email — compliance uncertain whether information reaches all 3,200 employees effectively. Exam: compliance staff spending 2-3 hours answering internal regulatory questions daily — "Does this scenario require ASF notification?" requiring manual document search. (4) Customer service: 180 branches and call centre agents answering customer questions about policies, coverage, and claims. Product knowledge: 180+ insurance products each with specific terms, conditions, and exclusions — impossible for any individual to know all products in detail. Customer wait: agents putting customers on hold to search for product information — average 4 minutes per search. Quality: inconsistent answers across branches — the Lisbon branch potentially giving different information than the Porto branch for the same product question. (5) Portuguese language: all documents in European Portuguese — including Portuguese legal language (often complex and formal), insurance-specific Portuguese terminology, and regulatory Portuguese. Document quality: older documents being scanned PDFs (Portuguese OCR challenging for insurance-specific terminology), some handwritten notes (illegible), and inconsistent formatting across decades of document creation.
We built an enterprise RAG system over 6 weeks — ingesting 45,000 documents and serving 3,200 employees. (1) Document processing: ingesting the knowledge base. PDF extraction: processing 45,000 documents — modern PDFs (text extraction), scanned PDFs (Portuguese OCR using specialised insurance vocabulary), and legacy formats (RTF, DOC conversion). Portuguese OCR: fine-tuned OCR model for Portuguese insurance documents — handling insurance-specific Portuguese terminology, table extraction from policy documents, and handwritten annotations. Metadata: extracting document metadata — type (policy, procedure, regulatory), date, version, department, and product line. Portuguese metadata: extracting Portuguese-specific document identifiers — apólice (policy number), circular (regulatory reference), and norma (standard reference). Quality filtering: excluding illegible documents, duplicates, and superseded versions — keeping the knowledge base clean and current. Result: 42,000 documents successfully processed (93% — 3,000 excluded due to poor quality or duplication). Total: 2.6 million pages of Portuguese insurance knowledge indexed and searchable. (2) RAG pipeline: Portuguese insurance knowledge retrieval. Chunking: document-aware chunking — policy wordings chunked by clause, procedures chunked by section, and regulatory documents chunked by article. Portuguese structure: respecting Portuguese document conventions — numbered articles (Artigo), clauses (Cláusula), and annexes (Anexo). Embeddings: multilingual-e5-large with Portuguese fine-tuning on insurance domain — ensuring Portuguese insurance queries retrieve relevant Portuguese insurance documents. Vector store: Qdrant (self-hosted — insurance data not leaving company infrastructure). Hybrid search: vector search + BM25 keyword search — ensuring exact policy numbers, clause references, and regulatory circular numbers are always found. Portuguese keyword: Portuguese keyword search handling diacritics and morphological variation. Reranking: cross-encoder reranking with Portuguese insurance training data — improving precision for complex insurance queries. LLM: GPT-4o for answer generation — system prompt establishing Portuguese insurance context, citation requirements, and accuracy guidelines. On-premises option: Llama 3 deployed on company infrastructure available for sensitive data — routing queries to appropriate model based on data sensitivity. (3) Access and interface: serving 3,200 employees. Chat interface: conversational Q&A — employees asking questions in natural Portuguese and receiving cited answers. Example: claims handler asks "Quais são os limites de cobertura para danos por inundação na apólice multirriscos habitação?" (What are the coverage limits for flood damage in the multi-risk home policy?) → RAG retrieves relevant policy wording sections → generates answer with specific coverage amounts and policy clause citations. Search interface: document search with AI summarisation — employees searching for documents and getting relevant results with AI-generated summaries. Branch interface: simplified interface for branch staff — product information queries with customer-appropriate language. Compliance interface: compliance team interface — regulatory query answering with regulation-specific citations. Access control: role-based — claims handlers seeing claims-relevant documents, underwriters seeing underwriting documents, compliance seeing regulatory documents. Sensitive: certain documents (HR policies, board minutes) restricted to authorised roles. (4) Integration: connecting to existing systems. SharePoint: real-time indexing of SharePoint documents — new documents automatically processed and added to RAG knowledge base. Claims system: RAG accessible from within the claims management system — claims handler clicking "AI Assist" while processing a claim. Email: regulatory circulars received via email automatically ingested into the knowledge base — compliance updates available immediately. CRM: customer service agents accessing RAG from within CRM — product information available alongside customer record. (5) Quality and compliance: ensuring accuracy. Accuracy: every RAG response including source citations — document name, page number, and section. Confidence: system indicating confidence level — high confidence (clear answer from single source), medium (synthesised from multiple sources), and low (limited relevant information found). Disclaimer: all responses including standard disclaimer — "This information is based on indexed documents. Please verify critical decisions with the relevant department." Version control: superseded documents marked — RAG preferring current versions and noting when information may have changed. GDPR: data processing records, access logs, and right-of-access capability (users can see what data the system holds about them). Audit trail: complete query and response logging — available for compliance review.
Results over 8 months. Claims efficiency: document search time from 22 minutes to 3 minutes per claim (86% reduction). Claims handlers: 2,475 hours/day saved in search time — equivalent to 309 FTE. Claims quality: overturned decisions from 4% to 1.2% (70% reduction) — handlers finding all relevant provisions. Annual saving: overturned decision reduction saving approximately €2.8M/year (fewer corrections, fewer customer complaints, fewer ombudsman cases). Compliance: regulatory query response from 2-3 hours to 5 minutes. Compliance team: freed from routine questions to focus on proactive compliance monitoring. Regulatory updates: new ASF circulars available in the knowledge base within 2 hours of receipt — from days/weeks for email distribution to reach all employees. Compliance accuracy: regulatory questions answered with specific circular citations — 98% accuracy verified by compliance review. Customer service: product information search from 4 minutes to 30 seconds. Customer hold time: reduced by 65% for information-related calls. Branch consistency: all branches receiving the same product information — answer consistency from 72% to 97% across branches. Customer satisfaction: CSAT for information-related interactions from 68% to 88%. Knowledge preservation: 45,000 documents now permanently searchable — institutional knowledge no longer dependent on individual employees. New employee: new employees productive in 2 weeks (from 6 weeks) — RAG providing instant access to institutional knowledge. Employee retirement: knowledge preserved — retiring employees' accumulated expertise captured in documents now accessible through RAG. Usage: 4,200 queries/day across 3,200 employees — average 1.3 queries per employee per day. Adoption: 89% of employees using the RAG system at least weekly. Most-queried: claims procedures (35%), product information (28%), regulatory requirements (18%), and operational procedures (19%). Financial: annual value — claims efficiency €5.2M (search time saving), claims quality €2.8M (reduced overturned decisions), compliance efficiency €420K (compliance team time), customer service €380K (reduced hold time), training €290K (faster onboarding). Total annual value: €9.09M. Development cost: €220,000 (6-week project — document processing, RAG pipeline, interfaces, and integration). Annual operation: €84,000 (infrastructure, LLM API, and maintenance). ROI: 41x return in the first year.
Ingest, chunk, embed, and index your documents for fast, accurate retrieval with any LLM.
Internal knowledge systems that let employees search across wikis, SOPs, contracts, and Slack history.
Give your customers an AI assistant that answers product questions using your documentation and help center.
Pull data from PDFs, web pages, databases, APIs, Google Drive, Notion, Confluence, and more.
Every answer includes source citations so users can verify and trust the information.
Continuously improve retrieval quality with evaluation frameworks, feedback loops, and reranking.
Lisbon's RAG development market includes AI platform vendors (Microsoft Azure AI, AWS Bedrock — platform-embedded RAG with limited Portuguese optimisation), global consultancies (Accenture, Deloitte — enterprise AI at premium rates), Portuguese tech companies (some Portuguese AI companies exploring RAG — early-stage), and open-source tools (LangChain, LlamaIndex — powerful but requiring significant engineering to implement well). ZTABS provides RAG development with Portuguese language depth — European Portuguese embedding optimisation, Portuguese document processing, and Portuguese answer generation — combined with enterprise architecture, GDPR compliance, and domain-specific knowledge engineering.
RAG development in Lisbon ranges from €30,000-80,000 for focused RAG systems (single knowledge domain, defined document set, 3-5 weeks) to €80,000-250,000 for enterprise RAG platforms (multi-domain, 10,000+ documents, access controls, system integration, 5-8 weeks). Annual operation: €5,000-20,000/month (LLM API + infrastructure + knowledge base maintenance). ZTABS provides RAG with Portuguese language and GDPR expertise.
Verified reviews from real client engagements — sourced from our public testimonial archive and Clutch profile.
My experience is throughout positive. Communication, service, the short response times and the flawless execution of a challenging topic was absolutely great. ZTABS is definitely my first choice again.
Christian Neff
Bank Software Advisory · Bank Software Advisory
Fintech
Fantastic Agency! I couldn't fault them even if I tried. They always go above and beyond to meet your expectations and always produces quality work. Thank you ZTABS.
Stephanie Kal
CEO · Beauty Finder Australia
Marketplace
It has been great working with ZTABS. They bounce off the ideas along the way. Amazing Experience.
Joel Rowe
CEO · Drill Quoter
Marketplace
We don't just contract — we ship and operate our own software. 17 products in production.
Common questions about RAG & knowledge systems for Lisbon businesses
Yes — Portuguese document processing is central to our RAG implementation. PDF extraction with Portuguese OCR, Portuguese-optimised embeddings for accurate retrieval, and Portuguese answer generation. Diacritics (ã, õ, ç) handled correctly throughout the pipeline. European Portuguese prioritised over Brazilian.
ZTABS is a remote-first RAG & knowledge systems agency serving Houston businesses — including custom rag pipelines, enterprise knowledge bases, customer-facing ai search. We work with Energy & Oil/Gas, Healthcare & Biotech, Aerospace & Defense companies in Houston, TX via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
RAG & Knowledge Systems in New York, NYZTABS is a remote-first RAG & knowledge systems agency serving New York businesses — including custom rag pipelines, enterprise knowledge bases, customer-facing ai search. We work with Finance & Fintech, Media & Advertising, Fashion & Retail companies in New York, NY via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
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Web Development in Lisbon, PortugalZTABS is a remote-first web development agency serving Lisbon businesses — including full-stack development, progressive web apps, api development. We work with Tourism Tech, FinTech, PropTech companies in Lisbon, Portugal via timezone-aligned engineers and async workflows; we do not have a local office, and we are explicit about that with every client.
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