Deploy Retrieval-Augmented Generation to ground AI responses in your company's actual knowledge base. Boaweb AI builds enterprise RAG architectures that deliver accurate, verifiable, and up-to-date AI answers.
LLMs confidently generate plausible-sounding but incorrect information when they lack knowledge. For customer support, technical documentation, or compliance scenarios, hallucinations create legal liability, erode customer trust, and force expensive human verification of every AI response.
Pre-trained models have knowledge cutoff dates, often 6-18 months behind current reality. They can't access your latest product updates, policy changes, pricing, or organizational knowledge. This makes them useless for applications requiring current information.
Your competitive advantage lives in internal documentation, customer histories, technical specifications, and institutional knowledge. Standard LLMs can't access this data, severely limiting their business value. Uploading sensitive data to third-party models creates compliance and security risks.
Users can't verify AI answers without source citations. Regulated industries require audit trails proving where information originated. Without retrieval mechanisms, LLMs are black boxes—users have no way to validate responses or understand reasoning, making them unsuitable for high-stakes decisions.
We identify and catalog all relevant knowledge sources: documentation systems, wikis, support tickets, CRM data, product specs, compliance documents, and internal communications. Our ingestion pipelines extract, clean, and structure this data for semantic search, handling PDFs, databases, APIs, and unstructured sources.
Deliverables: Knowledge inventory, data extraction pipelines, document processing workflows, metadata schemas
We design and deploy vector databases (Pinecone, Weaviate, or Qdrant) optimized for your scale and latency requirements. Advanced chunking strategies preserve context while enabling precise retrieval. Multi-stage embedding approaches combine dense and sparse retrieval for optimal accuracy across query types.
Deliverables: Vector database infrastructure, chunking strategy documentation, embedding model selection, retrieval performance benchmarks
We implement hybrid search combining semantic similarity with keyword matching, metadata filtering, and reranking algorithms. Query expansion, hypothetical document embeddings (HyDE), and multi-query strategies ensure the system retrieves the most relevant context even for ambiguous or complex questions.
Deliverables: Retrieval algorithm configuration, reranking models, query optimization logic, relevance metrics
Retrieved context is injected into carefully crafted prompts that instruct the LLM to answer based only on provided information. We implement citation mechanisms that link every claim to source documents, confidence scoring to flag uncertain responses, and fallback logic when retrieval yields insufficient context.
Deliverables: RAG prompt templates, citation formatting, confidence thresholds, answer quality evaluation framework
Automated pipelines keep the knowledge base synchronized with source systems in real-time or on scheduled intervals. User feedback loops identify retrieval failures and knowledge gaps. Analytics dashboards track query patterns, retrieval accuracy, and user satisfaction to guide continuous improvement.
Deliverables: Sync automation, feedback collection systems, analytics dashboards, knowledge gap reports, improvement roadmaps
Get our comprehensive technical guide covering vector database selection, chunking strategies, retrieval optimization, and production deployment patterns. Includes code examples and architecture diagrams from real implementations.
Factual accuracy for domain-specific queries vs. 71% for base LLM
Average query response time including retrieval and generation
User satisfaction with cited, verifiable AI responses
A B2B SaaS platform with 50,000+ users needed to scale technical support without proportionally increasing headcount. Their support documentation spanned 2,800 articles across product versions, integrations, and troubleshooting guides. Generic ChatGPT frequently provided outdated or incorrect solutions.
Technical implementation: Pinecone vector database with 12,000+ embedded chunks from support docs, changelog, API reference, and historical tickets. Hybrid search using OpenAI embeddings + BM25 keyword matching. GPT-4 generates responses grounded in top 5 retrieved chunks with inline citations. Real-time sync with Notion documentation system.
Business impact: Support team handled 2.4x ticket volume without additional hiring. Customer satisfaction scores increased from 3.2 to 4.5/5. Self-service resolution rate jumped from 22% to 60%, reducing support costs by €185,000 annually.
Fine-tuning teaches a model new patterns, styles, or behaviors by retraining on custom data. RAG gives a model access to external knowledge at query time without retraining. Use fine-tuning for domain-specific language, tone, or output formatting. Use RAG when you need access to large, frequently updating knowledge bases or want verifiable source citations. Many enterprise solutions combine both—fine-tuned models with RAG retrieval for optimal results.
We implement multilingual embedding models (e.g., multilingual-e5 or Cohere's multilingual embeddings) that understand semantic meaning across languages. This allows queries in Swedish to retrieve relevant English documents and vice versa. For generation, we use multilingual LLMs or translation layers. Organizations with significant multilingual content typically see 85-92% cross-lingual retrieval accuracy, enabling truly global knowledge access.
Properly configured RAG systems detect low-confidence retrievals through relevance scoring. When similarity scores fall below thresholds, the system can: acknowledge the knowledge gap ("I don't have information about this in our documentation"), suggest alternative queries, or route to human support. This honest handling of uncertainty is far superior to hallucinations. Analytics identify common unanswerable questions, guiding documentation improvements.
Initial setup ranges from €25,000-€90,000 depending on knowledge base complexity, data sources, and integration requirements. Ongoing costs include vector database hosting (€200-€2,000/month), embedding API calls (€0.0001-€0.0004 per 1K tokens), and LLM generation costs. RAG queries cost 20-40% more than simple LLM calls due to retrieval overhead, but the accuracy improvement and reduced human oversight typically deliver 3-5x ROI within 6-9 months.
Absolutely. Advanced RAG implementations combine semantic search over unstructured text with SQL generation for structured data queries. We build hybrid systems that understand when to retrieve documents vs. query databases. Text-to-SQL capabilities allow natural language queries like "What were Q3 sales in Germany?" to be translated into database queries. The LLM then synthesizes results from both structured and unstructured sources into coherent answers.
Build RAG on top of ChatGPT infrastructure for secure, knowledge-grounded AI.
Learn More →Combine fine-tuned models with RAG for maximum accuracy and customization.
Learn More →Optimize RAG prompts to maximize retrieval quality and answer accuracy.
Learn More →Schedule a RAG architecture consultation with Boaweb AI. We'll assess your knowledge sources, design a custom retrieval pipeline, and provide detailed implementation recommendations with cost projections.