RAG as a Service

Frequently Asked Questions

• Q:

  What is retrieval-augmented generation and what RAG systems does Azumo build?

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  Retrieval-augmented generation (RAG) is an AI architecture that grounds LLM outputs in your actual documents, databases, and knowledge bases instead of relying solely on the model's training data. This eliminates hallucination on factual queries and provides source attribution for every answer. Azumo builds production RAG systems for enterprise knowledge search, customer support automation, document Q&A, compliance research, internal knowledge management, and AI-powered search tools. We built an AI-powered supplier search tool for Meta that uses NLP and RAG to parse unstructured vendor data across a massive database. Our RAG stack includes vector databases like Pinecone, Weaviate, Chroma, and Qdrant, embedding models from OpenAI and open-source alternatives, and LLMs from OpenAI, Anthropic Claude, LLaMA, and Mistral. SOC 2 certified with nearshore teams across Latin America.

• Q:

  Why should a company build a RAG system instead of fine-tuning an LLM?

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  RAG and fine-tuning solve different problems and Azumo often combines both. RAG is the right choice when your knowledge base changes frequently (weekly or daily), when you need source citations for every answer, when traceability is a compliance requirement, or when you cannot afford to retrain a model each time data updates. Fine-tuning is better for teaching a model new behaviors, output formats, or domain-specific reasoning patterns that RAG alone cannot address. RAG keeps data current without retraining costs. Fine-tuning embeds deep domain understanding into the model itself. The hybrid approach fine-tunes a model for your domain's style and reasoning, then uses RAG to inject current knowledge at query time. This is what Azumo recommends for most enterprise deployments where both accuracy and freshness matter.

• Q:

  What are the key components of a production RAG system?

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  A production RAG system has five core components: a document ingestion pipeline that chunks, cleans, and processes source documents from PDF, Word, HTML, Confluence, SharePoint, Slack, Google Drive, and databases; an embedding model that converts text into vector representations; a vector database that stores and retrieves embeddings at scale; a retrieval layer that finds the most relevant chunks for each query using semantic search, keyword search, or hybrid approaches; and a generation layer where an LLM synthesizes retrieved context into a coherent answer with citations. Azumo adds metadata filtering for access control, re-ranking with cross-encoder models for improved precision, hybrid search combining dense and sparse retrieval, and citation generation that links every claim to its source document and page number.

• Q:

  What vector databases, embedding models, and LLMs does Azumo use for RAG?

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  For vector storage: Pinecone for managed cloud, Weaviate for hybrid search, Chroma for lightweight deployments, Qdrant for high-performance self-hosted, and pgvector for teams that want to stay in PostgreSQL. Selection depends on scale, latency targets, and infrastructure preferences. For embeddings: OpenAI text-embedding-3-large, Cohere Embed v3, and open-source models from Hugging Face including BGE-M3 and E5-large-v2. We benchmark embedding models against your actual data to find the best accuracy-cost tradeoff. For LLMs: OpenAI GPT-4o, Anthropic Claude, LLaMA 3, and Mistral, selected based on context window requirements, reasoning quality, and cost per token. Valkyrie, our AI infrastructure platform, provides unified access to all models through a single REST API.

• Q:

  How does Azumo handle document ingestion and chunking for RAG?

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  Document ingestion quality determines RAG accuracy. Azumo builds custom ingestion pipelines for PDF, Word, HTML, Markdown, Confluence, SharePoint, Slack, Google Drive, and relational databases. Our chunking strategies go beyond naive text splitting. We use semantic chunking that preserves paragraph and section boundaries, hierarchical chunking that maintains parent-child document structure, sliding window overlap that prevents information loss at chunk boundaries, and table-aware parsing that keeps structured data intact. We extract and preserve metadata including document title, author, date, section headers, page numbers, and access permissions for filtering and citation. Figures and diagrams receive OCR processing. We support multilingual content and validate chunk quality through automated retrieval tests before going to production.

• Q:

  How long does it take to build a production RAG system?

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  A proof-of-concept RAG system over a small document set can be delivered in 1-2 weeks. Production-ready RAG with enterprise data sources, security controls, and monitoring typically takes 2-4 months. Timeline depends on number and variety of data sources, document processing complexity, accuracy requirements, and integration scope. The longest phase is usually document ingestion and chunking optimization: achieving production-grade retrieval accuracy requires iterative testing against representative queries from your actual users. Azumo accelerates delivery with pre-built ingestion connectors for common enterprise systems, established evaluation frameworks using metrics like recall@k and faithfulness, and Valkyrie for model routing. Our nearshore teams work in US time zones with daily standups.

• Q:

  How do you measure and improve RAG system accuracy?

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  We evaluate RAG systems on retrieval quality and generation quality separately. Retrieval metrics include recall@k (are the right documents found?), precision@k (are irrelevant documents excluded?), and mean reciprocal rank (how high do correct results appear?). Generation metrics include faithfulness (is every claim supported by retrieved context?) and answer relevance (does the response address the query?). We build domain-specific evaluation datasets with known correct answers and source documents. Improvement techniques include chunking optimization, embedding model selection and fine-tuning, hybrid retrieval tuning between dense and sparse search, re-ranking with cross-encoders, and prompt engineering. We use LangSmith and custom dashboards for continuous production monitoring, tracking retrieval hit rates and answer quality to detect degradation as your knowledge base grows.

• Q:

  What security and compliance does Azumo implement for RAG systems?

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  Azumo is SOC 2 certified and implements document-level access controls in RAG systems. This means the same system can serve different user roles without exposing restricted documents: a manager and an analyst query the same knowledge base but retrieve only content matching their permission level. We encrypt all document embeddings and source content at rest and in transit using AES-256. For regulated industries, we implement HIPAA-compliant document handling with audit trails, GDPR data minimization and right-to-deletion support, and PCI-DSS controls for financial data. Every query, retrieved document, and generated answer is logged for compliance audit. PII detection prevents sensitive information from appearing in generated answers. We deploy RAG infrastructure within your private cloud, VPC, or on-premises when data sovereignty requires it.