RAG Development Services

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 only on the model's training data. This reduces hallucination on factual queries and provides source attribution for every answer. Azumo builds production RAG 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 stack includes vector databases like Pinecone, Weaviate, Chroma, and Qdrant, embedding models from OpenAI and open-source alternatives, and LLMs from OpenAI, 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 right when your knowledge base changes frequently, when you need source citations for every answer, when traceability is a compliance requirement, or when you cannot retrain a model each time data updates. Fine-tuning is better for teaching a model new behaviors, output formats, or domain-specific reasoning that RAG alone cannot address. RAG keeps data current without retraining costs, while fine-tuning embeds deep domain understanding into the model. The hybrid approach fine-tunes for your domain's style and reasoning, then uses RAG to inject current knowledge at query time, which is what we recommend for most enterprise deployments.

• 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 and processes sources from PDF, Word, HTML, Confluence, SharePoint, Slack, Google Drive, and databases; an embedding model that converts text into vectors; a vector database that stores and retrieves embeddings at scale; a retrieval layer that finds the most relevant chunks using semantic, keyword, or hybrid search; and a generation layer where an LLM synthesizes retrieved context into a cited answer. Azumo adds metadata filtering for access control, re-ranking with cross-encoder models for precision, hybrid search combining dense and sparse retrieval, and citation generation that links every claim to its source document and page.

• Q:

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

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  For vector storage we use Pinecone for managed cloud, Weaviate for hybrid search, Chroma for lightweight deployments, Qdrant for high-performance self-hosted, and pgvector for teams staying in PostgreSQL. Selection depends on scale, latency, and infrastructure preferences. For embeddings we use OpenAI text-embedding-3-large, Cohere Embed v3, and open-source models like BGE-M3 and E5-large-v2, benchmarked against your data to find the best accuracy-cost tradeoff. For LLMs we use GPT-4o, Claude, LLaMA 3, and Mistral, selected by context window, reasoning quality, and cost per token. Valkyrie 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 pipelines for PDF, Word, HTML, Markdown, Confluence, SharePoint, Slack, Google Drive, and relational databases. Our chunking goes beyond naive splitting: semantic chunking that preserves section boundaries, hierarchical chunking that maintains parent-child structure, sliding-window overlap that prevents information loss at boundaries, and table-aware parsing that keeps structured data intact. We extract and preserve metadata including title, author, date, section headers, page numbers, and access permissions for filtering and citation. Figures and diagrams receive OCR, and we support multilingual content and validate chunk quality through automated retrieval tests before 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 RAG with enterprise data sources, security controls, and monitoring typically takes 2-4 months. Timeline depends on the number and variety of data sources, document processing complexity, accuracy requirements, and integration scope. The longest phase is usually ingestion and chunking optimization, since production-grade retrieval accuracy requires iterative testing against representative queries from your users. Azumo accelerates delivery with pre-built ingestion connectors, established evaluation frameworks using metrics like recall@k and faithfulness, and Valkyrie for model routing. Our nearshore teams work in US time zones.

• Q:

  How do you measure and improve RAG system accuracy?

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  We evaluate retrieval and generation quality separately. Retrieval metrics include recall@k, precision@k, and mean reciprocal rank. Generation metrics include faithfulness (is every claim supported by retrieved context?) and answer relevance. 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 catch 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, so the same system serves different roles without exposing restricted content: a manager and an analyst query the same knowledge base but retrieve only what their permissions allow. We encrypt all embeddings and source content at rest and in transit using AES-256. For regulated industries, we implement HIPAA-compliant handling with audit trails, GDPR data minimization and right-to-deletion, and PCI-DSS controls for financial data. Every query, retrieved document, and generated answer is logged for audit, and PII detection prevents sensitive information from appearing in answers. We deploy within your private cloud, VPC, or on-premises when data sovereignty requires it.