RAG Development Services

What is RAG development?

RAG (retrieval-augmented generation) development is the work of building AI systems that first search your own content, such as documents, wikis, help-center articles, tickets and databases, and then give the most relevant passages to a large language model so it can answer from them. The result is an assistant that answers questions about your business accurately, cites its sources and stays current without retraining a model. Ryven designs, builds and maintains RAG systems for companies in the US, UAE, UK and Australia.

What we build with RAG

Most RAG projects fall into one of these patterns. We usually start with the one that answers the most repeated questions or saves the most hours.

• Internal knowledge assistants: staff ask questions in Slack, Teams or a web app and get answers from policies, SOPs, wikis and past projects.
• Customer support chatbots: grounded in your help center, product docs and past tickets, with handover to a person when the answer isn't clear.
• Document Q&A: search and question contracts, proposals, technical manuals and reports, with the exact source passage shown.
• AI search for websites and apps: natural-language search across your catalogue, listings or content library.
• Sales and proposal assistants: draft answers to RFPs and client questions from approved past responses.
• RAG-powered AI agents: assistants that look up information and then take action, such as creating a ticket or updating your CRM.

How much does RAG development cost?

At Ryven, a focused RAG assistant over one or two knowledge sources typically costs $3,000–$10,000 and goes live in 2–4 weeks. Multi-source systems with user permissions, integrations, evaluation dashboards and multiple languages typically run $10,000–$30,000+. After launch, running costs are mainly AI model usage and vector database hosting, which for most small and mid-size teams is tens to a few hundred dollars a month. You get a fixed-price proposal after a free scoping call.

Our RAG development process

Every build follows the same six steps, so you can see accuracy improve before anything goes live.

• Discovery and data audit: we list the questions users actually ask and the sources that hold the answers, then check their quality and access rules.
• Ingestion pipeline: documents are cleaned, split into passages and indexed in a vector database, with automatic syncing when content changes.
• Retrieval tuning: hybrid keyword and semantic search, metadata filters and re-ranking, so the right passage is found and not just a similar one.
• Answers and guardrails: prompts that require citations, refuse to guess when the answer isn't in your content, and keep answers on-brand.
• Evaluation: we test against a set of real questions and measure answer accuracy and retrieval quality before launch.
• Deployment and monitoring: go live on your website, app, Slack, Teams or WhatsApp, with logs, feedback buttons and monthly accuracy reviews.

RAG tech stack

We choose tools to fit your data, budget and hosting requirements rather than forcing one stack.

• Language models: OpenAI GPT, Anthropic Claude, Google Gemini, and open-source models such as Llama and Mistral for self-hosted deployments.
• Vector databases: pgvector (PostgreSQL), Pinecone, Qdrant, Weaviate and Chroma.
• Frameworks and orchestration: LangChain, LlamaIndex, and n8n for syncing data and triggering actions.
• Data sources: Google Drive, SharePoint, Notion, Confluence, Zendesk, HubSpot, websites, PDFs and SQL databases.

RAG vs fine-tuning: which do you need?

Choose RAG when your information changes often, when answers must cite a source, or when different users should see different documents. Choose fine-tuning when you need a model to adopt a specific style, format or narrow skill that prompting alone can't achieve. Most business assistants need RAG first; fine-tuning is an optional later step, and the two can be combined.

Is our data safe with a RAG system?

It is when the system is designed for it. We use AI providers' business API terms, under which your data isn't used to train their models, and we can deploy in your own cloud or with self-hosted open-source models when data can't leave your environment. Access controls are applied at retrieval time, so people only get answers from documents they're allowed to see, and personal data can be redacted before indexing. We agree the data architecture with you during discovery, in line with GDPR and UAE data protection requirements.

Why companies choose Ryven for RAG

We build RAG as part of complete AI systems, not isolated demos. The same team builds AI agents, n8n automations and custom software, so your assistant can connect to the tools your team already uses and take action, not just answer. Every project ships with documentation, a handover session and an accuracy report, and you own the code and the data pipeline.