Retrieval-augmented generation (RAG) setup services

Large language models sound smart, but without real-time data, they often miss the mark. Retrieval-augmented generation (RAG) solves this by connecting AI with live, trusted information making outputs accurate, relevant, and grounded.

At Deveit, we offer full-cycle RAG setup — designing and deploying systems that work in production, not just in demos. From architecture to rollout, every pipeline is tailored to help your AI generate responses based on knowledge.

Our retrieval augmented generation services

We help companies implement RAG services that combine fast retrieval with high-quality generation. Whether you’re building an internal tool or a customer-facing assistant, we make sure your system stays informed, relevant, and ready for real-world use.

  • End-to-end RAG system development

We build full-cycle RAG solutions, covering everything from retriever architecture to LLM prompt injection. Each solution is designed to integrate with your environment — whether you’re starting from scratch or evolving an existing product.

  • Integration with external knowledge bases

Good answers depend on good data. We connect RAG pipelines to your document stores, APIs, or knowledge bases to ensure retrieval always pulls from reliable, up-to-date content. For example, ecommerce clients often pair this with ecommerce integration services to keep product info consistent across platforms.

  • Custom retrieval layer configuration

With our custom RAG development services, we build the retrieval layer as a standalone system — using dense or hybrid search, custom filters, and flexible indexing strategies tailored to your data. If needed, we can help you connect this RAG pipeline to an existing LLM agent or API, enabling relevant responses from your internal knowledge base.

  • Domain-specific knowledge base preparation

RAG systems are domain-agnostic, but the way you prepare and filter data makes a difference. We help structure your knowledge base, apply retrieval filters, and define which sources are indexed. That way, users get more relevant and precise results, even in complex or highly specialized fields like healthcare, legal, or fintech.

  • Ongoing support and performance optimization

As your data grows, your AI system should grow with it. We offer continuous support, including infrastructure scaling, latency improvements, retriever audits, and feature extensions. We’re your long-term RAG agency.

How we build RAG systems

Our development process focuses on clarity, control, and success. We make sure every step — from data ingestion to deployment — aligns with your technical landscape and business goals.

  • Data ingestion and preprocessing

We begin by preparing your data for retrieval. That includes cleaning, chunking, tagging, and formatting so the retriever can work efficiently, even at scale.

  • Embedding generation and indexing

Next, we convert your content into vector embeddings using tools like SentenceTransformers or OpenAI Embeddings. Then we build fast indexes (with FAISS, Qdrant, or Weaviate) for real-time access.

  • Retrieval pipeline setup

We define how the system fetches relevant information. Whether using top-k logic, hybrid search, or metadata filtering, we ensure precision and speed in every query.

  • RAG utility exposure and integration

We expose the retrieval layer as a standalone utility that can be called from your application logic, agent, or orchestration system. It returns relevant, ranked content from your knowledge base — ready to be consumed by a language model or another component. Whether you’re using hosted LLMs (OpenAI, Anthropic) or local ones (LLaMA, Mistral), our setup ensures your app can retrieve the right information at the right time.

Need full-stack implementation? We integrate with your digital tools using our custom web development service.

  • Testing, evaluation, and deployment

Before go-live, we test response accuracy, retrieval relevance, latency, and more. Once it’s solid, we deploy to your environment with CI/CD and performance monitoring included.

Advantages of RAG

Why choose retrieval-augmented generation? Because it gives your AI what most models still lack — real grounding in actual data.

Access to real-time, relevant knowledge

Unlike static models, AI RAG retrieves live context at the moment of the query. That means answers that aren’t just fluent — they’re accurate, specific, and grounded.

Lower computational cost vs. large LLMs

Fine-tuning massive models isn’t always practical. With RAG artificial intelligence, you don’t need to retrain — you retrieve. It’s faster, more efficient, and fits naturally into existing pipelines.

Customization with private data sources

Need your AI to use internal docs, customer data, or live product info? With RAG as a service, we connect your system directly to private sources — securely and without touching the core model.

Better context-awareness for responses

Dynamic retrieval leads to better answers. Whether it’s a chatbot or a research assistant, users get responses with depth, detail, and fewer hallucinations.

Businesses we work with

Startups and small businesses

We help startups move quickly and intelligently. Lightweight RAG pipelines offer powerful functionality without enterprise overhead. Many pair this with b2b lead generation company tools for building smart prospecting assistants or research bots.

Marketing and creative agencies

Agencies use RAG to create real-time campaign insights, automate research, and support clients with on-demand AI tools. We also offer digital agency IT support to manage analytics, performance tracking, and infrastructure.

Medium-sized businesses and enterprises

For larger teams, we implement secure, multilingual RAG systems that integrate with internal tools, customer data, and knowledge bases — all while staying compliant and efficient.

Answers to frequently asked questions

What is RAG and how does it work in real-world applications?

RAG (Retrieval-augmented generation) is a method of combining search with language generation. The system retrieves relevant context like documents or structured data, before passing it to a language model, allowing the output to reflect real knowledge rather than general assumptions.

How much does an RAG development services cost?

Costs vary based on scope. Entry-level systems start around $7,000, while more complex implementations involving private data, open-source LLMs, and multilingual support scale from there. We tailor pricing to fit your goals and infrastructure.

Why choose Deveit as your RAG development services company?

We combine deep AI expertise with product thinking and DevOps know-how. You won’t just get a working prototype, you’ll get a stable, well-integrated solution ready for production. Each RAG service we deliver is built for long-term impact, not just experimentation.

I’m interested. How do I get started?

It’s easy. Let’s start with a quick call. Tell us what you’re building, or what you’re stuck on and we’ll map out a clear, realistic plan for getting it done.

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Looking for a retrieval augmented generation company you can rely on? Let’s build something that works together.