Service

RAG Development Services

Our RAG development services help businesses connect AI applications with their own documents, data and organisational knowledge. We design and develop custom RAG solutions that retrieve relevant information from trusted business sources and use it to provide more context-aware AI responses. From enterprise knowledge systems and intelligent document search to RAG integration and optimisation, we build solutions around your existing data, systems and business requirements. This page explains how RAG works, when to use it, what solutions we can build and how our RAG development process works.

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What Is Retrieval-Augmented Generation (RAG)?

A Retrieval-Augmented Generation (RAG), magyarul kereséssel kiegészített szöveggenerálás, olyan mesterségesintelligencia-megközelítés, amely összekapcsolja a nagy nyelvi modelleket a vállalat saját adataival és tudásanyagával. Ahelyett, hogy az AI kizárólag a betanítása során megszerzett ismeretekre támaszkodna, a RAG rendszer a válaszadás előtt releváns információkat keres ki dokumentumokból, adatbázisokból, tudásbázisokból és más üzleti rendszerekből.

A vállalati RAG megoldások megkönnyítik a belső tudásanyag elérését és felhasználását az AI-alapú alkalmazásokon keresztül. Azáltal, hogy a válaszokat releváns üzleti információkra alapozzák, a RAG rendszerek pontosabb kontextust biztosíthatnak, és hasznosabb válaszokat adhatnak a munkatársaknak, ügyfeleknek és más felhasználóknak.

When Does Your Business Need a RAG Solution?

A RAG solution becomes valuable when your business has large amounts of useful information, but employees or customers struggle to find the right answers quickly. Important knowledge may be spread across documents, databases, knowledge bases and different internal systems.

RAG connects AI with these trusted business sources, allowing users to access relevant information through natural language instead of manually searching across multiple systems.

Your Business Knowledge Is Scattered Across Multiple Sources

Important information is stored across documents, databases, knowledge bases and internal systems, making it difficult to find what is needed quickly.

Employees Spend Too Much Time Searching for Information

Teams repeatedly search through files, documentation and internal platforms to find answers that already exist somewhere in the organisation.

Generic AI Cannot Answer Questions About Your Business

Standard AI models do not automatically understand your internal data, processes or company-specific knowledge. RAG gives AI access to relevant business information when it needs it.

You Need AI Answers Grounded in Your Own Data

Enterprise RAG solutions can retrieve relevant information from approved business sources before generating an answer, helping create more context-aware and reliable AI applications.

What Problems Can RAG Solve?

Scattered Business Knowledge

Important information is often spread across documents, databases, knowledge bases and different business systems. RAG can bring this knowledge together and make it easier to access.

Time-Consuming Information Search

Employees can spend significant time searching through files and internal systems. RAG helps users find relevant information through natural-language questions.

AI Without Business Context

General AI models do not automatically know your organisation’s internal information. RAG provides relevant business context when generating responses.

Large Volumes of Documents

Finding specific information across thousands of documents can become difficult and inefficient. RAG can retrieve relevant content without users having to search through files manually.

Inconsistent Access to Internal Knowledge

Valuable knowledge may depend on specific employees or teams. RAG can make approved organisational knowledge more accessible across the business.

Outdated or Irrelevant AI Responses

Business information changes over time. RAG systems can retrieve information from maintained data sources, helping AI applications work with more relevant and up-to-date business knowledge.

Our RAG Development Services

RAG fejlesztési szolgáltatásaink lefedik a vállalati adatokra épülő, kereséssel kiegészített szöveggeneráló rendszerek tervezésének, fejlesztésének és integrálásának teljes folyamatát. Az egyedi RAG-alkalmazásoktól a meglévő rendszerek integrációjáig olyan megoldásokat fejlesztünk, amelyek a vállalati tudásanyagot könnyen elérhető és hasznos AI-alapú alkalmazásokon keresztül teszik felhasználhatóvá.

Custom RAG Solution Development

We design and develop RAG solutions tailored to your business requirements, data sources and use cases, from initial architecture to production-ready implementation.

Enterprise RAG Development

We build enterprise RAG solutions designed to work with complex business knowledge, multiple data sources and existing enterprise systems while considering security and access requirements.

RAG Integration With Existing Systems

We integrate RAG capabilities into existing web applications, internal platforms, business software and AI solutions without requiring you to replace your current systems.

Knowledge Base & Data Source Integration

We connect RAG systems with documents, databases, knowledge bases, APIs and other relevant data sources so AI applications can retrieve the information they need.

RAG Search & Retrieval Optimisation

We improve how information is indexed, retrieved and provided to the language model to increase the relevance and usefulness of generated responses.

RAG as a Service

Our RAG as a service solutions provide a managed approach to deploying and operating RAG systems. We support data ingestion, indexing, monitoring and ongoing optimisation to help keep the system reliable and effective as your business data and requirements evolve.

What Types of RAG Solutions Can We Build?

RAG can support a wide range of business applications where users need fast and reliable access to information stored across documents, databases and internal systems. We build RAG solutions around your existing knowledge sources, business requirements and the people who need to use them.

Enterprise Knowledge Management Systems

We develop AI-powered enterprise knowledge management systems that make information stored across documents, databases and internal platforms easier to find, access and use across your organisation.

Internal Knowledge Assistants

We build AI assistants that allow employees to ask questions and retrieve relevant information from company policies, procedures, documentation and other approved internal knowledge sources.

Intelligent Document Search & Q&A

We create RAG-powered search and question-answering systems that help users find specific information across large collections of documents using natural-language questions.

Customer Support Knowledge Assistants

We develop AI assistants that retrieve information from product documentation, FAQs, support materials and other approved sources to help customers or support teams find relevant answers faster.

Technical Knowledge Assistants

We build RAG solutions for accessing technical documentation, manuals, specifications and engineering knowledge, making complex information easier for teams and users to navigate.

Policy & Compliance Knowledge Systems

We develop RAG-powered systems that help users find relevant information across internal policies, guidelines, procedures and compliance documentation while keeping responses grounded in approved sources.

RAG vs. Standard LLM Applications: What Is the Difference?

STANDARD LLM APPLICATION

RAG-ENHANCED APPLICATION

Primarily uses the model’s existing knowledge and the information provided in the prompt

Retrieves relevant information from connected business data before generating a response

Works well for general tasks such as content generation, summarisation and rewriting

Works well for tasks that require access to company-specific knowledge

Does not automatically have access to your internal documents, databases or knowledge bases

Can retrieve information from documents, databases, knowledge bases and other connected sources

Relies on the context provided directly to the model

Dynamically retrieves relevant context based on each user request

Best suited to tasks that do not depend on proprietary or frequently changing information

Best suited to knowledge-intensive applications where business information matters

Azure OpenAI

Anthropic Claude

Google Gemini

Hugging Face

LlamaIndex

Mistral AI

Pinecone

PyTorch

TensorFlow

Caspar

Caspar Cloud is a case management system built for organisations that are looking after the elderly, sick or other people who cannot take care of themselves.

Custom Software Development • IT Staff Augmentation • United Kingdom • Healthcare

Fyld

Fyld enables fieldworkers to survey job sites with their smartphone or tablet, upload pictures, audio, and video content to the system.

IT Staff Augmentation • United Kingdom • Infrastructure

Grandio

Grandio is a web and mobile application for a Hungarian asset management firm that provides secure access to portfolio values and related data, with RabIT delivering ongoing support, shared QA, and agile development packages.

Custom Software Development • Mobile Application Development • Web Application Development • Hungary • Advisory and Financial Services

Rentkey

Rentkey is a modern, scalable bilingual web application built for international markets, designed to efficiently manage complex business workflows, document handling, and automated processes within a single platform through Scrum-based development.

Web Application Development • Hungary • Real Estate

Our RAG Development Process

Business Needs & Use Case Discovery

We identify the business problem, target users and use cases to define what the RAG solution needs to achieve.

Data Source Assessment

We review the documents, knowledge bases, databases and other information sources the RAG system will need to access.

RAG Architecture & Solution Design

We design the retrieval architecture, select the appropriate AI technologies and define how the solution will integrate with your existing systems.

RAG Development & Integration

We build the retrieval pipeline, connect the required data sources and integrate the RAG capabilities into the application or business environment.

Testing & RAG Evaluation

We evaluate retrieval quality, response relevance and system behaviour using realistic queries and business scenarios, then refine the solution where needed.

Deployment & Continuous Improvement

We deploy the solution into the target environment and support ongoing monitoring, optimisation and improvements as your data and requirements evolve.

Frequently Asked Questions

Still have questions?

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RAG development is the process of building AI applications that combine large language models with external data sources such as company documents, databases and knowledge bases. The system retrieves relevant information before generating a response, allowing the AI to work with business-specific knowledge rather than relying only on the model’s existing knowledge.

RAG works by searching connected data sources for information relevant to a user's request and providing that information to a large language model as additional context. The LLM then uses the retrieved information to generate a response grounded in the available business knowledge.

RAG development services include the design, development, integration, testing and optimisation of retrieval-augmented generation systems. They can cover data source integration, retrieval architecture, vector search, LLM integration, RAG evaluation and deployment into existing applications or business systems.

A standard LLM application primarily relies on the model's existing knowledge and the context provided directly to it, while RAG retrieves additional information from connected sources before generating a response. RAG is therefore particularly useful when an AI application needs access to company-specific, proprietary or frequently updated information.

RAG can help businesses improve access to scattered knowledge, reduce time spent searching through documents, make large document collections easier to use and provide AI applications with company-specific context. Common use cases include internal knowledge assistants, enterprise search, document Q&A, customer support and technical knowledge systems.

Yes. RAG solutions can be designed to retrieve information from existing sources such as documents, knowledge bases, databases, APIs and enterprise platforms. The appropriate integration approach depends on where the data is stored, how it is structured and what access controls need to be maintained.

Enterprise RAG solutions are retrieval-augmented generation systems designed for organisational use, where AI applications need to work with business-specific information across multiple data sources. Enterprise implementations may also need to account for user permissions, security requirements, data governance, system integrations and changing business knowledge.

Yes. RAG can be used as part of an enterprise knowledge management system to help employees find and interact with information stored across documents, knowledge bases and other internal sources. Instead of manually searching multiple locations, users can ask natural-language questions and receive responses based on relevant organisational knowledge.

RAG as a service is a managed approach to implementing and operating retrieval-augmented generation capabilities. Depending on the service model, this can include RAG infrastructure, data ingestion and indexing, retrieval pipelines, monitoring, maintenance and ongoing optimisation as business data and requirements change.

RAG systems should be evaluated by testing both retrieval quality and generated responses. This includes checking whether the system retrieves the correct information for representative questions, whether responses are grounded in that information, and how the system behaves when relevant information is missing. Evaluation and testing should continue as data sources, models and business requirements change.

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Contact Information

Let’s discuss your project!

Sales

+44 20 3807 2835 - United Kingdom

+1 (424) 238-7060 - United States

+36 70 620 1660 - Hungary

[email protected]

California, 90401 Santa Monica, Santa Monica Blvd Suite 222.

Hungary, 6724 Szeged, Cserzy Mihály street 30/B

Hungary, 7623 Pécs, Tüzér street 13.

Hungary, 1065 Budapest, Bajcsy-Zsilinszky Blvd 57.