Retrieval-Augmented Generation (RAG) is an advanced AI technique that enhances large language models (LLMs) with real-time access to external data sources. When a user asks LLM a question, RAG system retrieve relevant info from your databases, documents, or APIs, and then feed it into the AI model to give a detailed, niche-tailored, and up-to-date response.
This three-step process — query retrieval + augmentation + full prompt generation — ensures that the user gets:
More accurate answers based on the latest and domain-specific data
Context-aware insights tailored to your business knowledge base
Reduced hallucinations by grounding outputs in verified sources
At Fively, we provide end-to-end RAG development services designed to help businesses unlock the full potential of AI while staying in control of their data. Our services cover everything from preparation to deployment and long-term support:
Data preparation
We clean, structure, and normalize your data to make it RAG-ready. This includes document parsing, metadata enrichment, embeddings generation, and indexing, ensuring your information is optimized for accurate retrieval.
Building the information retrieval system
Our team designs and implements scalable retrieval pipelines (vector databases, semantic search, hybrid search) that allow your AI to access domain-specific knowledge instantly.
RAG model integration into your AI system
We seamlessly connect the retrieval system to large language models (e.g., OpenAI, Anthropic, or custom LLMs) so that responses are context-aware, accurate, and grounded in your own data.
Custom knowledge base development
From legal document repositories to enterprise wikis, we build custom knowledge bases tailored to your business. This ensures your AI assistant or chatbot always works with the right, verified information.
Multimodal RAG implementation
We integrate images, PDFs, audio, and video into your RAG pipeline. This makes your AI capable of retrieving and reasoning over multiple data formats for more advanced use cases.
Consultation and support on RAG services
Our team provides ongoing consulting, monitoring, and fine-tuning to ensure your Retrieval Augmented Generation as a service implementation evolves with your data, users, and business needs.
Fively combines the power of generative AI with access to domain-specific data to enable companies with smarter, more reliable, and industry-tailored solutions. Here’s how it works across industries:
By combining advanced retrieval techniques with generative AI, our RAG solutions help companies achieve higher accuracy, better user experiences, and smarter operations. Here’s what that means for you:
Enhanced accuracy and relevance
Our AI solutions ground every response in real, verified data. By pulling context directly from your knowledge base, the system provides outputs that are not only correct but also highly relevant to your specific domain, reducing misinformation and increasing trust in critical decisions.
Improved user experience
RAG-powered systems allow your users to ask natural questions and get precise answers without digging through endless documents. Whether it’s a customer searching a product catalog or an employee querying internal policies, the result is a faster, smoother, and more intuitive experience that feels personalized and effortless.
Operational efficiency
Instead of wasting hours on manual lookups or repetitive data checks, RAG automates the heavy lifting. Teams spend less time chasing information and more time acting on insights, which translates into leaner processes, reduced costs, and quicker time-to-decision across the organization.
We follow a clear, stepby-step workflow to deliver reliable, scalable, and business-specific RAG solutions. Here’s how we do it:
01
Data collection and preparation
We start by analyzing your business goals, current systems, and data landscape to define the best approach for Retrieval Augmented Generation as a service implementation. Then our team gathers and structures your data — ensuring it’s ready for high-quality retrieval.
02
Retrieval system configuration
We design and configure the retrieval layer (vector databases, semantic or hybrid search) that connects your knowledge base with the AI.
03
LLM system integration
The retrieval system is then integrated with the chosen large language model (OpenAI, Anthropic, or a custom LLM), enabling it to generate grounded, context-aware responses.
04
Prompt design and fine-tuning
We create and refine prompts tailored to your use case, ensuring the model responds accurately and consistently to your users’ queries. Where necessary, we fine-tune the AI model on your domain-specific data, improving performance and reducing irrelevant outputs.
05
Performance evaluation and refinement
We test the system against benchmarks like BERTScore, BLEURT, and METEOR to ensure accuracy, efficiency, and stability. Based on testing results and feedback, we continuously optimize retrieval pipelines, prompts, and integrations for maximum value.
06
Deployment and ongoing support
After deployment, we provide continuous monitoring, scaling, and support to keep your RAG solution secure, efficient, and aligned with your evolving business needs.
We bring together a team of officially certified AI and RAG engineers ready to turn your ideas into reality. Book a call with our experts and let’s discuss your project with top-notch AI experts!
Andrew, our talented data scientist, and the top machine learning engineer, boasts a rich background of AI-powered software projects. He thrives at the confluence of deep machine learning, NLP, LLMs, RNNs, and information retrieval, crafting solid machine learning pipelines for production. His passion extends to developing custom web products that push the limits of modern tech.
Kiryl, who is our top specialist in Cloud solutions development, is known for his collaborative spirit, working closely with engineering and UX teams to bring creative products to life. Driven by a passion for solving client challenges and enhancing customer satisfaction, he excels in developing full-stack applications, as well as venturing into ML and serverless development, always aiming to deliver exceptional and cutting-edge solutions.
With over 10 years of experience, Maksim has contributed his knowledge to numerous well-known companies and startups in sectors like healthcare, insurance, banking, and finance. In his projects, Maksim actively adopts recent advancements in AI, ML, and deep learning, which highlights his depth of knowledge and adaptability in the field.
Tsimafei is our top-level backend data practitioner, who has made his mark across diverse sectors such as cybersecurity, insurance, banking, media, and customer service, leveraging his academic foundation in applied statistics. Skilled in data analytics, Python, and SQL, he has successfully led numerous projects to success, consistently achieving positive business outcomes and enhancing customer experiences.
Hanna Boychenko
AI PM & BA
Being a highly motivated professional both in software engineering and project management, especially in Agile practices, Hanna shines at steering artificial development projects from their inception to launch. Her leadership style enhances our AI developers’ cooperation and surpasses client expectations consistently.
With a profound understanding of quality assurance and a specialty in AI project testing, Ekaterina brings precision and thoughtfulness to ensuring that our AI development services meet the highest standards. Her passion and expertise in identifying and rectifying errors, as well as a methodical approach to test design and execution, guarantee delivering top-notch AI solutions.
To build reliable Retrieval-Augmented Generation solutions, we combine cutting-edge AI models, powerful retrieval engines, and scalable infrastructure. Our team selects the right mix of tools based on your business goals, data types, and performance needs.
Large language models (LLMs)
Data processing & ETL
Search and indexing
Model serving & deployment
Cloud platforms
Fively employs officially certified Artificial Intelligence and RAG engineers. Let’s schedule a call to discuss your project with real experts!
Businesses from different industries and countries choose us when looking for an artificial intelligence software development company, because we are a trustworthy and experienced technology partner.
5+ years
in software development
We know how to utilize technology for business process improvement and existing system optimization.
100+
experienced engineers
We are proficient in Machine Learning, Computer Vision, Deep Learning, and other AI-related technologies.
~85%
are senior specialists
Artificial Intelligence development is sophisticated and requires the expertise of the best data scientists.
70+
successful projects
We successfully complete AI app development projects thanks to experienced developers and project managers.
Fively is a custom software development company, that has been gaining recognition throughout its existence.
Let's have a call and discuss your custom solution.
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Absolutely. We tailor every RAG implementation to your industry, workflows, and data sources. Whether you’re in healthcare, finance, eCommerce, or legal, our solutions adapt to your domain and ensure outputs are relevant, accurate, and compliant.
LLMs are pre-trained models that generate text based on patterns it learned during training. RAGs enhance an LLM by connecting it to an external knowledge base: this means responses are grounded in up-to-date, domain-specific data, rather than relying only on pre-training.
RAG-as-a-Service is a fully managed offering where businesses can leverage RAG without building the infrastructure themselves. It provides ready-to-use retrieval systems, LLM integration, and ongoing support so companies can adopt RAG quickly and focus on business outcomes rather than engineering complexity.
The main challenges when implementing RAG-as-a-service are: data quality issues (unstructured or inconsistent formats); scalability concerns when dealing with large datasets; latency in real-time queries if retrieval isn’t optimized; security & compliance risks when handling sensitive information.
At Fively, we solve these by using custom data pipelines, scalable architectures, and strict security controls.
Fine-tuning adapts the base model by training it further on domain-specific data. It’s powerful but resource-intensive, less flexible, and harder to update. RAG doesn’t retrain the model but instead augments it with real-time retrieval from external data sources. It’s faster to implement, cheaper to maintain, and easier to scale.
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