RAG architecture implementation services

We build and integrate Retrieval-Augmented Generation (RAG) applications. Our RAG systems combine Large Language Models (LLMs) with real-time data retrieval systems to deliver precise, domain-specific outputs.

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We lead the development of Bielik – an open LLM
As founders of the SpeakLeash /ˈspix.lɛʂ/ project, we gather and share language data to support AI growth.
We collaborate on the development of Bielik, open Large Language Model.
We work with top experts to ensure the AI meets local language needs while upholding ethical standards.
We built and deployed an AI Agent for Credit Agricole bank
We deployed a fully operational AI Agent in Credit Agricole’s customer service workflows.
The AI Agent handles simple customer inquiries and routes complex ones to the right teams.
Our expertise in regulated industries ensures the AI meets strict (financial) regulations.

RAG as a service

Our RAG development services

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RAG chatbot development

We build chatbots with RAG architecture for real-time, accurate interactions. These chatbots provide relevant information by combining generative AI with fast data retrieval.
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Custom LLM and RAG integration

We integrate retrieval mechanisms into LLM models. This enables real-time data retrieval, providing contextually accurate responses to user queries.
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Multi-index advanced RAG apps

We build and optimize data indexing systems that allow RAG applications to retrieve the right information quickly. Multi-index retrieval ensures efficient data searches and faster response times.
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Retrieval mechanism design

We design retrieval systems to connect with your databases or external sources. This ensures LLMs have quick access to relevant data.

Our Generative AI development expertise


330
IT experts on board
11
awards and recognitions
for our GenAI solutions
236
clients served in custom development

We develop and deploy RAG architecture in your private infrastructure

What we cover in RAG development services


  • RAG development services - Consultation

    1. Consultation & planning for RAG architecture

    We start by understanding your business needs and evaluating your current infrastructure to implement a RAG architecture. This ensures that your system is ready for efficient data retrieval and real-time generation.

    We cover:

    • Defining project goals and analyzing infrastructure for RAG implementation
    • Creating a detailed roadmap with timelines, resources, and milestones
    • Designing system architecture, including hardware, software, and network scalability
  • RAG development services - Data pipeline setup

    2. Data pipeline setup & optimization for RAG architecture LLM

    We build data pipelines that allow RAG architecture to process, index, and retrieve data quickly. This setup is crucial for LLM agents to fetch relevant data.

    This step includes:

    • Designing data pipelines for handling large datasets in RAG architecture
    • Optimizing retrieval mechanisms for fast data access and real-time responses
    • Ensuring clean data processing for more accurate LLM agent outputs
  • RAG development services - RAG architecture design

    3. RAG architecture design & LLM Agent integration

    We design and integrate RAG architecture into your LLM agent. This enables precise data retrieval and enhanced response generation tailored to your industry.

    We provide:

    • Developing retrieval systems that work with your databases and LLM agent
    • Integrating real-time retrieval and generative components for accurate outputs
    • Customizing RAG architecture to fit your specific industry requirements
  • RAG development services - RAG model fine tuning

    4. RAG model fine-tuning & training for LLM Agents

    We fine-tune LLM agents using your domain-specific data to improve their relevance and accuracy. This ensures the agent generates precise and contextually correct responses.

    We cover:

    • Fine-tuning RAG chatbot models with industry-specific data
    • Training the LLM agent to handle retrieval and generation tasks efficiently
    • Optimizing performance with fine-tuned hyperparameters
  • RAG development services - Self hosted deployment

    5. Self-hosted deployment for RAG implementation

    We deploy RAG architecture in your infrastructure. Our deployments are designed for security, performance, and scalability.

    This includes:

    • Deploying RAG architecture LLM in local data centers or cloud environments like AWS or GCP
    • Ensuring scalable deployment that can handle increased data volumes and users
    • Securing the infrastructure to comply with industry regulations and data protection standards
  • RAG development services - Monitoring and maintenance

    6. Monitoring & maintenance of RAG chatbot systems

    We continuously monitor and maintain your RAG chatbot system to ensure real-time accuracy and performance. Our support keeps your system optimized and up-to-date.

    This step covers:

    • Setting up real-time monitoring for latency, throughput, and retrieval accuracy
    • Regular updates, patches, and system performance optimizations
    • Offering model retraining for your RAG chatbot to stay current with new data

We build effective RAG Applications

Core RAG framework


Ingestion pipeline for data population
Batch and streaming: Processes data in real-time or batch intervals, ensuring the vector database is regularly updated.
Vector embedding: Converts raw data into vector embeddings, preparing it for fast, efficient retrieval.
Retrieval pipeline for efficient search
Query vector DB: Searches the vector database to find the most relevant data based on the user’s input.
Optimized search: Uses advanced algorithms to ensure fast and accurate matching for real-time responses.
Generation pipeline for answer creation
Prompt augmentation: Integrates retrieved data into the prompt, providing context for the LLM.
LLM generation: Uses the enriched prompt to generate precise, contextually relevant answers.

They trusted our expertise


cresit agricole logo
Dekra
Carefleet

Our featured Generative AI projects


  • AI Agent

    AI-powered assistant for customer service interactions

    CLIENT: CREDIT AGRICOLE

    • Message understanding: The system extracts key information from incoming messages and generates a summary containing the purpose and emotional tone. It helps eliminate human errors and ensures clear and uniform language
    • Intelligent routing: Simple requests are handled automatically for faster resolution, freeing up agents for more complex and personal interactions. More complicated messages are passed to the right teams.
    • Generating resources: The system creates customized draft replies and snippets. It can format them into PDFs for sending. It helps improve customer satisfaction scores, and meet service-level agreements.
  • AI assistant

    Intelligent sales assistant for credit card recommendations

    CLIENT: BANK • UAE

    • Meeting preparation assistance: The assistant helps sales representatives prepare for customer meetings. It provides detailed reminders about product terms and benefits for accurate and personalized recommendations.
    • Real-time data analysis: The assistant analyzes input from the salesperson in real-time and compares it against the conditions of over 20 different credit card products. Then, it issues accurate recommendations that meet both client expectations and bank requirements.
    • Integration with up-to-date product data: Direct integration with the bank’s product database ensures recommendations are based on the latest offer conditions.

We build safe, compliant, and ethical AI systems

Security & ethics in AI

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RAG with guardrails

We establish clear guidelines for the responsible use of LLMs. These guardrails minimize risks associated with their deployment, ensuring that AI behaves safely and within defined boundaries.

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Acceptable AI use policies for RAG systems

Our team works with you to develop and implement AI use policies tailored to your organization. These policies govern how AI is used, ensuring that it aligns with ethical practices and your business goals.

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Ethical AI practices for RAG implementation

We follow key principles of fairness, transparency, and accountability in all RAG implementations. We build AI systems that not only meet technical requirements but also adhere to strict ethical standards.

Testimonial

What our clients say

By automating certain customer interactions, bank employees are provided with a prepared “semi-product”, which enables them to dedicate more time to personalizing and empathizing with customer communication, and thus taking even better care of their needs.

Katarzyna Tomczyk – Czykier
Director of the Innovation and Digitization Division – Retail Banking

Why choose us

RAG development company

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Advanced RAG architecture

We specialize in building advanced RAG architecture to improve data retrieval and AI performance. We combine real-time data access with accurate, contextually relevant outputs.
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Industry standards compliance

We maintain the highest levels of security and data protection, holding ISO 27001 certification. Our solutions are fully compliant with industry standards (e.g. GDPR, CCPA).
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Domain expertise

With extensive experience in regulated industries like banking and finance. We develop RAG systems that meet the most complex compliance and security standards.

Get in touch

Let’s talk


Book 1-on-1 consultation 

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Grzegorz Motriuk

Head of Sales | Application Development

Our consultant is at your disposal from 9 AM to 5 PM CET working days from Monday to Friday for any additional questions.