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.
Our Generative AI development expertise
We develop and deploy RAG architecture in your private infrastructure
What we cover in RAG development services
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1. Consultation & planning for RAG architecture
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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
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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
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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
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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
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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
They trusted our expertise
Our featured Generative AI projects
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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.
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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
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.
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.
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.
Why choose us
RAG development company
Advanced RAG architecture
Industry standards compliance
Domain expertise
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