Retrieval Augmented Generation
RAG As A Service
Convert massive unstructured data into trainable datasets to generate contextually accurate responses with RAG in LLM
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Benefits
What are the benefits of RAG implementation?
Here’s how RAG software can optimize your business workflow
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Reduced AI Hallucinations
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Bias-Free Content Generation
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Up-To-Date Information
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Source Links Identification
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Coherent Responses
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Domain Specific Answers
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Seamless User Experience
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Query Based Responses
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Elevate Customer Engagement
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More Personalized Replies
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Increase In Retention Rate
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Easy Information Updates
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No Fine-Tuning Expenses
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Easy Information Access
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Reduced Copyright Issues
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Minimize legal Risks
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Accurate Source Citation
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Limit Spreading Misinformation
Tech Stack
Technologies that help us deliver premium RAG service
Our updated tools and technology kits help us refine your digital integrations
Get more insightsChoose the right technology to build a resilient RAG system
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NLTK
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SpaCy
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Hugging Face Transformer
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TensorFlow
Improve output accuracy with the fitting vector database
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Faiss
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Annoy
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Elasticsearch
Resolve all the business complexities with the ideal AI model
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GPT-3
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GPT 4
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Stable Diffusion
Centralize data storage for easy access and flexibility with reliable cloud platforms
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AWS S3
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Google Cloud Storage
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Azure Blob Storage
Choose an apt DBMS or its combination for effective data structuring & management
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PostgreSQL
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MySQL
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MongoDB
Easily deploy and manage the RAG solution with an ideal containerization tool
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Docker
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Kubernetes
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Amazon ECS
Our well-defined approach to RAG implementation
We ensure seamless RAG integration into your existing system
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01
01Requirement Identification
Assess both RAG implementation opportunity and data quality for retrieving and generating relevant information
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02Data Gathering & Structuring
Collect suitable data from multiple sources and convert it into an easy-to-access and search format
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03Select Retrieval & Generative Model
Choose an ideal retrieval model based on your primary requirements and a generative model per your usage
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04
04Combine The Two Models
Build an effective system to process queries, contextualize, and generate relevant output by merging retrieval and generative models
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05
05Evaluate & Refine
Utilize key metrics to understand how the RAG solution is performing and refine it accordingly
Why Openxcell
What makes Openxcell a reliable RAG development partner
Our team offers premium-quality services to optimize your workflow
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Client Focus Services
We streamline collaboration, simplify communication, and strategize the whole process, keeping client’s requirements as a central priority.
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Technical Adeptness
Our team has a strong understanding of AI, its related services, and RAG techniques. We design progressive solutions that ensure long-term success for our clients.
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15+
years of delivering quality solutions
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1000+
Happy Clients/ Loyal Clients
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400+
AI Experts
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1500+
Successful Projects
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95%
Client Retention
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20%
Faster Product Delivery
Case Study
Our successful projects tailored to match our client’s requirements
How we helped our clients integrate the best digital solutions
For more case studies
Speed - A leading AI-powered crypto platform to ensure the security of transactions
Speed primarily aims to collaborate with AI to identify and point out suspicious crypto transactions in real time. It provides a secure environment for all users to maintain the proper crypto transactions.
Technology Used
- Data Storage - SQL/NoSQL
- Data Processing- Apache Spark
- ML Frameworks- TensorFlow and Scikit-learn
- Real-time Processing- Apache Kafka
- Deployment- Docker and Kubernetes
Key Features
- AI better the detection rate of fraudulent transactions
- Real-time analysis, minimizing potential losses
- Reduced False Positives
JobTatkal - Job platform powered by Generative AI
The platform bridged the gap between recruiters and job seekers with its GPT-powered capabilities. It allowed users to create accurate job descriptions and filter results for relevant profiles. This saves recruiters’ time while candidates benefit from better visibility.
Technology Used
- Primary AI Technology - OpenAI GPT-4
- Frontend - React.js, Next.js
- Backend - Node.js
- Database - MongoDB Atlas
Key Features
- 10x Faster Profile Setup With AI
- Hiring Time Reduced By 91%
- 7x Faster Job Posting
Cribzzzz AI Assistant - A generative AI chatbot designed for real estate
We designed a generative AI solution for Cribzzzz – a platform that connects real estate agents with potential buyers. The solution was designed to handle massive datasets and create a unique yet engaging client experience.
Technology Used
- GPT model – GPT 4
- Server – Microsoft SQL
- Frontend – ReactJS
- Backend - Dotnet Core
- Database – MongoDB
Pointers
- Generative AI-powered search
- Real-time assistance and 24/7 support
- Voice assistance and personalized suggestion
TracknTake - Discover and Deliver all products: Your Local Marketplace at Your Fingertips
The platform empowers users to search for products available in their local area. TracknTake’s beneficial feature is a flexible pick-up, which cuts wait time. It enhances the shopping experience, offering convenience and reducing delivery costs and time
Technology Used
- Backend - Python, PHP Laravel
- Frontend- Android - Kotlin, IOS- Swift
Key Features
- Find products nearby easily
- Simple search
- Instant results
- Hassle-free shopping
Industries
Industry specific RAG solutions for your enterprise
Our expertise spans across a vast range of verticals
Build a custom solution
Testimonials
Here’s what the clients have to say about our services
We help our clients upscale with modern digital integrations
Resources
Latest insights about Retrieval Augmented Generation
Know how RAG is transforming the digital landscape
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Frequently asked questions about
RAG as a service
Resolve your queries and then begin!
Retrieval augmented generation, or RAG, is an advanced methodology that improves the accuracy of LLM models while eliminating the need for retraining. It refers to external knowledge sources to further enhance LLM’s already impressive capabilities.
Yes, given that RAG utilizes external knowledge sources, it can easily be customized to refine LLM with domain-specific data.
Both have different use cases. RAG is a cost-effective way to improve LLM models with current and accurate real-time information while fine-tuning enhances model performance. Consulting a professional for advice on the matter would be a better option.
While RAG has many use cases across the industry, some of the most commonly occurring ones are in customer support, sales and marketing, and R&D.
Apart from the key performance metrics, some of the quality identifiers can be its accuracy, content language coherence, and efficiency. These define how well the model is trained and its quality.
Ready to move forward?
Contact us today to learn more about our AI solutions and start your journey towards enhanced efficiency and growth
