Agentic Solution
A RAG (Retriever-augmented Generation) system with a multi-agent setup refers to combining retrieval-based methods (which use external information) with generative models, while employing multiple agents to collaboratively or independently handle tasks. This setup can be applied in various areas like natural language processing (NLP), robotics, or decision-making systems.
RAG System
We help you build intelligent AI systems that combine retrieval and generation. The retriever searches large knowledge bases for relevant content, while the generator uses that data to produce accurate, context-aware responses. This approach enhances the quality of AI outputs by grounding them in real-world knowledge, especially when internal data is limited.
Multi-Agent System
As your technology partner, we help you build multi-agent AI systems where specialized agents—such as retrievers, processors, or user-facing bots—work independently or collaboratively to achieve complex goals. These systems streamline decision-making, automate workflows, and deliver intelligent interactions at scale.
RAG & Multi-Agent System
We help you build scalable, multi-agent AI systems where each agent specializes in tasks like data retrieval, response generation, or domain-specific processing. These collaborative agents work together to deliver faster, more accurate, and intelligent outcomes—adapting easily as your needs grow.
Evaluation
We are here to assist you building evaluation frameworks that effectively trace and maintain data quality. Our proactive approach ensures we identify any latency issues firsthand, allowing us to resolve them before they reach your customers.
