Let your AI speak facts, not just patterns
We identify where your most valuable content lives, PDFs, Confluence, Google Drive, Notion, Zendesk, SQL DBs, SharePoint, and define retrieval logic.
Your documents are broken into logical “chunks” (semantic units) and embedded using vector databases like FAISS, Pinecone, or Weaviate, so the system can understand meaning, not just keywords.
We connect a retriever module (like Elasticsearch or vector DBs) with a language model like GPT, Claude, or open-source LLMs, forming the RAG loop.
We add formatting rules, citation references, response filters, and escalation paths, so answers are not just smart, but business-ready.
Powerful coding foundations that drive scalable solutions.
Enhanced tools and add-ons to accelerate development.
Secure, flexible, and future-ready infrastructure in the cloud.
LLMs & GenAI
Smarter AI through retrieval and generation
Our RAG Model Development services combine retrieval-based systems with generative AI models to provide accurate, context-aware, and dynamic outputs. By leveraging knowledge retrieval, vector databases, and language models, we build solutions that answer complex queries, summarize documents, and generate insightful responses. Ideal for enterprise search, knowledge management, and AI-powered assistants, RAG models enhance AI capabilities by grounding generative outputs in real-world data.
Deliver search and retrieval systems that link content, workflows, and cloud data, ensuring every AI output is trustworthy, accurate, and grounded.
