Offering: Architecting, developing, and deploying scalable RAG-based systems for enterprise knowledge retrieval and conversational AI.
Scope Includes:
End-to-end RAG pipeline design (ingestion, chunking, embedding, vector store, retriever, and LLM integration)
Developing evaluation-based ETL for your RAG system using shoveller.io
Fine-tune specialized and custom embedding models using SanderLang
Custom Reranking models for better conversational search UX
Domain-specific ontology development or adaptation of legacy ontology-based search into to the new RAG search paradigm
Custom NLP pipelines for query understanding and reranking models for better conversational search UX.
Infrastructure setup using tools like Redis, LangChain, SageMaker, or Bedrock
Semantic caching, semantic routing, and hybrid search optimization
Deployment automation and monitoring support
Ideal For: Organizations looking to integrate GenAI into internal search tools, knowledge assistants, or customer-facing bots.
