RAG Development Company in Bangalore and USA

RAG development costs depend on the complexity of your data pipeline, number of data sources, vector database requirements, chunking strategies, and LLM selection. A basic RAG chatbot MVP with a single data source may start from $12,000, while enterprise-grade RAG systems with multi-source ingestion, hybrid search, guardrails, and production MLOps can range between $25,000 and $120,000+. Contact Cipher Trivia for a detailed, no-obligation proposal tailored to your specific RAG project requirements.

A RAG-powered MVP with a single data source and pre-built vector store can be delivered in 4 to 8 weeks. A fully custom enterprise RAG platform with multi-source ingestion, advanced chunking strategies, hybrid retrieval, evaluation frameworks, and production deployment typically takes 3 to 6 months. We follow agile methodology with iterative retrieval quality improvements at every sprint.

We work with all leading vector databases including Pinecone, Weaviate, ChromaDB, Qdrant, Milvus, pgvector (PostgreSQL), Azure AI Search, and Elasticsearch with vector capabilities. We select the optimal vector store based on your scale requirements, query latency targets, metadata filtering needs, cost considerations, and infrastructure preferences — whether cloud-hosted or self-managed.

Yes. We specialize in building RAG pipelines that ingest and index your existing documents — PDFs, Word files, Excel spreadsheets, Confluence wikis, SharePoint libraries, Notion pages, Slack archives, Google Drive, SQL databases, and REST APIs. Our document processing pipelines handle OCR, table extraction, layout analysis, metadata enrichment, and intelligent chunking to maximize retrieval accuracy across all your content.

We implement comprehensive RAG evaluation frameworks measuring retrieval precision, recall, answer faithfulness, and relevance using RAGAS, LangSmith, DeepEval, and custom evaluation pipelines. Our optimization techniques include hybrid search (semantic + keyword), re-ranking with cross-encoders, query decomposition, HyDE (Hypothetical Document Embeddings), and iterative chunking strategy refinement to achieve 95%+ retrieval accuracy.

Absolutely. We build secure RAG architectures with role-based access controls, document-level permissions, data encryption at rest and in transit, PII redaction, prompt injection prevention, audit logging, and on-premise or private cloud deployment options. Our RAG systems comply with GDPR, HIPAA, SOC 2, and industry-specific regulations for handling sensitive enterprise data.

RAG retrieves relevant context from external knowledge sources at query time, keeping responses current and factual without modifying the base model. Fine-tuning permanently adjusts model weights on your data, which is better for teaching style, tone, or domain-specific terminology. We often recommend a hybrid approach — RAG for factual knowledge retrieval combined with lightweight fine-tuning for domain-specific language patterns — to get the best of both worlds.

We work with LangChain, LlamaIndex, Semantic Kernel, Haystack, DSPy, OpenAI API, Azure OpenAI, Anthropic Claude API, Pinecone, Weaviate, ChromaDB, Qdrant, pgvector, RAGAS, LangSmith, FastAPI, and Docker/Kubernetes for deployment. Our team selects the optimal stack based on your specific RAG use case, scale requirements, latency targets, and deployment environment.

Yes. We provide comprehensive post-deployment support including retrieval quality monitoring, chunking strategy optimization, embedding model upgrades, new data source onboarding, cost optimization, latency tuning, guardrail refinement, and regular RAGAS evaluation cycles. Our RAG systems are designed for continuous improvement as your knowledge base grows and user query patterns evolve over time.