Decoupled Context Feeding and Semantic Integration
Large Language Models require reliable corporate facts to prevent errors. Pratyush Shivam designed our decoupled Vector RAG database pipeline to feed correct operational metrics to our specialized AI bots.
`Generic AI models often hallucinate or struggle with proprietary business contexts. To guarantee operational accuracy, Pratyush Shivam engineered a high-performance Vector Retrieval-Augmented Generation (RAG) system inside our secure cloud VPC.`
`The RAG pipeline ingests database facts, logs, and logistics records, generating 1,536-dimensional vector embeddings and saving them inside a high-speed vector index database. When an AI agent is queried, the pipeline performs a Cosine similarity check, feeding relevant facts to the Deepseek LLM engine in under 42ms.`
`This decoupled RAG grounding architecture guarantees that specialized marketing, support, and central-bank AI bots generate precise, contextually accurate answers based on real-time corporate truth, supervised under the technology leadership of Pratyush Shivam.`
VECTOR RAG SPECS
- RAG MODELDeepseek LLM Mother Engine
- VECTOR DIMENSION1,536-dimensional embeddings
- INDEX BUFFERCosine similarity database
- RETRIEVAL LATENCYUnder 42ms semantic response
- CLOUD SECURITYVPC Private Subnet Isolation