Custom RAG Pipeline & Intelligent Knowledge SystemsAhamed Shahmi
Transform your unstructured organizational data into a high-performance, zero-hallucination conversational asset. I engineer production-grade Retrieval-Augmented Generation (RAG) systems that combine advanced document chunking strategies with hybrid retrieval methods (combining dense vector semantic search via FAISS with traditional keyword matching via BM25). This service is ideal for enterprises looking to build secure internal search systems, dynamic document query platforms, or context-aware AI assistants that process proprietary data with absolute reliability.
What is included:
Advanced document ingestion and parsing pipelines (PDFs, Markdown, text docs)
Optimized text chunking and metadata enrichment strategies
Hybrid retrieval architecture configuration (Semantic + BM25)
Seamless vector database indexing (FAISS/Pinecone)
Sub-second query latency and precision benchmarking
FAQs
Example work
Contact for pricing
Duration2 weeks
Tags
FAISS
LangChain
Python
Backend Engineer
Machine Learning
Natural Language Processing
Artificial Intelligence
Information Retrieval
Vector Database
Service provided by
Ahamed Shahmi Kalmunai, Sri Lanka
Custom RAG Pipeline & Intelligent Knowledge SystemsAhamed Shahmi
Contact for pricing
Duration2 weeks
Tags
FAISS
LangChain
Python
Backend Engineer
Machine Learning
Natural Language Processing
Artificial Intelligence
Information Retrieval
Vector Database
Transform your unstructured organizational data into a high-performance, zero-hallucination conversational asset. I engineer production-grade Retrieval-Augmented Generation (RAG) systems that combine advanced document chunking strategies with hybrid retrieval methods (combining dense vector semantic search via FAISS with traditional keyword matching via BM25). This service is ideal for enterprises looking to build secure internal search systems, dynamic document query platforms, or context-aware AI assistants that process proprietary data with absolute reliability.
What is included:
Advanced document ingestion and parsing pipelines (PDFs, Markdown, text docs)
Optimized text chunking and metadata enrichment strategies
Hybrid retrieval architecture configuration (Semantic + BM25)
Seamless vector database indexing (FAISS/Pinecone)
Sub-second query latency and precision benchmarking
FAQs
Example work
Contact for pricing