Contact Us

Harinarayanpur, Kushtia, Bangladesh

RAG, Vector Search & Knowledge Pipelines

Retrieval-Augmented Generation (RAG), vector embeddings & semantic search.

RAG, Vector Search & Knowledge Pipelines

High-Accuracy RAG & Knowledge Architecture

Unlock the value of your private company documents, manuals, and databases. We construct production-ready Retrieval-Augmented Generation (RAG) pipelines that allow LLMs to deliver hallucination-free answers grounded in your data.

Engineering Deliverables

  • Hybrid Vector & Lexical Search: Combining dense vector embeddings (OpenAI, Gemini) with sparse BM25 indexing for peak accuracy.
  • Intelligent Document Parsing: Automated chunking, OCR, table extraction, and metadata tagging from PDFs and databases.
  • Low-Latency Retrieval: Integrated with Pinecone, pgvector, Qdrant, and Milvus for microsecond vector query responses.
  • Audit Trails & Citations: Every AI response references exact source chunks with verifiable page and line citations.

Frequently Asked Question

How do you ensure AI accuracy and avoid hallucinations in RAG, Vector Search & Knowledge Pipelines?
We apply robust RAG pipelines, deterministic verification checks, schema-validated structured outputs, and strict temperature controls to guarantee production reliability.
We work across premier models including Gemini 2.5 / 1.5 Pro, Claude 3.7 Sonnet, OpenAI GPT-4o, and open-source models (Llama 3, DeepSeek) hosted via Ollama or vLLM.
Yes, we implement telemetry to track latency, token usage, drift detection, and user feedback loops to continuously optimize performance.
All Services
Need help?

Tell us about your project and we will get back within one working day.

Contact us
© 2026 All Rights Reserved Insomniacafe