LP.
SYSTEM CASE STUDYmybotgenie.ai

MyBotGenie AI

Production RAG assistant that ingests company documents and answers questions with source citations. Custom retrieval pipeline with vector search, chunking strategies, and streaming LLM responses.

Next.jsOpenAIPineconeLangChainTailwind
VERIFIED BENCHMARKS & IMPACT
Benchmark

10K+ documents indexed

Benchmark

<800ms time-to-first-token

Benchmark

95% citation accuracy

01 / THE PROBLEM

Operational Bottleneck

Companies struggle to make internal knowledge accessible. Employees waste hours searching through scattered docs, wikis, and Slack threads to find answers.

02 / SYSTEM ARCHITECTURE
01

Document ingestion pipeline with PDF/DOCX/TXT parsing

02

Custom chunking strategy with overlap for context preservation

03

OpenAI embeddings → Pinecone vector store for semantic retrieval

04

Hybrid search combining keyword matching + vector similarity

05

Streaming LLM responses with source citation linking

06

Next.js frontend with real-time chat interface

03 / RESULTS & PRODUCTION IMPACT
-Handles 10K+ documents with consistent retrieval quality
-Sub-second response time with streaming output at mybotgenie.ai
-Source citations on every answer for verifiability

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