AskTiz is an internal AI-powered knowledge assistant that transforms an organization's Slack workspace into a searchable knowledge base. The platform continuously ingests Slack conversations, indexes them using semantic embeddings and hybrid search techniques, and enables employees to ask natural language questions about company history, technical discussions, decisions, and documentation.
By combining Retrieval-Augmented Generation (RAG), hybrid search, reranking, and streaming AI responses, the system delivers highly accurate answers with inline citations linking directly to the original Slack conversations.
Project walkthrough
Demo & Architecture Overview
What we built
Real-Time Slack Knowledge Ingestion
Developed pipelines that continuously ingest Slack messages using the Slack Events API.
Built asynchronous processing queues using PostgreSQL pgmq for reliable message ingestion.
Automated embedding generation and indexing for newly received conversations.
Supported incremental synchronization without interrupting existing search indexes.
Retrieval-Augmented Generation (RAG)
Designed and implemented a complete RAG pipeline for enterprise knowledge retrieval.
Combined semantic retrieval with lexical search to maximize answer accuracy.
Enabled contextual AI responses grounded entirely in company conversations.
Returned inline citations linking directly to original Slack messages for transparency and verification.
