Tool stack
AI Data and RAG Builder Stack
A technical stack for preparing messy documents, web data, retrieval stores, and AI workflows for useful internal assistants.
Who it is for
Problems it solves
Messy documents
RAG quality
Web data collection
Retrieval testing
AI workflow orchestration
Recommended tools
Document processing
Turn PDFs, docs, and web pages into AI-ready text and structured data.
Vector database
Store embeddings and support semantic retrieval for RAG applications.
AI app workflow
Build assistants, workflows, and testable knowledge-base Q&A.
Browser and web automation
Collect source-backed web data and run repeatable web research tasks.
Orchestration
Connect ingestion, AI steps, validation, review, and notifications.
Review hub
Track source owners, failed questions, content freshness, and cleanup work.
Workflows included
Prepare messy documents for a RAG knowledge base
Parse messy docs, clean chunks, choose a vector database, and create a testable retrieval workflow.
Setup
4 hours
Saves
5-15 hours
Build a browser-based research agent for repetitive web tasks
Use browser automation and search APIs to collect structured web evidence for recurring research tasks.
Setup
3 hours
Saves
4-12 hours
Build a lightweight API-to-AI operations workflow
Connect APIs, web data, AI summaries, and business tools without building a full internal app.
Setup
2.5 hours
Saves
4-12 hours
Build an internal AI assistant from company docs
Create a simple internal assistant that answers team questions from SOPs, policies, help docs, and product knowledge.
Setup
2-3 hours
Saves
4-10 hours
Beginner setup plan
Start with one high-value knowledge domain and a real question set.
Clean sources before embedding them.
Test retrieval quality before judging answer quality.
Add automation only after source ownership and update rules are clear.