back_office_ops · ecommerce · workflow
Woowa Brothers BADA team builds QueryAnswerBird LLM-based AI data analyst with Data Discovery feature
Employees at Woowa Brothers with varying data literacy spent significant time understanding complex queries, and non-data roles such as product managers lacked effective ways to explore internal data beyond text-to-SQL query generation.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User submits question
A user submits a question to QAB in a Single-Turn conversation consisting of a single question and response.
Tools used
SQLGlotLangGraph
Outcome
QAB's Data Discovery feature improved user satisfaction and reliability across data and non-data roles, with the team expecting QAB to significantly boost internal productivity.
What failed first
The initial text-to-SQL feature had gaps in table selection accuracy and business logic application; users frequently skipped the user guide and asked vague questions, receiving unsatisfactory answers; and LLM-generated table metadata suffered from hallucination errors.
Results
Volumemore than 10%
Running sinceAugust 2024
Grounding & classification
Source type: technical build writeup
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