back_office_ops · saas · workflow
Solving Data Discovery at Scale: How Wix Uses RAG and Multi-Agent Systems to Find the Right Data Fast
Wix's data spans hundreds of tables and thousands of dimensions across multiple product domains, making it complex and time-consuming for users to locate the right data without depending on domain experts.
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 · Natural language query to Anna
Users ask business questions in natural language via Anna's chat interface.
Tools used
AnnaData PlaygroundVespaCubeAirflowTrino
Outcome
The multi-agent Anna system achieved an 83% success rate for RAG-based dimension matching, and user feedback has been overwhelmingly positive, with natural language queries reducing the barrier to entry for data exploration.
What failed first
Initial embedding approaches—first at the table level, then at the individual dimension level—were ineffective due to sparse metadata and cross-domain variation before a question-to-question matching breakthrough was found.
Results
Volume83%
Grounding & classification
Source type: technical build writeup
27 fields verified against source quotes.
content generationconversational aienterprise searchmulti agent workflowragknowledge basefailure mode describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedsoftwareaccuracy improvementemployee productivitytechnical build writeupback office opsagentic task executionrag answering