Chord AI Technical Overview

this guide provides an overview of how chord uses ai and machine learning to deliver reliable, transparent, and actionable features for e commerce brands it’s designed for brand users who want to understand what’s under the hood overview learn more about how chord ai powers our platform — from customer segmentation to personalized recommendations — using your commerce data and chord's data framework here's how it works behind the scenes how chord ai works 1\ connect data source what happens we securely connect your commerce data to chord ai why it matters ensures the ai platform has the most recent and relevant store information to work with to generate accurate responses behind the scenes our data source connection uses warehouse share, with validation checks for schema, freshness, and permissions note that we do not support connecting multiple data sources at this time 2\ data source modeling what happens this is where your data schema is indexed and transformed into structured, analytics ready models you can also add your store’s specific instructions and/or sql pairs via the chord context studio to refine chord ai outputs why it matters this is an important abstraction layer between your data warehouse and chord’s ai models with the chord context studio, you can also create data definitions and consistency—so “revenue,” “customer,” and other terms mean the same thing across your business when you calculate key metrics behind the scenes this is where chord ai runs its indexing and stores data schema in a vector database we only look at the data that you want us to look at chord ai does not store your data; we simply work with your store’s data schema this means that we are working with consistent data models that are decoupled from your data warehouse 3\ data retrieval what happens once a question is asked via copilot chat, chord ai retrieves the relevant data tables from the data modeling layer why it matters keeps outputs accurate, relevant, and efficient behind the scenes chord ai looks into the vector database to see what information is relevant to the question we always pull the top results when we query the data schema 4\ generating outputs via llm what happens chord ai answers your question via prompt engineering and validates copilot answers via chord’s large language model (llm) why it matters this step automates manual and complex analysis—giving you the right answer faster, without needing to think or write complex sql manually to generate an answer behind the scenes chord ai’s llm translates your request builds an optimized sql query to subsequently build the output we build the sql query, validate it, and run it against your data source prior to returning the output to you