TikTok GPT: AI Data Copilot for E-commerce Intelligence
A conversational analytics layer that lets business teams query complex TikTok commerce data in plain English instead of writing SQL.
The Problem
TikTok commerce data was fragmented across multiple providers and required complex SQL queries to extract meaningful insights. Business teams depended heavily on data analysts for even simple questions, creating delays in decision-making. As data volume and complexity grew, this dependency became a bottleneck, limiting how quickly teams could respond to trends, evaluate products, and act on market signals.
The raw data compounded the problem. Feeds from different commerce intelligence providers arrived in incompatible formats and needed substantial cleaning and transformation before they could be analyzed at all, so even the analysts spent much of their time on preparation rather than insight.
What We Built
We built an AI-powered data copilot that allows business users to query complex TikTok datasets using natural language. The system combines a multi-source ETL pipeline with an LLM-driven query layer that translates user questions into executable database queries. It maintains context across interactions, understands schema dynamically, and returns structured insights in seconds. This effectively replaces manual SQL workflows with a conversational interface over live business data.
The platform is structured as three independent tiers—ingestion, AI query translation, and user-facing chat—so each can scale and evolve separately. Critically, the query layer inspects the real database schema at query time rather than relying on a hardcoded model, which keeps generated SQL grounded in the data that actually exists.
Natural language to SQL translation
A LangChain agent turns plain-English business questions into executable SQL, using schema-inspection tools so generated queries reflect the live database structure rather than assumptions.
Conversation memory
Context persists across a session, so follow-up questions like "now break that down by month" work naturally without restating the original query.
Orchestrated multi-source ETL
Luigi workflows manage extraction from multiple TikTok commerce data providers with dependency tracking and retry logic, keeping the queryable dataset current without manual exports.
Authenticated chat service
A Go service handles authentication, OAuth sign-in, and persistent chat history, so teams can revisit and share previous analyses rather than re-running them.
How It Works in Practice
A marketing manager wants to identify trending products—a question that previously meant filing a request and waiting on an analyst.
- 1
Ask in plain English
The manager opens a chat session and asks which products gained the most sales velocity in the last thirty days, with no SQL and no knowledge of the schema.
- 2
Ground the query in real schema
The agent inspects available tables and columns, builds a query against the actual structure, and executes it safely against the commerce dataset.
- 3
Return structured results
Results come back in seconds as a readable answer rather than a raw result set, with the underlying query available for anyone who wants to verify it.
- 4
Refine conversationally
Follow-ups narrow by category, region, or price band, each building on the retained context instead of starting a new analysis from scratch.
Engineering Approach
Three independently scalable tiers
Ingestion, AI query translation, and the user-facing chat service are separate components with clear boundaries, so heavy ETL runs never contend with interactive query latency and each tier can evolve on its own.
Schema discovery over hardcoded models
The agent inspects live table and column structure at query time rather than relying on a fixed schema definition, which keeps generated SQL correct as the underlying data model changes.
Guarded query execution
Generated queries run through a constrained tool surface rather than arbitrary database access, keeping a natural-language interface from becoming an unbounded execution path against production data.
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