Creator Matching System
Implements an enterprise-grade MLOps pipeline with LLM-powered brief analysis, vector database semantic search, feature store computations, model registry inference, and distributed tracing. Provides real-time observability, feedback loops, and conversational AI for iterative refinement.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
LLM Brief Analysis - Chain-of-thought reasoning interface for extracting explicit and implicit requirements from brand commission briefs
Multi-Agent Orchestration - Temporal-compatible workflow showing 7 specialized agents executing in parallel phases across the creator matching pipeline
Feature Store Pipeline - Feast-compatible feature computation with Redis caching, real-time materialization, and feature view registry
Match Results with SHAP Explainability - Ranked creator matches with transparent AI-driven scoring and feature contribution breakdowns
AI Agents
Specialized autonomous agents working in coordination
LLM Brief Analysis Service
Brand briefs contain complex, nuanced requirements in natural language that need sophisticated understanding beyond keyword extraction to capture creative intent and implicit constraints.
Core Logic
Leverages GPT-4 class LLMs with chain-of-thought prompting to analyze briefs. Streams reasoning tokens for transparency, extracts explicit requirements (budget, timeline, platforms), implicit preferences (tone vectors, style markers), and constraints. Generates 384-dimensional semantic embeddings for downstream matching.
Vector Database Service
Finding semantically similar creators from millions of profiles requires efficient similarity search that understands meaning, not just keywords.
Core Logic
Provides Pinecone-compatible vector operations including embedding generation, similarity search with configurable top-K, metadata filtering, and namespace isolation. Returns ranked matches with similarity scores and supports 2D UMAP projections for visualization. Maintains creator profile embeddings with rich metadata.
Feature Store Service
ML models require consistent, up-to-date features computed from raw creator data. Manual feature engineering leads to training-serving skew and inconsistent predictions.
Core Logic
Implements Feast-compatible feature store with entity definitions, feature views, and materialization pipelines. Computes real-time features (engagement rates, recent activity) and batch features (historical performance, audience demographics). Ensures feature consistency between training and inference.
ML Model Inference Service
Deployed ML models require version management, A/B testing capability, performance monitoring, and consistent serving infrastructure for production predictions.
Core Logic
Provides MLflow-compatible model registry with version tracking, stage transitions (staging/production), and inference endpoints. Runs creator-brand match scoring models with configurable versions, returns predictions with confidence intervals, and logs inference metrics for monitoring.
Agent Orchestrator Service
Complex matching workflows require coordination of multiple specialized agents with dependencies, parallel execution, error handling, and state management.
Core Logic
Implements Temporal-compatible workflow orchestration with DAG-based execution. Manages agent lifecycle, handles failures with retry policies, coordinates parallel agent execution, and maintains workflow state. Provides workflow visualization and execution history.
Distributed Tracing Service
Debugging distributed AI systems requires end-to-end visibility into request flows, latencies, errors, and dependencies across all services.
Core Logic
Provides OpenTelemetry-compatible distributed tracing with automatic span creation, context propagation, and trace correlation. Captures LLM calls, vector operations, feature computations, and model inference with detailed attributes. Enables flame graph visualization and latency analysis.
Conversational Refinement Agent
Initial matching results may not perfectly align with brand intent. Users need an interactive way to refine results through natural conversation rather than complex filter adjustments.
Core Logic
Provides a conversational AI interface for iterative result refinement. Understands natural language feedback ('show more fitness creators', 'prefer higher engagement'), translates to filter adjustments, re-runs matching pipeline, and explains changes. Maintains conversation context for multi-turn refinement.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
6 technologies
Architecture Diagram
System flow visualization