AI Campaign Orchestrator

Core Logic

Acts as the central coordinator using a DAG execution engine. Manages agent lifecycle, routes data between agents, handles failures with circuit breakers, and ensures proper execution ordering. Emits workflow-level traces and aggregates results from downstream agents.

Core Logic

Uses LLM-powered chain-of-thought reasoning to parse brief text, extract explicit requirements (budget, timeline, platforms), identify implicit preferences (tone, style, demographics), and classify constraints. Outputs structured intent vectors with confidence scores.

Core Logic

Applies feature engineering pipelines to extract categorical features (industry, content type), numerical features (budget range, duration), and derived features (urgency score, complexity index). Normalizes and encodes features for downstream ML models.

Core Logic

Generates high-dimensional embedding vectors using transformer-based models. Embeds creative direction text, target audience descriptions, and brand voice guidelines into a shared semantic space for cosine similarity search against creator embeddings.

Core Logic

Classifies target audiences into predefined segments using demographic analysis (age, gender, location) and psychographic clustering (interests, values, behaviors). Outputs audience affinity scores and segment membership probabilities.

Core Logic

Queries the vector database with brief embeddings using HNSW index for sub-linear search complexity. Returns top-K candidates with similarity scores, filters by metadata constraints, and provides 2D projections for visualization.

Core Logic

Uses gradient-boosted models trained on historical campaign outcomes to predict engagement rates, conversion likelihood, and ROI for each candidate creator. Outputs predictions with confidence intervals and feature importance explanations.

Core Logic

Formulates budget allocation as a constrained optimization problem. Uses linear programming with creator rates, predicted ROI, and budget constraints to find optimal allocation. Suggests budget adjustments if constraints are too restrictive.

Core Logic

Aggregates outputs from all upstream agents to generate ranked creator packages. Each package includes creator profiles, predicted performance metrics, pricing breakdown, timeline estimates, and AI-generated explanations for the recommendations.

Core Logic

Applies business rule validation (budget limits, platform constraints, exclusivity checks), consistency verification (timeline feasibility, availability conflicts), and quality scoring. Flags issues and triggers re-processing for failed validations.

Core Logic

Monitors market signals including trending hashtags, emerging content formats, platform algorithm changes, and seasonal patterns. Adjusts matching weights and surfaces trend-aligned creators with momentum indicators.

Core Logic

Analyzes competitor campaign data, identifies creators with recent competitor partnerships, detects market saturation in niches, and provides competitive positioning recommendations. Flags potential exclusivity conflicts.

Core Logic

Performs sentiment analysis on creator content history, detects controversial topics, assesses brand safety scores, and evaluates tone consistency with brand guidelines. Outputs sentiment vectors and risk flags for each candidate.