Aidena

— AI STACK RECOMMENDATION

AI Supply Chain Optimization Platform

End-to-end platform for demand forecasting and logistics optimization using ML models, data pipelines, and agent-driven decision-making at startup scale.

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AI Supply Chain Optimization Platform

End-to-end platform for demand forecasting and logistics optimization using ML models, data pipelines, and agent-driven decision-making at startup scale.

high confidence

Core Stack ℹ︎

Dagster

Primary

Asset-based data orchestration designed for ML workflows. Models demand forecasting pipelines as assets with lineage tracking, enabling reliable feature engineering and model retraining at scale.

$0/month (open-source) or $500+/month (managed)

CrewAI

Primary

Multi-agent orchestration framework perfect for supply chain decision-making. Agents can specialize in demand forecasting, inventory optimization, and logistics routing, collaborating autonomously on complex workflows.

$0/month

Claude Opus 4

Primary

Most capable LLM for complex supply chain analysis, demand pattern interpretation, and logistics optimization reasoning. Handles extended context for multi-SKU forecasting.

$50-200/month

Complete the Stack ℹ︎

Airbyte

Alternative

300+ connectors for ingesting sales data, inventory systems, supplier APIs, and logistics platforms. Automates data centralization for training demand forecasting models.

$0/month (self-hosted) or $100+/month (cloud)

Chroma

Alternative

Lightweight vector database for storing historical demand patterns, supplier performance metrics, and logistics constraints. Enables semantic search for similar past scenarios.

$0/month

AgentOps

Alternative

Observability platform for monitoring agent decisions, cost tracking, and LLM call analysis. Critical for auditing supply chain optimization decisions and cost optimization.

$0/month (free tier) or $500+/month (pro)

Getting started

  1. 1Set up Dagster to orchestrate data ingestion from ERP/WMS systems via Airbyte connectors, creating asset definitions for sales history, inventory levels, and supplier data.
  2. 2Build demand forecasting pipeline in Dagster using historical data, training time-series models (ARIMA, Prophet, or ML-based).
  3. 3Create CrewAI agents: Demand Forecaster (predicts SKU demand), Inventory Optimizer (calculates safety stock), Logistics Planner (routes shipments).
  4. 4Store demand patterns and logistics constraints in Chroma for agent context retrieval.
  5. 5Connect agents to Claude Opus for reasoning on complex multi-objective optimization (cost vs. service level).
  6. 6Deploy AgentOps for monitoring agent decisions, tracking forecast accuracy, and cost per optimization decision.
  7. 7Expose optimization recommendations via API for integration with ERP/WMS systems.
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