Aidena

— AI STACK RECOMMENDATION

AI Dynamic Pricing Engine for Retail

Real-time pricing optimization using competitor monitoring, demand forecasting, and automated price adjustments. Scalable from startup to enterprise with cost-efficient inference and data pipelines.

Stays alive for 365 days after the last visit.

E-commerce

AI Dynamic Pricing Engine for Retail

Real-time pricing optimization using competitor monitoring, demand forecasting, and automated price adjustments. Scalable from startup to enterprise with cost-efficient inference and data pipelines.

high confidence

Core Stack ℹ︎

Claude Sonnet 4

Primary

Balanced model for analyzing competitor pricing data, demand signals, and generating pricing recommendations with strong reasoning capabilities at lower cost than Opus.

$50-200/month

Firecrawl

Primary

Scrapes competitor websites and pricing pages reliably, extracting clean LLM-ready data for real-time competitor price monitoring at scale.

$0-100/month

Dagster

Primary

Orchestrates data pipelines for ingesting competitor data, demand signals, and inventory. Asset-based approach ensures data quality and lineage for pricing decisions.

$0-500/month

Complete the Stack ℹ︎

Cohere Command R

Alternative

Efficient model optimized for RAG with built-in tool use for structured pricing analysis and citation generation for audit trails.

$50-150/month

Chroma

Alternative

Stores historical pricing, competitor data, and demand patterns as embeddings for fast semantic retrieval during pricing decisions.

$0/month

Beam Cloud

Alternative

Serverless GPU platform for running pricing optimization models and inference at scale with pay-per-second billing, ideal for variable workloads.

$50-300/month

Getting started

  1. 1Set up Dagster pipelines to ingest competitor pricing via Firecrawl daily, normalize demand data from sales/inventory systems, and store in data warehouse.
  2. 2Create Chroma vector store indexed by product category, competitor, and time period for fast retrieval.
  3. 3Build Claude Sonnet agent that queries Chroma for relevant competitor and demand context, analyzes pricing elasticity, and recommends optimal prices.
  4. 4Deploy pricing API on Beam Cloud that triggers Claude analysis on demand or on schedule.
  5. 5Implement feedback loop: track actual sales velocity against recommended prices, feed back into Chroma for continuous model improvement.
  6. 6Add audit logging for all price changes with reasoning for compliance.
Copy link to clipboard

What are you building?

Build your own AI stack →