by marctossip | 2025-08-16 01:59:26+00:00
Hi HN,

I’m excited to share a project I’ve been working on: Stockage Courtage, a platform that leverages AI and a custom algorithm to streamline the brokerage of storage solutions for high-value art and patrimonial assets. Think of it as a specialized marketplace connecting collectors, galleries, and institutions with secure, tailored storage facilities across France.

The Problem

Storing valuable items like paintings, sculptures, or historical artifacts isn’t just about finding a warehouse. It requires precise conditions (temperature, humidity, security) and often involves complex logistics, especially for delicate or culturally significant pieces. Traditional storage brokers rely on manual processes, which can be slow, error-prone, and lack transparency. Clients often struggle to find the perfect facility that matches their specific needs, while storage providers miss out on potential matches due to limited visibility.

Our Solution

At Stockage Courtage, we built an AI-driven platform to tackle this. Our core algorithm analyzes a range of factors—item type (e.g., oil paintings, archival documents), required storage conditions, location preferences, and budget—to match clients with the most suitable storage facilities. We use a combination of machine learning and rule-based systems to:

Optimize Matching: Our model scores storage facilities based on client requirements, factoring in real-time data like facility certifications, security ratings, and environmental controls.

Predict Logistics Needs: The AI suggests optimal transport solutions, integrating with logistics partners to ensure safe handling of fragile items.

Dynamic Pricing: We use historical data and market trends to propose fair pricing, balancing client budgets with provider margins.

We also employ natural language processing to parse client inquiries (e.g., “I need climate-controlled storage for a 17th-century tapestry in Paris”) and translate them into structured requirements for our matching engine. This makes the process intuitive for non-technical users, like art collectors or museum curators.

Tech Stack

Backend: Python (FastAPI) for the core API, with PostgreSQL for data management.

AI/ML: Scikit-learn for the matching algorithm, fine-tuned with domain-specific data on art storage requirements. We’re experimenting with a BERT-based NLP model for parsing free-text inquiries.

Frontend: React with Tailwind CSS for a clean, responsive interface.

Data Sources: We pull real-time data from storage facility APIs and public datasets on art preservation standards.

Why It Matters

The art and patrimonial asset market is growing, but storage remains a bottleneck. Our platform not only saves time but also ensures that priceless items are stored in optimal conditions, reducing the risk of damage. For storage providers, it opens up a new channel to reach high-value clients. We’re also exploring blockchain for provenance tracking to add an extra layer of trust.

Challenges and Feedback

One challenge we’re tackling is scaling the algorithm to handle niche requirements (e.g., storing large sculptures or rare manuscripts). We’re also working on integrating IoT data from storage facilities for real-time monitoring of conditions like humidity and temperature. I’d love to hear your thoughts on:

Optimizing the matching algorithm for edge cases.

Privacy concerns when handling sensitive client data (e.g., high-value art collections).

Potential integrations with other tech (e.g., IoT, blockchain) to enhance trust and transparency.

Check out the platform at https://www.stockage-courtage.fr and let me know what you think! We’re still in early stages, and feedback from the HN community would be invaluable.