A machine-learning forecast of port congestion for Chilean importers and exporters. Trained on about twenty million customs records, it predicts each port’s monthly load a year ahead from its own history, adds the risk of the port closing to swell, and tells a shipper which port to use for each commodity.

customs records of imports and exports. Each one carries an HS code, so the forecast splits by commodity.

20M

ports, imports and exports, forecast month by month for the next 12 months.

23

accuracy in 2026 so far. We checked the forecast against the real shipments from January to May.

91.5%

accuracy at San Antonio, which handles 57% of Chile’s maritime imports. The busier the port, the better the forecast.

96.6%

Import view, combined layer · 23 ports ranked

Every port at its real position, coloured by its predicted congestion. San Antonio handles 57% of the country’s maritime imports and runs hottest, which is why the answer is so often a different port.

Valparaíso, imports · congestion forecast, 2026

Load forecast month by month against the port’s own record, so a planner can see the quiet months and the peak before booking.

San Antonio · vehicles and parts · weather layer

Hours the port is shut by swell, each month of a typical year, laid over the forecast for one commodity, which is the question an importer actually has.

Arica · oil seeds · combined view

For exporters too: it scores every port for the chosen commodity and says when another one is the better route.

Alma Secret, a Spanish dermocosmetics brand, launched this campaign on almasecret.cl and wanted it to do more than bring visitors to the site. Now a customer takes a selfie on her phone and gets an AI reading of her skin and a personal three-product routine from the brand’s own catalogue, with safety rules for pregnancy and sensitive skin built in. Every result goes to the brand’s CRM, so the follow-up is personal and each sale can be traced back to the analysis that started it. The brand’s own team runs it day to day, without touching code.

skin conditions read from one selfie.

13

products in her personal routine.

3

details saved to the CRM for each analysis, ready for the follow-up.

58

Live occupancy counting for bars and venues in Barcelona, where going over the licensed capacity means a fine. A camera and a Raspberry Pi count the people in the room every second with a computer-vision model, show the number and the night’s peak on a display the staff can see, and can feed a sign, a log or an alert. The video is processed on the device and never stored or sent anywhere, so no footage of customers leaves the building.

frames of video stored or sent off the premises.

0

second between counts, so the number on the display stays live.

1

camera works: a webcam, an IP camera or a recorded video.

Any

A bar terrace, every person boxed and counted · simulated scene

A simulated scene, not a client’s venue: the detections and the total are how the counter marks up what it sees.

The 16×2 panel the counter drives, on the bar

Drawn from the panel the Pi version renders. It can also drive a real LCD on the bar, or send the count on to a sign or a log.

A broker in Santiago and Pichilemu whose listings lived on portals that diluted her brand and charged her for every lead. Her own site now: her properties only, each enquiry arriving on WhatsApp with the listing code already in the message, and a panel she runs from her phone.

Brand, featured property and search, without scrolling

Prices carry UF with the peso equivalent refreshed daily, and the map shows an approximate location so an owner’s address is never published.

Built session by session over a term on the MSc infrastructure module, on a thousand-odd raw files of real aircraft tracking data. Not client work, and listed as what it is — but it is where the medallion layering, the orchestration and the CI that the client pipelines use were learned in the open.