CLOUDLESS LABS

Project 24. Agricultural insurance data

From data scientists' notebooks to a weather-data product for insurers

An agtech start-up's data scientists had the models, in MATLAB and Python notebooks, but no product. We turned them into a working back end on AWS with a front end, and shipped the MVP of a marketplace selling weather and agricultural data to insurers.

What it changed

  • An MVP shipped: the data scientists' models running as a product on AWS.
  • MATLAB and Python notebooks ported into tested Python and Node.js on AWS Lambda, with PostgreSQL behind them.
  • A foundation the client's own engineers took over and grew.

The situation

Insurers price agricultural risk on weather: drought, frost, rain at harvest. A start-up wanted to sell them the data and the analysis to do it better, as a marketplace for weather and agricultural data. It had data scientists, and their models worked, in MATLAB and Python notebooks. What it didn't have was a product. A notebook runs when a scientist runs it; a product runs every day, for every customer, without one.

It was one of neap.co's first serious projects.

What we did

More data science and data wrangling than app building, and a lot of business analysis first, to understand what the insurers needed and what the models produced.

  • From notebooks to code. We ported the scientists' MATLAB and Python into production code, Python for the analysis and Node.js for the AWS Lambda functions around it, checking every result against the originals.
  • A real back end. The data in PostgreSQL on Amazon RDS, the processing on Lambda, so the analysis ran on schedule instead of on someone's laptop.
  • A front end for customers to use the data.

How it ended

We shipped the MVP, and the client was happy with it. They then hired their own engineers, who took the product on from there. That's a good ending: the point of an MVP is to prove the idea and leave a foundation others can build on.

Why it matters to you

Many companies have good data scientists and no path to production. The gap between a notebook and a product is a craft of its own: tested code, data pipelines, scheduling, and a design that customers can use. We've crossed it many times, from weather forecasts to market analysis.

Related projects

  1. 02 Cutting an AWS bill from US$240k a year to under US$40k An Australian agriculture-technology company with a large IoT sensor network · Agriculture technology · In progress An agtech platform was paying about US$240,000 a year for AWS. The programme under way takes it under US$40,000: roughly US$200,000 a year back in the business, for the same workload.
  2. 04 Real-time ingestion for a large farm-sensor network An Australian agriculture-technology company with a large IoT sensor network · Agriculture technology Thousands of field devices reporting around the clock, processed as the data arrives instead of hours later, on infrastructure that scales with the network.
  3. 05 Deep neural networks that forecast the weather An Australian agriculture-technology company with a large IoT sensor network · Agriculture technology Machine-learning models and deep neural networks, trained on the company's own sensor data with GPUs on Amazon SageMaker, to predict the weather where its customers farm.
  4. 06 Forecasting a weather-driven risk for farmers, from a research paper to agents An Australian agriculture-technology company with a large IoT sensor network · Agriculture technology A sensor network, a white paper from an established Australian scientific organisation and some research code became a production system that forecasts when field conditions turn risky: data lake, models, accuracy reporting and an installable app. Built by hand, then modernised with Claude Code, which now runs it.

Got a project like these?

Tell us where the business needs to go. You'll talk to the engineer who will design and build it, not a salesperson, and get an honest answer on whether we're the right fit.

Contact Us

For general inquiries, please contact us at:

nic@cloudlesslabs.com

For e-learning capabilities, please contact:

lin@shirleenwong.com