Greener Than Yours

Problem

There is no shortage of lawn-care information on the internet. The harder part is figuring out which advice applies to a specific situation. The same task can be a good idea in one garden and a mistake in another.

Factors include the season, weather, lawn condition, lawn size, previous treatments, available time and the owner's goal. The owner needs to know:

• what they should do,
• when the best time is to do it,
• whether the current weather allows it,
• how to carry out the treatment correctly,
• what they did before and what result it produced.

Product idea and assumptions

The app builds a context for a specific lawn, helps plan work, takes weather conditions into account and guides the user through the next stages of lawn care.

Instead of giving everyone the same universal schedule, the system combines information about the lawn, the season, planned tasks, the history of completed work and the current forecast.

A plan instead of random treatments

The app can suggest whether a task is worth doing now, what time of day will be safest, which conditions may make the work harder, how to prepare for it and whether the plan needs to be adjusted.

Users can also analyse photos of their lawn, track changes over time and ask questions in the context of a specific task, analysis, plan or weather conditions.

Lawn care is a process that lasts throughout the season. The app helps turn isolated pieces of advice into a coherent plan that can later be modified and adapted to real needs.

Weather as part of the decision

Knowing the temperature or the chance of rain alone does not tell you whether it is a good time to fertilise, mow or apply a treatment. Greener Than Yours evaluates the conditions in the context of a specific type of work, looks for suitable weather windows, warns about potential issues and points out situations where a task is better postponed.

AI grounded in context

Artificial intelligence is not a separate feature added simply because it is popular. The model receives the context of the lawn, task, plan, weather or completed analysis, so its answers relate to the user's real situation.

The most important limitations and safety-related decisions are supported by predictable rules and data rather than being left entirely to a language model.

History you can actually use

Completed work does not disappear when a task is marked as done. It becomes part of a lawn-care history that helps users track what they have done, return to earlier treatments and plan the next steps more effectively.

My role

I develop Greener Than Yours as an independent product. The project lets me combine technical experience with product thinking—from the first user need all the way to a working system.

• problem analysis and product assumptions,
• feature and user-flow design,
• backend architecture and data model,
• mobile application development,
• integration with AI models and weather-data providers,
• domain-rule design,
• testing, deployment and environment maintenance,
• controlling the operating costs of AI-powered features.

Technologies

The mobile app is built with React Native and TypeScript. The backend uses NestJS, TypeScript, MySQL and Docker, while development is supported by automated tests, code-quality checks and API documentation based on OpenAPI and Swagger.

• React Native
• TypeScript
• NestJS
• MySQL
• Docker
• OpenAI
• Weather Data
• Firebase Cloud Messaging
• Auth0

The most interesting challenge

The biggest challenge is not connecting an AI model. The harder part is combining model-generated answers with the user's real state, predictable domain rules, weather data and the history of completed actions in a way that makes recommendations useful and safe.

Project status

Greener Than Yours is an actively developed product. The first distribution channel is the Android app, and subsequent versions are developed based on testing and real user scenarios.