our machine learning technology
efficiency + happiness

livee uses computer vision and machine learning to reshape the landscaping design process. on average, landscape designers take one week to produce a proposal. we do it in 4 seconds.

rethinking landscape design

our computer vision model compares thousands of SKUs (plant and pot combinations) and matches them within seconds.

using the principles of matrix factorization, our algorithm analyzes and compares the features of the user and the product to produce the most accurate proposal.

we aim to implement a system to monitor and linking cross-dependencies between placed landscaping and it's effect on productivity, happiness, level of stress and turnover rates.

+ using digital images from cameras, videos and deep learning models.

SKU image auto-generation (computer vision model)

+ our model analyzes thousands of plant and pot combinations.

this process takes several seconds, whereas for a human it takes one week.

+ the 'reaction' is the recommendation.

the computer vision model is used to produce the most case-accurate recommendation, comparing the stock and the client's input data i.e. the amount of workers and size of the office.

machines can identify and classify objects and then react' to what they see.

+

our current ai systems achieve 85% accuracy with SKU recommendations

our system started at 30% in SKU generation.

just like plants, our technology is growing.

30%

we trained the system to achieve an 85% accuracy rate.

85%

with the current trend in growth, we forecast our model to achieve 95% accuracy

95%

+

we are training our system to consistently grow in accuracy. by evaluating SKU (stock keeping units) accuracy and validating the best outcomes, we are improving the efficacy of our algorithm exponentially. the more data it processes, the more accurate our computer vision model becomes.

+

our proposal recommendation system

the best way to explain our machine-learning recommendation system is by thinking of Spotify or Netflix's algorithms that recommend the most appropriate music or movies, based on your data.

if you tried to find new music without such a tool, it would take you a lot more time. we're applying this same concept to our proposal generator.

real landscape designers approve or disapprove past projects, teaching our system what to recommend.

+ datasets validated by real designers.

the crux of AI technology is improvement. our system is constantly teaching itself to be more accurate and more efficient.

+ machine learning means constant improvement.

our proposal recommendation system not only widens the design options for our clients, but saves a huge amount of time.

+ saving time and cost.

matrix factorization

our recommendations use what is called matrix factorization — a class of collaborative filtering algorithms used in recommender systems.

the system allows lower user interaction, by employing two underlying matrices. this recommendation method was made famous by Simon Funk in the Netflix Challenge.

more time = more proposals

average time for a proposal (human)

opportunity cost

1 week

average weekly salary (US) for landscape designer

$1,100

average time for a proposal (Livee)

4 seconds

+

We are training our system to consistently grow in accuracy. By evaluating SKU (stock keeping units) accuracy and validating the best outcomes, we are improving the efficacy of our algorithm exponentially. The more data it processes, the more accurate our computer vision model becomes.

happiness impact + ecosystem: transformer-based language models for sentiment analysis

we aim to use monitors, censors and data on turnover rates, sick leave and KPI achievement to formulate the tangible effect of landscaping on humans in the workplace. to do this, we will set up monitors 2-3 weeks before installing greenery and then make a comparative analysis.

+

the psychological benefits of greenery.

our happiness + indexes happiness as something tangible that we can improve through landscaping. this is why this is so important.

+ reduce stress

+

+ increase productivity

+ lighten mood

+ boost energy level

+ improve concentration

+ promote social interaction

ai does not replace humans, it improves their capacity.

machine learning is superseding human labor in areas such as design. but this doesn't mean that designers will no longer have jobs. it means that they can stop spending unnecessary time on routine tasks which our systems can do far more faster and efficiently.

in turn, designers can process more leads and commit their time to more projects. machine learning does not have to replace human labor, rather, it makes it more efficient.

happiness + efficiency

countless studies have shown the positive psychological outcomes of plants and greenery in the workplace. but due to the logistical complexity of commercial landscaping and the significant design and administration costs, too many workplaces are not facilitating optimal working environments. we aim to smoothen this transition.

stability.
productivity.
agility.

fast.
easy.
effective.

like magic.

for free.

real.

© 2021 ai-powered service for
a rapid corporate greenery prototyping. made in greenpact futurelab.