LABOT

Automation of design process aiming to expedite achieving optimized solutions while reducing repetitive design tasks

The problem that we’re committed to solving is the issue of providing “good” homes in a timely manner. A statistic supports that the world needs more than 2 billion new homes over next 80 years and the housing demand is even worse in urban areas due to urban concentration. According to Forbes, by 2050, our urban population will double. The problem is even more serious in countries where the population is growing steeply but the supply of housing has not caught up the demand

To solve the problem, we develop an automation process for high-rise residential building design. This automation process revolutionarily reduces the time and cost to design, and helps find optimal solutions through multiple factors. More specifically, LABOT makes the feasibility study phase quick with the real-time capability where as architects would need 3 to 6 weeks within a small amount of fee or their own marketing budget. In general, design options are selected mainly by aesthetical reasons even though target numbers such as unit mix ratio, total unit number and construction cost are asked to be met. LABOT takes all these factors and helps choose the most optimized design solutions.

There are two key innovations in our automation process. First, we have introduced to build a database for all possible building designs under specific constraints, for example unit-mix ratio or floor area, automatically. This allows users to compare and choose the most optimized or preferred options among millions of others. Second, we have developed a scoring system to evaluate each generated building design option and show the top solutions in real-time. The scoring system is essential for residential unit layout for locating expensive units in expensive spots. We would like to remark that the scoring system in the first demo is made to work the same way how high-rise apartments units in reality are valued.

We believe our solution can revolutionize the way people work in the building design process. Architects will be able to focus on creative tasks rather than generating repetitive design options. Since tedious tasks in a project can be taken by LABot, architects are free from repetitively adjusting design to meet target numbers. LABot also enables to choose the most optimized design solution with various factors taken into consideration at once. This ability means a lot to the current practice because this not only counts for conditions in the preconstruction phase but also does for after-construction. For instance, the orientation, shape, and size of a tower can be determined based on the analysis of daylight, cost for heating and cooling along with construction cost and revenue. The design solutions can easily be energy efficient, profitable, and/or cost effective from all parameters, which has not been possible.

Our solution is innovate in the AEC industries where any data-driven building design process has never done. Although Data-driven decision making has become popular such as Amazon’s online shopping where customers filter goods with conditions and filtered goods are shown by the probability that the customer would purchase, which is processed through its own scoring system.

We apply this idea into designing high-rise residential buildings. Given the total unit-mix of a building, we save all possible building designs options  having similar unit-mix value to the input. We have developed a way to filter and select the best options in real-time.

Technology is not only important for our solution; it is how LABOT was born. As users input data such as total number of units and unit mix, LABOT instantly generates millions of possible options within given site parameters. These results are evaluated in real-time with a scoring system, and the top scored design options are visualized as floor plans and 3D building forms on a dashboard as well as through VR. The latest technology is essential for this  real-time process of generating a large number of design options and developing the scoring system synchronized with real values in practice.

We want to achieve three main goals over the next 12 months. Our first goal is to keep enriching our database with relevant data such as apartment prices, construction cost, projected revenue among dozens of others. Our second goal is to improve performance of evaluation system synchronized with value of residential buildings in reality. Our third goal is to establish a web-based platform where users can easily input their data, adjust their desired goals, then visualize the results. Specifically, the platform will be developed to include visualization tools such as parallel coordinate, evaluation dashboard, and eventually a real-time VR.

In the next 12 months, we are planning to get four to six projects. Once we set up the web-based service, we expect more than two hundred subscribers a year. In the three years, we want to be at a stage running our product weekly for new clients and the number of the subscribers shall be over five hundred.

We would seek for opportunities through our network and by attending relevant conferences. We want to establish a web-based service and work with real estate developers and/or architects as consultant on project basis. Thus, any individual who are interested in LABOT will be able to experience our product online and any business entities can get our service directly.

We are at a stage looking for an architectural design firm to have their projects tested with our product so we can fine tune it and start to collect data from real projects.

In the next 12 months, we are planning to get four to six projects. Once we set up the web-based service, we expect more than two hundred subscribers a year. In the three years, we want to be at a stage running our product weekly for new clients and the number of the subscribers shall be over five hundred.

We have the ability to integrate building design knowledge specifically in the high-rise residential type and software engineering. Our team has experienced experts in the field of architectural design, building energy, mathematics and computer science. We also have the ability to utilize our network for business development and collaboration.

We are going to be a for-profit company and will provide custom service to business entities  and offer subscription-based web service for individual and corporate users. With the custom consultancy model, we would charge $2,000 to $5,000 depending on projects for couple hours to a day. In the subscription-based web service, we would charge individual users $250 monthly and corporate users at a higher price.

We would like to introduce our idea to Solve community and get supports. Since having developed the idea, we have realized communicating with people with different perspective is critical to improve and expand the idea. Also, we hope to find collaborators in the community to apply our solution to prove related problems such as energy conservation of buildings and reducing construction cost.

A couple of key barriers are sharing the idea with bigger communities and collecting building design data from the relevant industries. We are very confident in our idea and believe the building design automation will be the way how buildings will be designed even if it might not be LABot. Thus, it’s important to share it with the overall AEC industries to get supports and collaborate for developing workflows and streamlining processes because this will affect everyone of us. By doing so, collecting data can be easier and making meaningful tools that work for related entities are highly probable.