Table Of Contents
Table Of Contents
We raised Series A round led by Mucker Capital. Here's what we are going to do with it.
2020 is shaping up to be a massive year for our data driven design platform with 20% month on month growth. Historically, we have spent proportionately way more on research and development than a typical software company and that will not stop any time soon. This funding will go straight into building great new features at the pace you have come to expect from us. Last year, we shipped 25 new types of analysis saving hundreds of hours and thousands of dollars per project. Now we are accelerating that pace and doubling our research and development team.
Substantially improving our existing products, especially for architects, engineers, contractors, and manufacturers.
Powered by machine learning, cove.tool is an easy-to-use platform that helps architects, engineers and contractors optimize sustainability and energy efficiency while saving on project costs. By automating the tedious task of energy modeling, cove.tool eliminates the need for manual labor, cutting what once took consultants 150 hours to do manually into just 30 minutes. For instance, cove.tool helps builders achieve energy, daylight, glare, radiation, water and embodied carbon targets for new and existing buildings and also offers the ability to compare different options, such as finding the cheapest way to meet a building’s energy targets. Since the onset of the COVID-19 pandemic, we have also added a COVID occupancy tool to its platform.
cove.tool customers have built nearly 11,015 projects in over 22 different countries to save $650M and achieve 24,028 LEED points with ease.
We’ve come a long way since we launched cove.tool in 2018. We could not have done this without great partners and customers at our side as we pursue our mission to help significantly reduce the cost to comply with regulations and design performance targets. So at a high level, here is what we are working on next.
Part I: Automate workflows for other professions, stitch databases together, add intelligent feedback loops, and integrate manufacturers into a marketplace.
The next step is to begin tying more professions together by moving outside of building performance modeling to structural, mechanical, electrical, plumbing. Each profession opens a completely new user base and gets more project collaboration between professions. Many tools in the AEC that are data-based suffer from an active data entry. Essentially, someone has to call an architect, contractor, owner, or engineer and then enter the data manually. This makes construction related data dirty and obsolete. Instead, data generated passively by users doing something else ensures that data is accurate. This is an opportunity for connecting to the manufacturer’s databases making cove.tool even more accurate.
Part II: Build the architecture machine that conforms to the unique needs of each participant, suggests innovative solutions as a true partner, and learns and improves.
“Physical form is the resolution at one instant of time of many forces that are governed by rates of change” – D’Arcy Thompson, 1917
It is vital to move beyond just running models faster or just displaying simple manifestations of cause-effect. Showing more options is not helpful as insight requires using design judgement to balance competing objectives. This means the machine must give unsolicited feedback based on the context of the task at hand.
To understand that context, the machine must have all the different professions working in the same shared digital workspace. A large data set of projects for each firm trains an AI system to uniquely respond to each company's needs and begin providing suggestions and prompts to users. This two-way conversation takes on the quality of a dialog between a senior and junior architect. Designing humans into the loop is vital for avoiding deterministic workflows as that dialog continually changes the context providing impetus to discover more interesting solutions.
Maintaining a common AI engine behind the scenes simplifies and streamlines the software development effort by giving users one core application that manages everything and then each participant in the process views an interface that is customized to their skillset and expertise. The machine will learn about the particular capabilities and workflow and reveals more or less information depending on the person using it. Watching thousands of participants work will help us identify trends and alert teams to bottlenecks in their workflow. The machine managing everything behind the scenes allows each professional to check out the building model, do their work, and merge their changes back into the main design without fear of causing coordination errors similar to a Git. Checking out branches parallelizes the work allowing projects to be completed in a few weeks rather than months or years saving owners and design teams millions of dollars in construction and business costs.
Automation, connecting participants, big data processing, and training AI to manage the complexity of the design process is just the beginning of the journey for our dynamic and data driven team.
A Big Thank you!
Thanks to every architect, engineer, contractor, and developer that has pushed their firm's leadership to adopt new, more effective workflows that cove.tool offers. Your feedback has been pivotal in shaping the platform, and we will continue to rely on that as you help shape the product road map. And of course, a big thank you to the fantastic cove.tool team. We are honored to get the opportunity to work with some of the most talented and driven folks in the industry. We can not wait to show you what is coming next!
Buildings account for 39% of carbon emissions and if we realistically hope to bend the curve on climate change, we have to automate simulations for better design choices by architects, engineers and contractors.