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AiCorb: Tackling the Designer's Dilemma and Risks with AI-Powered Rapid Prototyping

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Description

Designers often face the dilemma of unlimited options and a limited timeframe. AiCorb is an ambitious AI design tool made to address this issue. Established in 2017 as a collaboration between a top Japanese general contractor and a pioneering U.S. research institute, AiCorb currently focuses on facade study and 3D modeling, offering two key features: interpreting a designer's intent from sketch to design, then converting from design to building information modeling (BIM) via a Revit plug-in. As a result, designers can cut down their work time for a mid-sized building feasibility study from eight weeks to less than one week. This class will also explore how AiCorb possibly plans to implement a feedback system that will generate optimal facade designs based on engineering analysis simulation results from apps like Autodesk Forma software. Participants will discover the potential of AI to enable "rapid prototyping" in the early design stages, and they'll gain insights into an innovative design workflow where the designer helps the client make better-informed decisions.

Key Learnings

  • Learn about streamlining the early design phase through AI-powered rapid prototyping to help the client make better-informed decisions.
  • Explore the potential of "emotional digital design" using AI to support design creativity from the designer's intent.
  • Learn how to use existing data for optimal suggestions and risk evaluations as "theoretical digital design."

Speakers

  • Avatar for Takuma Nakabayashi
    Takuma Nakabayashi
    Interested in the utilization of machine learning, computer vision, xR, and generative design in the AEC industry. I have started my career of research from computational design, and it is expanding toward the utilization of computer vision and machine learning for productivity improvement of the AEC industry after I experienced in working as a site manager. I’m now in charge of some promising projects which utilize latest ICT technologies, and collaborating with many startups. One of my recent projects which newly released is AI-powered architecture design process, AiCorb. We aim to help architects in terms of ideation and building concensus process.
  • Shusaku Kawamoto
    Shusaku Kawamoto is the Innovation Lead of Asia Digital Lab (ADL) by Asia Pacific Regional Headquarters, Obayashi Corporation, located in Singapore. ADL serves as Asia's gateway to Obayashi's global innovation network, which includes the Integrated Product Design Center (iPD Center) and, Technical Research Institute in Japan, and Silicon Valley Ventures & Laboratory in the US. With over 25 years of experience at Obayashi, Kawamoto has tackled engineering and business challenges on a grand scale. He has successfully led mission-critical building construction projects and consultation services in Japan, Taiwan, Singapore and Indonesia, earning an excellent reputation with clients. Kawamoto's expertise lies not only in delivering projects but also in leading cross-functional teams at ADL to bring innovation to life. His goal is not only to improve productivity but also to create new value in the built environment by integrating digitalization and sustainability. Currently, he is spearheading a new initiative to develop leading-edge VDC talents across Japan and the APAC regions through open innovation activities with great support from Japan, Singapore, and US Autodesk teams. One of his priorities is to scale up Obayashi's cloud migration to achieve a single source of truth in project data environments with Autodesk Construction Cloud, under the slogans "Beyond BIM" and "Democratize digital." He has also been promoting the "Smart BIM Standard (SBS)," which Obayashi originally developed for internal use and recently released to the public to share its benefit more widely. In addition to his work in innovation, Kawamoto is passionate about mentoring and coaching others in this field, always looking for opportunities to share his knowledge and skills. His leadership and innovative mindset have helped Obayashi stay ahead in the ever-evolving digital landscape and contribute to Asian society.
  • Yoshito Tsuji
    Tsuji's architectural, urbanism, and innovation career spans continents and roles. He began his journey at Kyoto University and pursued advanced studies at Harvard GSD. In 2013, Tsuji made a significant impact in Omotesando, Tokyo, with the completion of oak omotesando, a prominent office and commercial complex. This project, in collaboration with several artists, enriched Omotesando's skyline, known for fashion and cultural trends. In 2016, Tsuji led the design of the first R&D center outside the U.S. for a major U.S. IT company. This project required a design sophistication similar to the company's flagship smartphone and adherence to an 18-month design and build timeline, achieved through extensive mock-ups and design studies. From 2005 to 2009, Tsuji was part of the design management team for a Private Finance Initiative project to rebuild the House of Representatives building, showcasing his versatility in design and project management. Since 2017, he has been a member of SVVL, an open innovation organization by Obayashi Corporation in Silicon Valley, where he led the development of AiCorb, an AI-powered design assistant tool officially press-released in March 2022. In 2020, Tsuji moved to Singapore as Design Lead for the APAC region of Obayashi Group Companies while continuing to lead AiCorb's development. In 2022, he launched the Asia Digital Lab (ADL) as Chief Innovation Officer, focusing on digital transformation, robotics, decarbonization, and well-being technologies in Asia. In 2024, ADL was reorganized into Obayashi Construction-Tech Lab Singapore (OCLS) under Obayashi Technology Division. OCLS focuses on advancing robotics in construction and fostering a technology and business ecosystem between Japan and Singapore. Tsuji is the Chief Innovation Officer at OCLS, operating in both Japan and Singapore.
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Transcript

YOSHITO TSUJI: Today, we would like to introduce AiCorb, our new AI design assistant developed by Obayashi Corporation. Also, we will talk about the concept of AI-powered rapid prototyping, a new design process using AiCorb. Hope you enjoy our presentation.

Let me briefly introduce Obayashi Corporation. Obayashi Corporation is Japan's leading general contractor, operating in Japan, United States, and APAC regions over 130 years. In addition to construction, Obayashi has expanded our business field into urban development and renewable energy.

What is AiCorb? AiCorb is an AI Design Assistant. However, not just a design generator. Gaudi said, "Human do not create, they discover, and upon those discoveries, they build." We think AiCorb will be a powerful assistant for exploring discoveries for architects.

This is a key takeaway of our presentation. Our new proposed design process, AI-powered Rapid Prototyping, will enable early client agreements and increase their satisfaction. Also, it will solve design dilemma and reduce project risks. Also, it will bring broader stakeholder info-sharing and social benefit. By AI-powered Rapid Prototyping, we realize better informed decision-making with all stakeholders.

Before we jump in more detail, let me introduce us. Myself, Tsuji is an architect and working at Obayashi Corporation for 30 years. Designed R&D center, office building, and high end boutique and so on. At the same time, I also Chief Innovation Officer of Obayashi Construction Tech Lab Singapore, which is promoting robotics construction in Singapore and other APAC countries. Let me introduce Takuma.

TAKUMA NAKABAYASHI: Thank you, Yoshito. My name is Takuma Nakabayashi, and I'm an AI researcher responsible for AI-related technology development at Obayashi Corporation. I spent about three years here in Silicon Valley, California, conducting research and development for the AiCorb project that I'll be introducing today. It's a pleasure to be back here and to have the opportunity to present AiCorb to you. Now, I pass back to Yoshito.

YOSHITO TSUJI: OK. Thank you, Takuma. Let us start by telling you why we developed AiCorb. I will be the client, and Takuma will be the architect, so you can feel familiar more with our story. Please enjoy it.

[KEYBOARD CLICKS]

[CLICKS]

[EXPLOSION]

Enjoy. Have you ever had an experiences like this? This is everyday life for architects. Even I bought my idea that I was very confident. But sometimes they are completely defeated by clients. Very difficult to convince clients.

In the early design stage, usually it is not clear what client want to build. Even in such a situation, architect should explore the client intention, sometimes hidden idea by presenting several different options, considering building codes, costs, and other factors.

For medium sized office building case, these composition during the feasibility study often takes a couple months, but sometimes all the steps can be wasted. How quickly you agree with a client is very important.

If we draw this situation in the diagram, it would be like this. Although the architect is always creating design options, client decision-making is always delayed. So usually, there's a large gap between creation by architect and decision-making by client. This gap would be enlarged in concept design stage and early basic design stage. This gap became design dilemma and project risks.

In my previous project for US IT client seven years ago, I always require to present several options, and I consider every possible option. At the end of the project, my hair came completely gray, like this. Not like this manga guy.

Dilemma is categorized into two. One is dilemma of creativity. The other is dilemma of efficiency. However, each dilemma appears differently, depending on whether you are looking at it from client side or architect side. We always have give and take between the two sides. Let's look at more detail.

Architect explore unlimited option within limited time, and client usually ask architect to propose something new and interesting. Many architect refer to past and similar examples. In my case, I have sometimes copy and paste my previous work and sketch over it.

Using my hands, allow me to experience that joy of creation. Physical cut and paste is faster and better for me, but other persons prefer other way. However, in general, there is not enough time to explore new and interesting ideas, and architect must keep thinking. We must do quick and quick.

Dilemma in efficiency is even more complicated. Even if multiple options are selected by client, to obtain specific figures, such as costs, schedule, and environmental score of each option, is so tough. If architect push design team so hard, often everyone say, cannot be done until other profession finish.

Sometimes argument aligns between architects and engineers. In many cases, most of figures are not available until the end of the design stage, so client cannot get sufficient information for their decision-making.

We developed the concept of AI-powered Rapid Prototyping to fill the gap between creation of architect and decision-making by client, by solving these two dilemma and reducing project risks by AI.

Focusing on concept design stage. Our AiCorb will be a design assistant that enables architect to provide clients with the right information at the right time. By this, client can get a better informed decision-making. OK, let me pass to Takuma to explain about AiCorb capability. Takuma, please.

TAKUMA NAKABAYASHI: Thank you. So, from now on, I'm going to explain the overall structure of AiCorb to resolve the dilemma and risks we've discussed so far. We propose a method called AI-powered Rapid Prototyping through the development of AiCorb. But first, please watch this concept video about AiCorb.

[VIDEO PLAYBACK]

- Creation continues uninterrupted through humans. But humans do not create. They discover. And upon those discoveries, they build. Antoni Gaudi. In pursuit of a better future, we give design to our discoveries to build an ever new world. Those who came before us were in constant progress.

How did they turn their dreams into reality? As architects, dialoguing with our clients, we strive to make the best possible design proposals through a process of trial and error. A conversation between design and oneself has provided a key to solving the profound dilemma between creativity and efficiency. Now, in the age of AI, we're developing an innovative new design process, the AiCorb.

The AI in AiCorb instantly generates a variety of design ideas from a simple sketch. It can read the intentions behind any sketch and reflect those in a design. The generated image can then be modified directly by applying further sketch or design instructions, leading quickly to an ideal design.

Additionally, the AI within AiCorb can generate a 3D model from that ideal design. More easy to conceptualize than a simple picture, a 3D view facilitates discussion of various factors, like environmental or cost analysis.

AiCorb delivers appropriate information at the right time, empowering clients to make the best decisions. This not only solves the architect's dilemma, but also promotes better informed decision-making between clients and other shareholders.

It is a revolutionary solution that assists architects in the balance between creativity and efficiency. The process of design approaches new horizons with AiCorb.

[END PLAYBACK]

TAKUMA NAKABAYASHI: OK, thank you for watching the video. Since its past press release in 2022, AiCorb has been featured in numerous media outlets. It was included as a leading example of image-generating AI in Japan's Information and Communication white paper, and was even used on a TV variety show to design the ideal homes for comedians.

The AiCorb 3D modeling function that I'll introduce shortly, is expected to be available on the Autodesk App Store in early October. I'll provide more details on this feature later, but with this plugin, you can quickly convert generated proposed design concepts into 3D models. Please give it a try.

Before introducing the specific feature of AiCorb, let me explain how we use AI to tackle the dilemmas and risks we mentioned. This is a traditional architectural design workflow. First, based on site conditions, regulations, and the client requests, we consider the building's volume, floor plans, and elevations.

Once these are decided, we will move on to the interior and exterior designs. Design isn't just about appearance. It must also take into account factors like cost and the environmental performance. To evaluate these, architects typically create 3D models manually and run various assessments.

Based on the results, they then adjust the design and make proposals to the client. In the process of building consensus, this cycle is repeated again and again. And as just mentioned, many proposals end up being rejected in this cycle.

The issue is, that design and 3D modeling will take considerable time, and having to repeat this process again and again. Let's do the limits and risks. To help reach a satisfying goal with fewer iterations in the consensus building cycle, we developed AiCorb.

Currently, AiCorb serves as a tool to assist in the design and model stages. With AiCorb Facade Design Function, architects can generate various design options from sketches using generative AI based on the architect's intent and the client's needs.

The AiCorb 3D modeling function helps turn these generated images into 3D models. By using these AI features, architects can present new proposals to clients much earlier in the process.

Now, let me explain more concretely how AiCorb has been addressing these dilemmas and risks. First, please watch this video on the Facade Design Function.

[VIDEO PLAYBACK]

- Design decisions require ongoing dialogue between the client and architect. With AiCorb Facade Design Function, you can visualize the client's ideal design from the first meeting. AiCorb Facade Design Function is intuitive to use. Even if you are not an architect, it brings your design ideas to life.

You can explore further from your favorite design. You can add note to the sketch and make partial edits to the generated design. Design exploration used to start with searching real world examples, but now AI lets you generate entirely new ideas. AiCorb helps you make design decisions with greater satisfaction.

[END PLAYBACK]

TAKUMA NAKABAYASHI: OK, thank you for watching the video again. As shown in the previous video, AiCorb places greater emphasis on sketches. Our AI is specifically trained to understand an architect's intent, even from rough sketches.

Why do we focus so much on rough sketches? The result, the dilemmas, and risks faced in the early stages of architectural design. We believe this function must be something that can be used quickly during meetings.

With the rise of computational design, some architects prefer exploring possible designs using 3D models. However, making quick adjustments during a meeting is challenging. Adjusting a few parameters might be possible, but changing the overall feel of a design is not easy.

On the other hand, sketches have no such limitations. They allow for quick changes to the building's volume, or adding symbolic details to the facade. And that's why we still believe that sketches are the most effective tool for architects to quickly express their intent during discussions, especially when design changes are needed repeatedly.

With AiCorb, various options can be generated, even from rough sketches. We believe that the sketch to design flow using generative AI is the optimal method for optioneering, which is crucial in resolving dilemmas. It's rare for clients to fully articulate their design requests at the start of the project, so it's common for architects to present numerous options to them.

However, preparing options that aren't selected also takes time. Now, this is a real dilemma architects face. They often end up creating more design options than are really necessary. But if you can confirm the client's real preferences during the past meeting, this dilemma can be resolved.

The benefits of using generative AI go far beyond just speeding up design exploration. Traditionally in the early stages of architectural design, architects would expand their ideas by referencing existing examples. But the options you can search for are always limited. And, of course, you can search for something doesn't exist.

With the arrival of generative AI, the ideation process has transformed dramatically. Now architects can generate limitless new designs without being restricted by existing examples. This means you are not just searching for ideas, but also, you are bringing the images in your mind to life, and verifying them on the spot.

Generative AI is particularly powerful in combining different elements, like mimicking specific branches in architecture or blending architecture with a particular culture style, era, or even living creatures. This allows architecture architects to instantly explore entirely new concepts. It's a tool that will spark new inspiration in the world of architecture.

So far, I have explained how AiCorb 's Facade Design Function helps overcome dilemmas. As you've seen, generative AI can produce incredible visuals. However, we cannot propose buildings that are unbuildable or too expensive or lack environmental performance.

These are not things that can be judged from one image alone. And clients cannot make decisions based solely on a beautiful picture. In other words, a great picture alone is nothing more than pie in the sky.

So, we looked for a way to go beyond just creating images. We thought about how to use this amazing technology to better support consensus-building, and provide a practical solution to the real dilemmas and risk faced by architects and clients. This led to the development of the AiCorb Modeling Function. First, please watch this video.

[VIDEO PLAYBACK]

- For design decisions, clients need quantified information, like environmental performance and cost. They can't be understood by appearance alone. These aspects can only be evaluated once the design is turned into a 3D model.

AiCorb Modeling Function assists in converting designs into 3D models. It reads details, such as window sizes and materials, from a design image, and reflects them as Revit family parameters.

Let's give it a try. Apply this design to the lower levels. And then apply this design to the upper levels. The extracted designs are stored in the palette, allowing for easy comparison of different option.

By using the AiCorb Modeling Function, you can evaluate design proposals with numerical data, helping clients make quicker decisions. Let's start using AI, not just to create beautiful images, but to take design further.

[END PLAYBACK]

TAKUMA NAKABAYASHI: OK, thank you again for watching the video. So, as shown in the video, this feature uses AI to extract the facade characteristics and converts them into parameters, like tunnel aspect, window aspect. window depth, volume thickness, and panel color, as illustrated here.

As I mentioned earlier, this feature is scheduled to be available on the Autodesk App Store in early October, and will function as a plugin for Revit. We concluded that converting the generated images into parametric 3D models is necessary to address the dilemmas and risks we have discussed so far.

In recent years, AI technology for estimating realistic surface models from photos has become mainstream. While this is an impressive technology, and actually, I'm personally excited about it as a researcher, but we see it as an extension of image-based techniques. In other words, it's mainly focused on visualization.

What clients need for decision-making, quantitative details, such as environmental performance, cost, and various other performance metrics. To analyze these aspects, parametric 3D model is required. We adapted this approach to perform quantitative analysis and present the results to clients early in the process, ultimately speeding up decision-making for better informed decisions.

As shown here, the 3D modeling function allows us to easily convert generated parts of the images into 3D models. On these 3D models, we can extract real scalar data, such as the area of opening and the shape of the exterior. This allows us to provide approximate, but quantitative evaluations, such as sunlight exposure and environmental performance early in the process, which can be presented to the clients.

While inside, we began through our efforts and that resolving the dilemmas and risks faced by clients and architects requires an approach that optimizing both the emotional and theoretical aspects in architectural design. Although failing only one of these aspects is not enough, you can't make a decision based on just one side.

Up until now, most of the technological developments aimed at improving design efficiency have focused on the theoretical aspect, particularly those related to analysis and plan part. There are several reasons for this, but one of the main reasons, is that we simply didn't have the technology to effectively address the emotional aspect.

However, now generative AI is beginning to change the situation. Many of you have likely been amazed or moved by images, music, or videos generated by AI. But even so, generative AI by itself cannot fully bridge the gap between the emotional and theoretical aspects.

Understanding this challenge, we created AiCorb as a tool to support the proposal of emotionally impactful designs, and linked them to quantitative analysis. With this approach, we can resolve the kinds of dilemmas and risks shown here, and achieve smoother, more convincing decision-making. OK, now it's time to wrap up, so let me hand it back to Yoshito again.

YOSHITO TSUJI: OK. Takuma, thank you for a good presentation. Let me recap the key takeaway. We propose a new design process, AI-powered Rapid Prototyping. It will enable early client agreement and increase their satisfaction. Also, it will solve a design dilemma and reduce project risks. And also, it will bring broader stakeholder info-sharing and social benefit.

By AI-powered Rapid Prototyping, we realize better informed decision-making for all stakeholders. We introduce AiCorb, a design assistant, to realize this. AiCorb works on concept design stage, and enable architect to provide client with the right information at the right time.

Although development is still in progress, we are striving to realize a new design process. Our vision is that AiCorb will solve design dilemma and reduce project risks, not only for the client and architect, but also for the multidisciplinary team, by sharing information generated through AiCorb.

Furthermore, we believe that better informed consent can be created by sharing various information from the early stage of the project, not only with the project team, but also with various outside stakeholders.

AiCorb is a powerful assistant for exploring this vision. We collaborated with all these people until now. We would like to express our gratitude to SRI International, Autodesk Research, Hyper, Geo, and many individuals. Thank you so much.

As a final, me introduce our LinkedIn page, as shown here, together with Obayashi Construction Tech Lab Singapore. Please check it. This is the end of our presentation. Thank you for listening. Thank you.

______
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We use Optimizely to test new features on our sites and customize your experience of these features. To do this, we collect behavioral data while you’re on our sites. This data may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, your Autodesk ID, and others. You may experience a different version of our sites based on feature testing, or view personalized content based on your visitor attributes. Optimizely Privacy Policy
Amplitude
We use Amplitude to test new features on our sites and customize your experience of these features. To do this, we collect behavioral data while you’re on our sites. This data may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, your Autodesk ID, and others. You may experience a different version of our sites based on feature testing, or view personalized content based on your visitor attributes. Amplitude Privacy Policy
Snowplow
We use Snowplow to collect data about your behavior on our sites. This may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, and your Autodesk ID. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. Snowplow Privacy Policy
UserVoice
We use UserVoice to collect data about your behaviour on our sites. This may include pages you’ve visited. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our platform to provide the most relevant content. This allows us to enhance your overall user experience. UserVoice Privacy Policy
Clearbit
Clearbit allows real-time data enrichment to provide a personalized and relevant experience to our customers. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID.Clearbit Privacy Policy
YouTube
YouTube is a video sharing platform which allows users to view and share embedded videos on our websites. YouTube provides viewership metrics on video performance. YouTube Privacy Policy

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Adobe Analytics
We use Adobe Analytics to collect data about your behavior on our sites. This may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, and your Autodesk ID. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. Adobe Analytics Privacy Policy
Google Analytics (Web Analytics)
We use Google Analytics (Web Analytics) to collect data about your behavior on our sites. This may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. We use this data to measure our site performance and evaluate the ease of your online experience, so we can enhance our features. We also use advanced analytics methods to optimize your experience with email, customer support, and sales. Google Analytics (Web Analytics) Privacy Policy
AdWords
We use AdWords to deploy digital advertising on sites supported by AdWords. Ads are based on both AdWords data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that AdWords has collected from you. We use the data that we provide to AdWords to better customize your digital advertising experience and present you with more relevant ads. AdWords Privacy Policy
Marketo
We use Marketo to send you more timely and relevant email content. To do this, we collect data about your online behavior and your interaction with the emails we send. Data collected may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, email open rates, links clicked, and others. We may combine this data with data collected from other sources to offer you improved sales or customer service experiences, as well as more relevant content based on advanced analytics processing. Marketo Privacy Policy
Doubleclick
We use Doubleclick to deploy digital advertising on sites supported by Doubleclick. Ads are based on both Doubleclick data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Doubleclick has collected from you. We use the data that we provide to Doubleclick to better customize your digital advertising experience and present you with more relevant ads. Doubleclick Privacy Policy
HubSpot
We use HubSpot to send you more timely and relevant email content. To do this, we collect data about your online behavior and your interaction with the emails we send. Data collected may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, email open rates, links clicked, and others. HubSpot Privacy Policy
Twitter
We use Twitter to deploy digital advertising on sites supported by Twitter. Ads are based on both Twitter data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Twitter has collected from you. We use the data that we provide to Twitter to better customize your digital advertising experience and present you with more relevant ads. Twitter Privacy Policy
Facebook
We use Facebook to deploy digital advertising on sites supported by Facebook. Ads are based on both Facebook data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Facebook has collected from you. We use the data that we provide to Facebook to better customize your digital advertising experience and present you with more relevant ads. Facebook Privacy Policy
LinkedIn
We use LinkedIn to deploy digital advertising on sites supported by LinkedIn. Ads are based on both LinkedIn data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that LinkedIn has collected from you. We use the data that we provide to LinkedIn to better customize your digital advertising experience and present you with more relevant ads. LinkedIn Privacy Policy
Yahoo! Japan
We use Yahoo! Japan to deploy digital advertising on sites supported by Yahoo! Japan. Ads are based on both Yahoo! Japan data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Yahoo! Japan has collected from you. We use the data that we provide to Yahoo! Japan to better customize your digital advertising experience and present you with more relevant ads. Yahoo! Japan Privacy Policy
Naver
We use Naver to deploy digital advertising on sites supported by Naver. Ads are based on both Naver data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Naver has collected from you. We use the data that we provide to Naver to better customize your digital advertising experience and present you with more relevant ads. Naver Privacy Policy
Quantcast
We use Quantcast to deploy digital advertising on sites supported by Quantcast. Ads are based on both Quantcast data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Quantcast has collected from you. We use the data that we provide to Quantcast to better customize your digital advertising experience and present you with more relevant ads. Quantcast Privacy Policy
Call Tracking
We use Call Tracking to provide customized phone numbers for our campaigns. This gives you faster access to our agents and helps us more accurately evaluate our performance. We may collect data about your behavior on our sites based on the phone number provided. Call Tracking Privacy Policy
Wunderkind
We use Wunderkind to deploy digital advertising on sites supported by Wunderkind. Ads are based on both Wunderkind data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Wunderkind has collected from you. We use the data that we provide to Wunderkind to better customize your digital advertising experience and present you with more relevant ads. Wunderkind Privacy Policy
ADC Media
We use ADC Media to deploy digital advertising on sites supported by ADC Media. Ads are based on both ADC Media data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that ADC Media has collected from you. We use the data that we provide to ADC Media to better customize your digital advertising experience and present you with more relevant ads. ADC Media Privacy Policy
AgrantSEM
We use AgrantSEM to deploy digital advertising on sites supported by AgrantSEM. Ads are based on both AgrantSEM data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that AgrantSEM has collected from you. We use the data that we provide to AgrantSEM to better customize your digital advertising experience and present you with more relevant ads. AgrantSEM Privacy Policy
Bidtellect
We use Bidtellect to deploy digital advertising on sites supported by Bidtellect. Ads are based on both Bidtellect data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Bidtellect has collected from you. We use the data that we provide to Bidtellect to better customize your digital advertising experience and present you with more relevant ads. Bidtellect Privacy Policy
Bing
We use Bing to deploy digital advertising on sites supported by Bing. Ads are based on both Bing data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Bing has collected from you. We use the data that we provide to Bing to better customize your digital advertising experience and present you with more relevant ads. Bing Privacy Policy
G2Crowd
We use G2Crowd to deploy digital advertising on sites supported by G2Crowd. Ads are based on both G2Crowd data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that G2Crowd has collected from you. We use the data that we provide to G2Crowd to better customize your digital advertising experience and present you with more relevant ads. G2Crowd Privacy Policy
NMPI Display
We use NMPI Display to deploy digital advertising on sites supported by NMPI Display. Ads are based on both NMPI Display data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that NMPI Display has collected from you. We use the data that we provide to NMPI Display to better customize your digital advertising experience and present you with more relevant ads. NMPI Display Privacy Policy
VK
We use VK to deploy digital advertising on sites supported by VK. Ads are based on both VK data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that VK has collected from you. We use the data that we provide to VK to better customize your digital advertising experience and present you with more relevant ads. VK Privacy Policy
Adobe Target
We use Adobe Target to test new features on our sites and customize your experience of these features. To do this, we collect behavioral data while you’re on our sites. This data may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, your IP address or device ID, your Autodesk ID, and others. You may experience a different version of our sites based on feature testing, or view personalized content based on your visitor attributes. Adobe Target Privacy Policy
Google Analytics (Advertising)
We use Google Analytics (Advertising) to deploy digital advertising on sites supported by Google Analytics (Advertising). Ads are based on both Google Analytics (Advertising) data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Google Analytics (Advertising) has collected from you. We use the data that we provide to Google Analytics (Advertising) to better customize your digital advertising experience and present you with more relevant ads. Google Analytics (Advertising) Privacy Policy
Trendkite
We use Trendkite to deploy digital advertising on sites supported by Trendkite. Ads are based on both Trendkite data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Trendkite has collected from you. We use the data that we provide to Trendkite to better customize your digital advertising experience and present you with more relevant ads. Trendkite Privacy Policy
Hotjar
We use Hotjar to deploy digital advertising on sites supported by Hotjar. Ads are based on both Hotjar data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Hotjar has collected from you. We use the data that we provide to Hotjar to better customize your digital advertising experience and present you with more relevant ads. Hotjar Privacy Policy
6 Sense
We use 6 Sense to deploy digital advertising on sites supported by 6 Sense. Ads are based on both 6 Sense data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that 6 Sense has collected from you. We use the data that we provide to 6 Sense to better customize your digital advertising experience and present you with more relevant ads. 6 Sense Privacy Policy
Terminus
We use Terminus to deploy digital advertising on sites supported by Terminus. Ads are based on both Terminus data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that Terminus has collected from you. We use the data that we provide to Terminus to better customize your digital advertising experience and present you with more relevant ads. Terminus Privacy Policy
StackAdapt
We use StackAdapt to deploy digital advertising on sites supported by StackAdapt. Ads are based on both StackAdapt data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that StackAdapt has collected from you. We use the data that we provide to StackAdapt to better customize your digital advertising experience and present you with more relevant ads. StackAdapt Privacy Policy
The Trade Desk
We use The Trade Desk to deploy digital advertising on sites supported by The Trade Desk. Ads are based on both The Trade Desk data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that The Trade Desk has collected from you. We use the data that we provide to The Trade Desk to better customize your digital advertising experience and present you with more relevant ads. The Trade Desk Privacy Policy
RollWorks
We use RollWorks to deploy digital advertising on sites supported by RollWorks. Ads are based on both RollWorks data and behavioral data that we collect while you’re on our sites. The data we collect may include pages you’ve visited, trials you’ve initiated, videos you’ve played, purchases you’ve made, and your IP address or device ID. This information may be combined with data that RollWorks has collected from you. We use the data that we provide to RollWorks to better customize your digital advertising experience and present you with more relevant ads. RollWorks Privacy Policy

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