Introduction

Thornton Tomasetti’s CORE studio is widely recognized as one of the most forward-looking technology groups in the AEC industry. At its center are computational designers like Eesha Jain, whose journey began at Pratt Institute and quickly led her to this influential team.

With a background that bridges architecture, coding, and electronics, she now develops tools that sit at the intersection of design and engineering — building systems that turn complex Grasshopper definitions into polished, usable applications. At CORE, her work goes beyond geometry; it’s about creating workflows and platforms that directly impact how engineers and project teams work on some of the world’s most ambitious projects.

In this interview, Eesha shares how CORE studio balances immediate project needs with long-term innovation, what it takes to successfully introduce new tools across a global engineering firm, and how platforms like ShapeDiver are reshaping their ability to deliver automation at scale. She reveals the internal challenges of adoption, the speed gains unlocked by ShapeDiver, and how Thornton Tomasetti engineers are already deploying more than 30 ShapeDiver-powered tools in their daily practice.

This conversation is especially relevant for engineering teams worldwide that rely on computational design groups to develop tools but struggle with bottlenecks: cumbersome file sharing, license management, and the need for specialized software to deploy their work. If you’re exploring ways to unlock automation across your design and engineering workflows, this interview offers a rare inside look at how one of the industry’s leading firms has done just that.

Section 1: Background

1. Hi Eesha, thank you for joining us today! Could you please share a bit of your background (education, early professional steps) and hobbies?

Hi, thank you for taking the time to interview me. I’m excited to answer these questions! My name is Eesha Jain. I was born and brought up in Mumbai, India, where I also did my schooling. In 2018, I moved to New York to study architecture at Pratt Institute.

In my second year, the pandemic paused everything, and I used the downtime to learn to code. I learned Arduino, Electronics, Circuitry, Grasshopper, and advanced tools in most of the software I used in the studio. When I got back to NYC, I landed an internship at a construction tech company called Avvir (now Hexagon), where I was introduced to BIM and reality capture. I worked with processing 360 LiDAR (Light Detection and Ranging) scans, scanning data, and checking the construction process to track for clashes, design changes, and deviations. This really exposed me to how the construction industry was leveraging cutting-edge technology to make its processes more efficient.

Soon after, in 2023, I graduated with a Bachelor of Architecture and a double minor in morphology and sustainability. For the last 1.5 years, I have had the incredible opportunity to work as a computational designer in CORE studio at Thornton Tomasetti. This role has expanded my understanding of technology’s impact on the AEC industry — from maintaining CORE’s C#-based plugins to building web applications with ShapeDiver. I work closely with engineers, modelers, and developers to create tools that empower project teams across the firm.

Outside of work, in my free time, I like to play tennis, go to concerts, and watch whale documentaries.

2. What attracted you to Computational Design as a career?

Honestly, I’m happiest when design has a rule set. In my design studios at Pratt Institute, I gravitated to a lot of grids, curvilinear form, and morphology, but my ideas outpaced what I could model until I found Grasshopper. Encoding intent, iterating fast, and seeing options instantly hooked me.

While getting a minor in Morphology, I was also able to be a Research Assistant to professors who encouraged me to work with Arduino, electronic circuitry, and Grasshopper to build out systems that I enjoyed working on. Since then, it’s grown beyond geometry into building tools and workflows using many plugins like ShapeDiver. For me, computational design is a pragmatic toolset for getting dependable outcomes—not a philosophy. It's the straightforward way to turn ideas into repeatable, reliable results.

Section 2: Team & Tools

3. What is CORE studio, and how does the CORE studio team fit within Thornton Tomasetti’s activities?

CORE studio is Thornton Tomasetti’s R&D and Technology group. We mix computational designers, software engineers, BIM specialists, and data folks to build tools and workflows that help our project teams design faster and with more certainty.

We have 7 subgroups within CORE studio:

• Modelling:  Provides project support by embedding with design teams to script automations, build parametric/optimization models, and streamline analysis.

• Application Development: Creates firmwide products/platforms like shared plugins, connectors, and web apps used across the firm to help streamline repetitive tasks and automate anything that may be overly time-consuming.

• AI: Leverages cutting-edge technology and machine learning to predict specific engineering solutions.

• Design: BIM Strategy, best practices, and automations 

• Delivery: Fabrication modeling automations and fabrication-ready, model-based delivery.

• Knowledge and Data: Enhance knowledge sharing by using the community. Establish data governance and best practices that empower staff with self-service access to data.

• AECtech Conference: This is a community-driven conference where computational designers and AEC innovators share ideas, learn new methods, and build tools—through talks, workshops, and a hands-on hackathon.

I work in the application development team and help create UI’s for web-based tools, maintaining Grasshopper plugins and other computational workflows.

Because CORE studio is a central resource within Thornton Tomasetti, we’re brought into projects across disciplines — from structures and façades to sustainability and forensics — wherever there’s a need for complex geometry, high-stakes coordination, or workflow automation.

4. What drives innovation at CORE studio? What is the balance between the concrete needs of current projects versus software projects attempting to improve or disrupt existing workflows in the long term?

Innovation in our work comes from two primary sources:

1. Immediate project needs: Addressing high-value challenges that directly impact ongoing projects. This might mean building a custom Grasshopper definition to automate repetitive geometry tasks, creating plugins for structural design software to accelerate project work, or developing a computational workflow to automate something time-consuming.

2. Long-term vision: Prototyping tools and workflows that will serve the firm, clients, and the greater AEC Tech community well into the future. This includes developing reusable Grasshopper plugins, shaping project visualization and analysis tools like Ellipse, and structural sketching tools like ForceSketch. 

We balance these by directly embedding ourselves with project teams, understanding their unique constraints, and identifying opportunities for technology to unlock efficiency or insight. From there, we design solutions that meet the immediate brief while also considering reusability, scalability, and integration into CORE’s larger ecosystem of tools—ensuring each innovation has both an immediate and lasting impact.

5. What is the balance between developing tools for internal purposes versus the delivery of tools to clients and external stakeholders?

We balance internal tools that improve our own workflows with external tools that serve the wider AEC community.

Internal tools help our teams work faster and more accurately. Many of these evolve into external tools that share those benefits more broadly, such as TT Toolbox (a collection of Rhino/Grasshopper utilities), Ellipse (a web-based structural design platform), and ForceSketch (an interactive force diagramming tool).

By publicly releasing selected tools, we contribute to the community, foster collaboration, and help advance industry-wide capabilities beyond our own projects.

6. In practice, how does it translate into the delivery schedule of new tools? What is a typical development time frame for the tools developed at CORE studio?

Timelines vary widely depending on the scale, complexity, and integration requirements of the tool.

Smaller automations: Often built directly in Grasshopper or as lightweight scripts — can be prototyped, tested, and deployed in just a few days to a couple of weeks. These are typically focused, task-specific solutions such as automating a modeling workflow, extracting project data for reports, or generating quick visualizations. Their speed comes from using existing project geometry, minimal UI, and direct deployment to the project team without large-scale dependencies.

Larger platforms: Especially those that require input from multiple teams, dedicated UI/UX design, integration with external systems, and rigorous QA — may take 3–9 months or more to reach a stable release.

The introduction of ShapeDiver has significantly accelerated our production timeline for Grasshopper-based tools. By allowing us to package complex Grasshopper definitions into accessible web applications, we can bypass the need for local Rhino/Grasshopper installations or a timeline for complex web development. We can streamline deployment and shorten the time from prototype to usable tool.

7. Can you tell us about the challenges associated with tool adoption, particularly within the Thornton Tomasetti offices? What drives the adoption of new tools, both at the individual employee level and company-wide?

In a large, distributed company like Thornton Tomasetti, tool adoption is not automatic — it depends on visibility, trust, and ease of use. Even the most technically advanced solution can struggle to gain traction if it feels disconnected from day-to-day work or requires too steep a learning curve.

Some common challenges we face include:

• Competing priorities: Project teams often work under tight deadlines, making it difficult to pause and learn something new, even if it will save time later.

• Workflow disruption concerns: Users may hesitate to change familiar processes, fearing errors, rework, or client delays.

• Limited awareness: In a company with multiple offices and disciplines, useful tools can easily go unnoticed outside their original project group.

We address these challenges by:

• Designing tools that integrate seamlessly into existing workflows: Whether it’s a Grasshopper automation, a ShapeDiver web tool, or a Rhino.Compute service, the goal is to fit into the tools and processes people already use, rather than requiring a complete overhaul.

• Demonstrating value early: Showcasing measurable time savings, improved accuracy, or new capabilities within days or weeks of release builds confidence and momentum.

• Providing support and visibility: Clear documentation, short training videos, internal presentations, and targeted marketing campaigns help ensure that tools are both discoverable and approachable.

• Embedding with project teams: By sitting alongside engineers, architects, and designers during active projects, we can refine the tool in real time based on feedback, accelerating trust and adoption.

When users can see immediate benefits, understand how the tool improves their own workflow, and feel confident that support is available, adoption spreads organically. Over time, tools that started as niche project solutions often become firm-wide standards.

Section 3: ShapeDiver

8. How does ShapeDiver fit in the context of CORE’s activities?

ShapeDiver is part of our delivery layer — the bridge that turns dense, highly complex Grasshopper scripts into clean, customized web tools. With it, our project teams, partners, and clients can explore design options right in their browser, no Rhino or Grasshopper required.

This platform powers our AI micro-apps, trained on Thornton Tomasetti’s deep knowledge base — decades of project experience, proven calculations, and design techniques from our structural engineering experts. The AI team builds the models, the modeling team shapes the geometry, and the app dev team designs the UI — all within Grasshopper. Because these scripts pass through many hands and evolve daily, their native output can be messy. ShapeDiver lets us wrap that complexity in a tailored interface, delivering results as clear tables, charts, and diagrams. We can even push silent updates, fix bugs, or add features without disrupting the user experience.

These applications let users quickly calibrate trusses, shear walls, column stacks, and more — simply by adjusting parameters and instantly seeing updated outputs. Used primarily in the early design stages, they provide rapid quantity takeoffs and clear visualizations that help project teams make informed decisions and give clients the confidence to move forward. What once required manual recalculations and hours of modeling can now be done in seconds, streamlining design iteration and strengthening our ability to validate/test several designs early on.

It’s also a great testament to the ShapeDiver team for creating such an adaptable and transformable plugin. We can take advantage of their robust building blocks while still developing our own plugins and following our own dev schedule — tailoring features specifically for the needs of a structural engineering firm without being locked into a single workflow.

Our ecosystem is strengthened by tools developed both in-house and by colleagues from other practices and the overall Grasshopper community. These integrate seamlessly into ShapeDiver, giving us autonomy to create features specific to TT. We can also connect to the Grasshopper plugin ecosystem, including our own custom plugins. Our applications can communicate with external APIs, export PDFs, Excel files, create custom bitmaps, and generate custom charts — elevating our product and UI experience. CORE studio has also built backend scripts that export SAP and ETABS models so that structural engineers can validate ShapeDiver results against trusted analysis tools.

In short: ShapeDiver transforms our internal AI innovation into accessible, polished, and continuously improving tools.

9. For you personally, how has the adoption of ShapeDiver changed the way you work?

ShapeDiver has fundamentally changed the way I approach Grasshopper scripts. What was once “just a definition” is now a full-fledged product — complete with its own UI, audience, branding, and lifecycle. I now think beyond mere functionality and also consider how someone will interact with the tool, their first impression, and how we’ll maintain and develop it over time.

This shift has pulled me deeper into the realm of UI/UX design, where I’ve learned to think critically about hierarchy, button placement, visual clarity, and the pacing of user interactions. I now approach each build with the same care a product designer would — defining the audience, tailoring the interface to their needs, and balancing aesthetics with speed and precision.

It’s also made me more conscious of the economics and audience demographics of every tool we release. Who will use it? How much time will it save them? What value does it add to a project or client relationship? ShapeDiver has turned every Grasshopper project into an opportunity to create something polished, purposeful, and sustainable — not just for today’s task, but for the long run.

10. Has ShapeDiver helped computational designers release production tools with minimal or no involvement from software developers in CORE studio?

ShapeDiver drastically lowers the barrier to publishing tools.

Before, turning a Grasshopper definition into something shareable often required front-end development skills, hosting infrastructure, and custom integration — a process that could take weeks or months. With ShapeDiver, we’ve been able to bypass most of that complexity.

For example, a glass designer tool idea came from a team in the facades practice, and we were able to help them release a fully customized AI tool with several layers of automation and complexity using ShapeDiver’s App Builder plugin components.. With the AI tool integrated into the Grasshopper geometry generation script, we could instantly publish it as a ShapeDiver application. For further customization, we used our App Builder templates, and it only took a few days to create the entire app. From there, it was a process of understanding what kind of post-processing outputs users would want and adding more features before we launched it on our intranet. Even with only basic front-end experience, users have been able to release multiple complex UI applications in a fraction of the time it used to take.

The accessibility extends beyond our development team. Structural engineers with no prior ShapeDiver plugin experience have successfully published simple tools directly from Grasshopper, instantly making them usable across the firm. This ease of deployment means ideas can be tested, shared, and iterated on quickly, without waiting for a dedicated dev cycle. The result is a more collaborative and experimental culture, where anyone with a good idea and a Grasshopper script can get their work into the hands of users.

11. How many tools built on ShapeDiver are currently actively used at Thornton Tomasetti?

We have over 30 AI/ML tools published to our ShapeDiver organization page, along with about a half dozen more from other TT practices outside CORE studio. These tools range from early-stage prototypes to fully mature applications, and we work continuously to refine, update, and scale them based on feedback and evolving project needs.

12. How many users in Thornton Tomasetti benefit from those tools?

We embed our ShapeDiver applications directly into the company intranet using iframes, making them instantly accessible to all Thornton Tomasetti employees. This means any engineer — regardless of software expertise — can use these tools in the early stages of structural design.

If a client simply wants to know which material performs best, how much load a wall can bear, or needs preliminary quantity estimates, our engineers no longer have to spend days or weeks running manual calculations. Instead, they can open a ShapeDiver-powered tool, input a few parameters, and have clear, reliable answers in minutes. This speed not only improves efficiency but also allows us to engage clients earlier in the decision-making process with data-backed insights.

13. Can you tell us more about one of the ShapeDiver Apps your team has worked on and successfully deployed? How does it serve the execution of projects and other activities at Thornton Tomasetti? Do you have an estimation of the time saved, either for specific tasks or globally in the context of a specific project phase?

Asterisk is the second generation of CORE studio–developed structural design and optimization tools that use custom ML and generative AI models to help our engineers explore design options quickly and efficiently. The first-generation platform was developed in 2017 as a stand-alone tall-building ML application.

Since then, the CORE.AI team has developed predictive ML models for every major structural component in steel, concrete and mass-timber buildings, allowing users to apply specific design parameters to generate a wide variety of structural concepts with rapid member sizing, quantity takeoffs and embodied-carbon computations.

These options can be iterated and compared in real time, enabling faster decision-making during the early design stages. The CORE.AI team has leveraged generative AI to predict many design options for various structural components.

One of the ways the full Asterisk application is delivered to Thornton Tomasetti is through ShapeDiver, which serves as the platform for deploying our tools as interactive, browser-based apps. ShapeDiver enables fast cloud-based computation for multiple building elements, along with custom charts and a Grasshopper UI that makes complex output easy to interpret.

Users can export the entire processed building model—including attributes—directly as breps, meshes, or centerlines, ready for analysis in other CORE studio-powered software such as Konstru or other structural analysis software. Each ML model within Asterisk—such as truss designers, shear walls, concrete slabs, and other structural systems is also hosted as its own micro-app on ShapeDiver. These micro-apps are embedded directly into TT’s internal platforms via iFrames, making them accessible firmwide without requiring users to open Rhino or Grasshopper. This integration streamlines workflows, reduces computation time from days to hours, and allows engineers to test more design options in less time.

Asterisk is designed for tasks like structural optioneering, which involves adjusting and testing structural parameters to optimize designs. Its AI enables it to automate much of this process, significantly reducing time spent on tasks that traditionally required manual calculation and coordination meetings, and providing fast, smart design options for our clients.

14. What are your goals for the next Apps you are developing?

We’ve developed a suite of AI App Builder applications for use across Thornton Tomasetti. Each has a custom UI that allows users to input project-specific information and instantly receive outputs such as quantity takeoffs and load capacity calculations. These micro-applications can be deployed firm-wide to streamline the design of structural elements like columns, beams, slabs, and more — enabling consistent, fast, and accurate early-stage design decisions.

Looking ahead, a powerful next step would be the ability to use ShapeDiver’s compute components directly inside Grasshopper. This would let us call ShapeDiver applications from the web within a Grasshopper script, allowing modelers to incorporate real-world engineering data into their geometry from the very start. Imagine designing a structural model where each beam and column is automatically sized, verified, and optimized using ShapeDiver’s data-driven systems — all without leaving the Grasshopper environment. This integration would bridge conceptual modeling and engineering precision, resulting in faster workflows and more reliable design outputs.

Section 4: Future of AEC & Computational Design

15. As a member of CORE studio, you have the chance to both learn and experiment with new technologies, while also driving change yourself within the AEC industry. Which trends are you currently witnessing, and which ones do you think are relevant?

Right now, the biggest trends we’re seeing are the integration of AI/ML into design workflows, cloud-based computation for accessibility, and the push toward real-time collaboration tools. Another important trend is the convergence of design and analysis — architects, engineers, and fabricators are increasingly working from shared, live data environments, which shortens feedback loops and reduces errors.

16. Where do you see the role of computational design going in the future, and how does CORE studio position itself within the upcoming challenges facing the industry?

Computational design is moving from being a specialist skill to a core part of everyday practice. In the future, I see it becoming more about building interconnected ecosystems rather than one-off scripts — systems where geometry, data, and analysis flow seamlessly between disciplines, platforms, and even companies. CORE studio is positioned at the center of this shift. By embedding with project teams, developing reusable toolkits, and leveraging platforms like ShapeDiver, we’re able to test, deploy, and scale solutions quickly across the firm. This adaptability is key as the industry faces challenges like compressed project timelines, increasing sustainability requirements, and the demand for greater transparency in design decisions.

17. Do you see AI as an alternative to computationally-driven algorithms, or simply as a new element in your toolbox?

For me, AI isn’t a replacement for computational design — it’s a powerful new layer on top of existing tools. Computational algorithms excel at solving well-defined problems with precise rules, while AI thrives in identifying patterns, making predictions, and generating options from complex, less structured data. In our work, the most exciting outcomes come from combining the two — using AI to inform or guide computational workflows, and computational logic to validate and refine AI-generated results. This hybrid approach not only speeds up iteration but also makes outputs more accurate, explainable, and directly applicable to real-world projects.

- Thank you very much for your time, Eesha!

Thank you as well for this opportunity to share our work at CORE studio!

That's it for this new edition of Getting to know... Don't forget to visit CORE studio's website and LinkedIn account!

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