Better, Faster, Cheaper: How Green Standards Built Its Own Tech Stack

Satenstein is not a professional software developer, but he has scoped builds and managed development contracts and served as a system administrator, giving him a grounding in data modeling and a range of use cases. His early exposure to machine learning and AI coding agents helped him identify the optimal development stack and begin assembling a custom system while the ERP rollout stalled. 

“What makes Rick so special is not just what he can do with Claude,” Langdon said. “It's that he can do that while also being armed with eight and a half years of deep, deep, intimate experience in our business.” 

The new stack runs on a shared backend, and includes a CRM (Horizon), a proposal-generation platform (Forge), an inventory and ESG-reporting tool, and several smaller internal tools built to fix everyday friction points. 

The benefits for staff across the organization have been significant. Teams tracking the same inventory once relayed information to each other by email, Satenstein said. That data now moves through a shared backend automatically, freeing staff from the manual handoffs between teams. The new tools also scan emails and document stores, ingesting and processing a wide range of data and delivering it to internal or external stakeholders in whatever format suits them best. 

“We've decided that AI is important not just to embed, but to use in the building of much more customized tools for everyone across the company to use,” Satenstein said. “Tools that are better than all the out-of-the-box software companies typically use, without all the resourcing that you need for customization and development in the old world.” 

The shared backend also speeds up reporting clients rely on, like the tons of furniture diverted or kilograms of carbon avoided that clients track through their ESG programs, now delivered through a self-serve portal instead of one-off reports.  It also draws on best-in-class middleware, with endpoints that bring together functions once spread across several platforms, so users now work from a single CRM. 

They even built an app for employees to request specific snacks from their Costco order. 

"None of these six things existed in any way, shape or form more than say four months ago," Langdon said. "And now they're all launched." 

Neither Langdon nor Satenstein credits this shift entirely to internal ambition. Both pointed to their participation in the World Trade Centre Toronto's Growth Development Program (GDP) as a turning point. Not just for expertise on AI, but for the confidence and outside perspective to pursue it seriously. 

"We were kind of going down a road [on AI] on our own before we joined the program," Langdon said. "And when you're doing that on your own, you don't know if you're doing the right things or not. You don't know if you're going down rabbit holes that are going to prove fruitless." 

One moment stuck with him as proof they were on the right track. While describing a tool the company had built for onboarding and training, a GDP mentor stopped him mid-sentence. 

"Three years ago, I would have told you guys that you had just built a standalone product that you should take to market and turn into a spin-off company," the advisor told him. "And now, with the ability to build software and tools so quickly using AI, you wouldn't say that anymore." 

That reaction became a kind of validation Langdon couldn't have gotten internally. 

"This is a professional-grade tool that solves a business challenge," he said. "Hearing that from somebody who had that perspective was super valuable." 

Satenstein also pointed to the peer network GDP provided as value from the program. 

"One of the companies in the peer group had done a lot to restructure their proposal process, to use AI in essentially modifying the proposal production process," he said. "That was something that we grabbed onto and have subsequently started building our own proposal generation platform. And it's worked, and it's working very well." 

Ask Satenstein for a hard ROI number and he'll tell you the question is framed wrong. 

"Traditionally people look at [Profit and Loss] and they say, these are my employee costs, and so if I do the same work with fewer employees, I'd therefore save money," he said. "I don't think that's the right lens for this." 

Instead, he measures return in time reclaimed and in the quality of the work left behind once the administrative burden is gone. 

"You're not just freeing up people's bandwidth. You take away the worst part of their work," he said. 

There's a competitive dimension too, one that doesn't show up directly on a balance sheet. 

"Every other company that we are competing with are also doing a lot of the same things," Satenstein said. "If we don't do it, we'll become less competitive against them." 

Langdon echoed the sentiment, adding that not implementing AI could show up as a quiet decline in revenue as competitors move faster. 

For leaders weighing whether a program like GDP is worth the time, Langdon's answer is unambiguous. 

"The ROI is undeniable," he said. "It's existential, and it's not optional." 

Satenstein summarizes the benefits in three words. 

"Better, faster, cheaper."