About
Forty years of AEC practice. One very specific problem to solve.
Zingerworks was founded by Carl Springer to share what he learned doing AI integration from the inside, and to help other AEC firms do it better.
Why zingerworks exists
Integrating AI into AEC is unlike anything we have done before
I spent over 40 years in AEC practice. I have worked through a lot of changes in how this industry operates: new technologies, new delivery methods, new regulatory environments. None of them quite prepared me for what AI is asking of firms right now.
This is not a software upgrade or a new project management methodology. Integrating AI into AEC workflows is a large-scale organizational change, more than most firms can readily get their hands around on their own.
I know that from direct experience. At my previous firm I worked through AI integration from the inside: the excitement, the friction, the false starts, and the things that eventually worked. That experience taught me more about what actually drives adoption than any consultant's framework could.
I founded Zingerworks to share that knowledge with other AEC firms. Not as a vendor, not as a technologist, but as someone who has lived the organizational complexity and come out the other side with a clear picture of what it takes to do this well.
How I work
A few principles that shape every engagement
Process before technology AI tools are only as useful as the workflows they fit into. Every engagement starts with how your firm actually delivers work, not with what a tool claims to do.
Honest over optimistic Not every AI opportunity is worth pursuing. Part of what I do is help firms avoid investing in things that will not move their practice forward. That sometimes means saying so directly.
Built to work without me The goal of every engagement is to leave your firm with something it can use independently. I am not interested in building dependency. I am interested in building capability.
Background
Where the expertise comes from
My professional roots are in multimodal transportation systems planning, a discipline that requires integrating complex technical analysis with organizational decision-making across multiple stakeholders and agencies. That background shapes how I approach AI strategy: systems-first, with a clear eye on where decisions actually get made and where things tend to break down.
As AI Director at a mid-sized AEC firm, I worked across both sides of that equation. On the technical side, that meant evaluating and deploying AI tools including large language models, prompt engineering, custom AI screening tools, and workflow automation against real project delivery demands. On the organizational side, it meant redesigning how teams worked, how responsibilities shifted, and how leadership and staff developed the shared understanding needed to make adoption stick.
That combination is what Zingerworks is built on. Not just knowing which tools work, but knowing what it takes organizationally to make them work inside an AEC firm.
What I believe about this work
AI in AEC is an organizational challenge
as much as a technical one.
01
The tool is never the hard part
Every AI adoption challenge I have seen comes down to people, process, and organizational readiness. Not the technology itself. Getting those right is the work.
02
Shared understanding comes first
When principals and staff do not have a common picture of what AI means for the firm, every subsequent decision is harder and more contested than it needs to be.
03
Speed is not the goal
The firms that move fastest on AI are not necessarily the ones that do it well. Firms that take the time to build the right foundation tend to get further, faster, in the long run.
Ready to talk about where your firm is?
A short conversation is usually all it takes to figure out where you are and what would be most useful. No pitch, no pressure.