Ivan Tertychnyy is the Lead Product Manager at TraceAir, a fast-growing construction tech company building a site intelligence platform that blends software with a hands-on service layer. With over a decade of experience transforming chaotic startup operations into scalable, data-driven systems, he was brought in to streamline production, align cross-functional teams, and unlock SaaS-level efficiency. The conversation highlights the Product Manager’s leadership in process optimization, people management, and cost efficiency
When you stepped into the role, what was the first operational challenge that demanded your attention?
When I joined, TraceAir was growing so fast but the processes were not scalable enough. Critical project data was fragmented across chats, emails, and spreadsheets—turning even basic status checks into detective work. Core teams like GIS, OPS, and Customer Success were manually stitching together workflows that had never been designed for scale. Onboarding timelines lagged, costing us velocity and revenue. And for a company on a mission to become the global leader in site intelligence for construction, that was unacceptable. Operational excellence wasn’t just an internal fix — it became a strategic lever to enable global scale.
Can you share an example of a process improvement that noticeably elevated team productivity and client experience?
Redesigning GIS operations was the inflection point. This team processes complex drone scans and terrain models — a workflow that was manually stitched together. We built a tailored UI, instrumented every action, and automated where possible. The result: GIS throughput per person tripled. That kind of uplift is rare, especially without adding headcount. On the client side, we introduced a self-service portal offering full transparency, and reworked onboarding into a highly automated experience. Today, over 80% of customer onboarding is fully automated—a number that’s nearly unheard of in construction, one of the most conservative industries with some of the lowest technology adoption rates.
How do you ensure they feed long‑term scalability?
We instilled a product mindset in operations. Instead of long roadmaps, we now deliver lean proofs-of-concept within weeks. We’ve built a repeatable framework: small vertical slices, fast feedback, and tight integration with the core architecture. That agility means we can move fast even in conservative industries like construction.
But it’s more than just speed — it’s strategic. TraceAir is building the site intelligence platform for homebuilders: an integrated platform designed to digitize and optimize every layer of construction project delivery. There’s nothing like it on the market, and delivering on this vision requires more than execution — it demands constant hypothesis testing, experimentation, and cross-functional collaboration. It’s how we stay ahead as we scale — not just improving existing workflows, but inventing new categories of tools for the industry.
As the construction-tech market evolves, what keeps TraceAir adaptable and competitive from an operational standpoint?
We’re now the market leader in the homebuilding segment, but we’ve built much more than a single-product company. Our goal is to become the site intelligence platform and operating system for homebuilders —a digital backbone with no true equivalent in the industry. We know the construction market moves in long, volatile cycles, so we’ve built adaptability into our core. Our product strategy is deeply customer-centric – we listen closely to the challenges our clients face on the ground, and respond fast with tools that solve real pain points. That’s how we’ve expanded beyond homebuilding into adjacent workflows, creating a diversified platform that’s resilient by design. We’re not just reacting to the market – we’re co-evolving with our customers.
At the same time, we’re investing heavily into AI. We’re already releasing AI-powered tools that would have been impossible just a few years ago like Layout Generator. These aren’t experiments; they’re live products.
Your service org operates more like a SaaS engine than traditional ops. What drove that transformation?
We refused to treat service as a cost center. Every recurring task was documented, standardized, and where possible – automated. We applied the same discipline we bring to product development: instrumentation, scale, and continuous iteration. That mindset lets us exceed traditional efficiency benchmarks in an industry where high margins are rare. But more importantly, it freed up our teams to do deeper, more impactful work. We’re building a culture where people grow alongside the systems they help improve – and we’re actively hiring product-minded talent to take that even further.
Big process changes can stall if culture doesn’t keep up. How did you drive adoption?
We injected product‑marketing DNA inside engineering: demos, release notes, internal road shows. Enablement tools translate features into workflows, and shy developers now present at open demos. Transparency is addictive—once teams see live metrics, they volunteer insights we never expected.
How do you empower team members to evolve their roles as repetitive tasks become automated?
Automation removed the old resource bottleneck. We focused on eliminating tasks people shouldn’t be doing in the first place. Experts got time back to solve harder problems, ENPS climbed, and the company kept growing—without burning out talent. Today, the same headcount tackles roadmap items we would’ve postponed a year ago. That’s why we’re building a team of operators and product thinkers ready to redefine what’s possible in construction tech
How do you envision leveraging AI to push operational efficiency and user experience even further in the coming years?
AI accelerates development, letting us ship personalised, domain‑specific tools that once took quarters to build. It means faster, risk‑free experiments—code generation, test scaffolding, even UI prototypes. Second, AI will erase leftover manual tasks: email triage, support ticket classification, and proactive notifications. Layer by layer, the stack becomes smarter and more autonomous, delivering a smoother experience for builders and a lighter workload for our teams.