From 500 Conversations to Product-Market Fit with Asad Tirmizi
Many startups chase product-market fit as if it’s a moment of discovery. But for deep-tech founders, the journey is often much longer.
Product-market fit doesn’t appear overnight. It’s built through years of research, experimentation, and conversations with customers.
That’s exactly what happened with Trener.
Before founding the company, Asad Tirmizi spent 14 years researching robotics systems, developing the technical foundations that would eventually become the company’s product. What started as academic research later evolved into a commercial solution for manufacturers.
From Research to Startup
Trener didn’t begin as a startup idea. It began as research.
During his PhD, Asad focused on one of the hardest challenges in robotics: enabling machines to sense, plan, and act in real-world environments. His work explored how robots could better understand their surroundings and make decisions autonomously.
Over time, that research expanded to include machine learning within the robotics stack, allowing robots to become more adaptive and capable.
The long-term vision was simple but ambitious:
Make robots easier to program and far more capable in real-world environments.
What started as an academic exploration gradually revealed something bigger. The technology being developed in the lab had clear commercial potential.
That realization eventually led to the creation of Trener, transforming years of robotics research into a product designed for real industrial use.
For many deep-tech founders, this path is common. The startup doesn’t begin with a sudden insight. It emerges slowly from years of technical exploration, experimentation, and iteration.
The Hardest Step: Turning Technology into a Business
At some point, the founders had to leap.
Asad left his role at ByteDance. His co-founder, Lars, stepped away from a tenure-track professorship. Both were leaving stable, prestigious careers to pursue an uncertain path.
That decision forced a much harder question than building the technology itself:
Can this become a real business?
Great technology isn’t enough. To build a company, the founders needed to prove that their robotics system solved a problem that companies would consistently pay for.
The challenge was no longer just technical. It was about turning years of research into a repeatable, scalable business.
The Validation Phase: 500 Conversations
Before launching the company, the founders focused on one thing: talking to customers.
They interviewed 60–70 companies and spoke with 400–500 people across the industry. The goal wasn’t to pitch a product. It was to understand where their technology could actually solve a meaningful problem.
Those conversations quickly revealed where robotics wouldn’t work.
At first glance, industries like grocery stores and pharmacies seemed promising. But the deeper they looked, the more obstacles they found. These markets had no established buying process for robotics, strong safety concerns, and limited automation budgets.
In other words, even if the technology worked, adoption would be difficult.
The conversations also revealed a much better opportunity.
Instead of introducing robots into new industries, the founders focused on industries that were already using them. These markets already understood the value of automation and had the budgets and processes to support it.
That insight helped narrow the search for the right market and brought them closer to finding product-market fit.
The Beachhead Strategy
Instead of pursuing multiple robotics applications at once, the founders made a deliberate decision: focus on a single use case first.
They chose machine tending in manufacturing, the process in which robots load and unload parts from machines such as CNC mills.
Several factors made this market attractive.
First, manufacturers were facing a severe shortage of skilled labor, making automation increasingly necessary. Second, the value proposition was clear: companies could see immediate, measurable ROI from automating these repetitive tasks.
Just as importantly, manufacturing already had significant adoption of robotics. Companies were familiar with automation and had established processes and budgets for it.
Finally, each deployment carried high economic value, meaning even a small number of successful implementations could generate meaningful revenue.
By focusing on a single beachhead market, Trener was able to concentrate its efforts, demonstrate the value of its technology, and lay the foundation for expansion into other robotics applications.
The First Customer (From an Interview)
Trener’s first customer didn’t come from outbound sales or a polished pitch deck.
It came from a discovery interview.
During one of their research conversations, the founders spoke with a manufacturer that had already tried to deploy a robot for machine tending—but the project had failed. The hardware was there, but the system simply couldn’t be made to work reliably.
That conversation quickly shifted from research to opportunity.
The company made a straightforward proposal: “If you can make this work with your software, we’ll sign a contract.”
Within a few months, Trener deployed its solution and successfully got the robot running.
What began as a discovery call became their first customer and eventually their first case study.
Traction and the Economics of a Winning Product
Two events helped turn Trener’s early progress into real traction.
The first was a successful customer deployment. After turning a discovery interview into their first contract, the team proved they could make their software work in a real manufacturing environment.
The second was external validation: winning the ABB Robotics AI Challenge, one of the industry’s major competitions.
Together, these milestones validated two critical points: the technology worked, and the market wanted it.
Just as importantly, the product’s economics were compelling.
Trener closely measured the value their software created for customers. Their analysis showed that for every $10,000 spent on the product, manufacturers generated roughly $138,000 in annual value.
That kind of 13× return on investment makes the decision to adopt the technology much easier, and creates strong momentum as the company scales.
The Long Game of Product-Market Fit
Even after early traction, reaching true product-market fit took time.
In the first year, the market’s signal became clear. Customer conversations and early deployments confirmed that manufacturers genuinely needed a better way to make robots work reliably.
But demand alone wasn’t enough. The technology still had to mature.
Around 18 months in, the system’s reliability improved significantly. Deployments became more stable, and customers could depend on the product in real production environments.
By year two, the next milestone appeared: scalable deployments through partners. Instead of individual installations, the company began to see the beginnings of a repeatable model.
Looking back, product-market fit didn’t arrive as a single breakthrough.
It emerged gradually, from the convergence of technology maturity, real customer demand, and the operational ability to consistently deploy the product.
Conclusion
Trener’s journey shows that product-market fit in deep tech is rarely a single breakthrough.
It’s built over time, through years of technical research, hundreds of customer conversations, and a focused entry into the right market.
- First, the technology proved it could work.
- Then, the market demand became clear.
- Finally, the deployment model became repeatable.
Product-market fit wasn’t a moment. It was the gradual alignment of technology, market demand, and execution.
NO TIME TO READ?
