Why the future of recovery means less human intervention, not more
Aivar Elbrecht is engineering our company toward a future where human intervention becomes minimal, and he’s completely fine with that.
“No matter what I was doing and where I was working, I’d be committed to this vision… of creating a better, more sustainable future.”
He’s talking about a world where devices self-correct before damage even happens. Imagine a future where circuits reroute, batteries recalibrate, and screens repair themselves all autonomously, without the need for external repairs or replacements. It may sound futuristic, but we’re already starting to see the pieces come together, and we’ll dive into how this vision could become reality.
But that future isn’t here yet. The world churns out 62 million metric tons of e-waste every year, and recovers barely 22% of it. In 2022 alone, $91 billion in materials–copper, gold, iron–never made it back into circulation (United Nations, 2022).
That’s a missed opportunity for components like microchips and sensors. Traditional testing methods that only measure pass or fail overlook these valuable parts. On the other hand, adaptive testing and self-healing technologies could catch these subtleties, making sure nothing valuable gets lost.
Building a future of self-correcting infrastructure means we need to train systems to detect small shifts in behavior. And that’s where precision recovery begins.
Orange is the new grey
For decades, electronics testing followed the same script: run the checklist, log results, mark it as pass or fail. There wasn’t much room for nuance. The entire recovery industry has been built on this binary foundation, leaving staggering value on the table.
That rigidity was the first thing we challenged. At our facility in Tallinn, our team was running diagnostics on components flagged for failure. Instead of using a fixed sequence, we handed control to our adaptive AI testing engine. It adjusted in real time, skipping unnecessary steps, slowing down when needed, and even repeating actions based on how each component responded.
It was thinking on its feet. Certain components seemed to give it pause. These parts weren’t passing. But they weren’t outright failing. Their signals were nuanced, shifting with temperature, timing, or load. Still, the system didn’t reject them. It just kept testing, looking at a granular level for what the issue really was.
Our team called this “the orange area”: a category for device behavior that didn’t cleanly pass or fail, but couldn’t be ignored.
“When you let AI dynamically adjust the test flow, you start seeing patterns which you didn’t see before,” Aivar says. “Quite often, a product is a bit of a living creature.”
Each time the AI runs a test, it captures detailed data: things like voltage drift, thermal lag, or how quickly a part recovers. This learning stacks. Over time, the AI builds a sharper profile of how components behave, and how to respond when those patterns resurface.
“It’s not about one big breakthrough,” Aivar explains. “It’s about thinking at the system level.” Coordinated tools. Adaptive testing. Feedback loops that sharpen with every cycle. It’s changing how testing works.
And now, some of the most precise diagnostics happen without physically touching the component at all. No probes, cables, or power supplies needed. This is where simulation and digital twins come in, pushing the boundaries of what we can test and how we test it.
Testing without touching
Simulation is pushing the boundaries of what testing can do. We can now run full diagnostics without powering on the device, replicating how it behaves under heat, load, and signal conditions, all virtually.
Using NVIDIA’s Omniverse platform, our team creates digital twins of devices and test environments. These models help validate designs, surface flaws early, and refine testing setups, without touching real hardware.
In one scenario, we tested hundreds of smart glasses configurations in simulation, adjusting optical settings, checking for scratches, and tweaking polarization levels. “With traditional methods, that could take months,” Aivar says. “In simulation, it happens in seconds.”
Each twin is trained on real-world data until it mirrors physical behaviour: how light moves across surfaces, how heat builds up inside the housing, and how materials react under pressure.
And the best part? Each simulation generates data that sharpens the AI models. Those models improve the next set of simulations, which return more precise test parameters. The loop builds on itself, without the need for human adjustment.
Simulation removes the need for physical handling in many cases, opening testing paths that would be too risky, expensive, or time-consuming in the lab. It can safely model scenarios that would damage real equipment, repeat rare edge cases without limit, and compress years of wear into minutes.
That level of foresight starts to change the whole idea of recovery. If the system knows how something will behave, it doesn’t have to wait for it to break. The line between testing and recovery starts to blur. At some point, prediction starts replacing response.
And this shift is already making a real impact. Take one of our clients, for instance. By tweaking a single aspect of our process, we slashed both costs and waste in a way that was almost too simple to believe.
When recovery moves upstream
In Southeast Asia, we were faced with a scenario that forced us to challenge the norm. A set of radio tower boards came in for what seemed like routine repairs. But something felt off. As we examined the components, we saw the subtle signs of trouble. A few electrolytic capacitors were already showing early signs of failure.
At this point, most teams would have stuck to the script and waited for the failure to fully unfold, then react. But by the time that happens, the damage is done. Delays. Additional costs. A prolonged supply chain.
So, we asked ourselves: what if we didn’t wait for failure? What if we took action before it had a chance to snowball? Instead of waiting for the inevitable, we proposed replacing the capacitors while they were still operational.
“Most of these boards came from remote towers in places like Malaysia or Myanmar,” Aivar explains. “If we didn’t catch the failure early, the board would be repaired, shipped back, reinstalled, then fail again in a few months. Someone would have to get back through the jungle, up into the mountains, climb back up that tower, remove it, and send it all the way back to Europe.”
(That’s one way to keep the supply chain in shape.)
Replacing a $2 capacitor before failure saved tens of thousands in downstream costs. Which is nice. But the environmental impact mattered just as much. Every early intervention kept hardware in the field longer.
What began as a technician’s observation now runs as a coordinated system. AI flags components likely to fail. Simulation validates the risk. And predictive maintenance moves recovery upstream, before damage ever takes hold.
It’s one of the clearest signs that recovery is starting to operate ahead of failure. A $2 capacitor might not sound revolutionary. But catching it early is the same logic that powers the next leap. “System-level thinking,” Aivar explains, is about aligning each part of the process so the whole keeps improving.
And the next step is already in motion: systems that detect failure and fix it themselves. No alerts. No technician. Just recovery, happening in the background.
Devices that heal themselves
Imagine your phone screen cracks and instead of panicking, you just… wait. Within hours, invisible molecular mechanisms have restructured the damage. By morning, the crack’s gone. (The kind of thing you’d expect in an Iron Man suit, not a consumer device).
We call it software-defined hardware repair: devices diagnose their own health and recover autonomously. When physical components fail, the system reroutes functionality through healthy hardware without external help. That future isn’t here… yet. But we’re exploring what it’ll take to get there.
Some of this already exists in our labs. Liquid metal microcapsules can restore conductivity in damaged circuits. Silicon anodes extend battery life tenfold. One e-skin prototype regained 80% of its sensor function within seconds of damage (Science, 2025).
The global market for self-healing materials is projected to grow from $2.1B to $14.7B by 2033 (Emergen, 2025). That investment is tracking a shift from passive materials to systems that recover themselves.
At Reconext, we’re leaning into that shift. The AI we use to identify recoverable components is being refined to better understand and assist recovery mechanisms. Our simulation platforms are advancing to model not just device behavior but also how internal repair processes might evolve in real-world scenarios.
While we’re not yet triggering recovery from within, we’re laying the groundwork by pairing our AI with advanced systems to create more seamless, intelligent recovery workflows. There’s a long way to go. Performance varies under extreme conditions, and manufacturing isn’t there yet. But we’re in it, researching, prototyping, iterating.
And at the center of it all is a self-learning recovery platform powered by AI and robotics. A closed-loop system that can see, decide, and act with minimal human input. One that keeps value in circulation longer, with less waste and less labor.
That’s the vision we’re building toward. And when we get there, there’s simply less left for us to do.
Working towards your own obsolescence
We’re developing testing platforms that adapt in real time.
We’re training models that recognize patterns faster with every run.
We’re building infrastructure that allows recovery to continue with less human input.
As we advance, some of the services we offer today won’t be needed tomorrow. And that’s the goal: to reshape the future of recovery, even if it means we’ll be doing things differently.
Precision Recovery is the framework guiding that transition. It was shaped in the field, under the pressure of limits old systems couldn’t overcome. (Learn more about it here.)
Together, these capabilities increase yield, reduce manual effort, and help extend the useful life of every component. We’re solving today’s problems while building the infrastructure for tomorrow’s recovery.
And if that tomorrow affects our business? Aivar doesn’t flinch. “Better to drive these technologies than to follow from the side.”
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You’ve made it this far.
By now, the direction should feel clear. Systems are getting smarter. Intervention is shifting upstream.
We’re helping teams design recovery that works earlier, learns faster, and keeps more value in circulation. Sometimes that begins with a single component. Sometimes it starts with the system itself. If your current tools are missing something, or you’re building toward something better, we’re ready when you are.
Contact our team
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