Most companies will tell you they’re investing in AI. Not many can show you where, how, and with what impact.
At Reconext, we’ve deployed proprietary AI agents into active repair workflows across two live facilities, and the results are undeniable:
- ~20,000 automated corrections executed
- 79.5% success rate on configuration mismatch resolution
- 44% productivity gain in one of our core repair operations
These numbers reflect how we run our business today, and increasingly, how we run yours.
Reverse logistics is a volume business, which means small inefficiencies carry outsized consequences. A few percentage points of yield loss, a configuration mismatch that recurs across a device fleet, a repair workflow that requires one more technician than it should: none of these feel like emergencies until you calculate what they cost across a full quarter. What we have built is a capability that addresses those problems before they compound, embedded directly into the workflow rather than layered on top of it.
At the center of our current deployment is Quincy, our AI-driven repair intelligence agent, now live across two major facilities with a distinct mission and measurable outcomes at each.
Solving yield loss at the source
At our Memphis authorized repair base facility, Quincy was introduced to address configuration mismatches that were creating a constant, invisible drag on pass yields. Rather than flagging issues for human review, Quincy identifies and corrects data problems directly within the customer’s systems. Since launch, it has executed nearly 20,000 corrections at a 79.5% success rate. Those corrections flow upstream as master data fixes, reducing the likelihood of repeat failures across future transactions and compounding in value over time.
Labor efficiency that doesn’t sacrifice coverage
In Reynosa, Quincy operates inside the live repair workflow itself. Technician requirements dropped from seven to four while maintaining approximately 75% fault code coverage, a 44% productivity gain achieved by making each technician more effective rather than narrowing the scope of work.
A broader agentic architecture
Quincy is what this capability looks like in one program. The architecture behind it runs wider. Reconext has built and operates a suite of agentic workflows covering materials optimization, teardown automation, and credit recovery, each one integrated directly into our shop floor systems and customer platforms. There is no custom integration work required on the customer side. The capability deploys into your environment as it exists today.
What connects these workflows is not just shared technology but a shared purpose: to find cost, yield, and recovery value that manual operations leave behind. Built and operated entirely in-house, this is infrastructure we have already proven in production, across programs, at scale.
Where this goes next
Expanding this model across additional sites introduces something more significant than incremental efficiency gains. When every repair, failure, and component interaction feeds a shared intelligence layer, the system improves continuously across the network rather than within a single program. Predictive capabilities follow naturally: anticipating failure modes, optimizing inventory positioning, and reducing cost before it materializes.
For enterprise customers, it’s a meaningful shift in what a reverse logistics partner can deliver. Reconext is building toward a future where we function as a data and engineering platform that compounds in value the more deeply it is integrated into your operations. If you’re evaluating how AI can drive measurable impact across your reverse logistics programs, we’d welcome the conversation.
