Business
Theft-Proofing Retail: How TRACARTS Revolutionizes Cart Security

The problem of shopping cart theft is as old as the shopping cart itself. According to the Food Marketing Institute, approximately 2 million shopping carts are stolen or simply go missing every year, leading to substantial monetary losses for retailers that trickle down to the everyday consumer.
The escalating cost of living is a growing concern not only for consumers but also for retailers. While stolen carts may not be making regular headlines, they are a costly issue that threatens to worsen the economic strain that has already taken hold, with entire municipalities now getting involved in the missing cart problem.
Recently, the city of Fayetteville, North Carolina, earmarked $78,000 of taxpayer funds to tackle the cart problem and used the money to round up as many missing carts as possible over a two-year period. Albuquerque, New Mexico, ran a similar program, retrieving over 1,800 carts in just two months.
Missing and stolen carts create economic hardship, issues for consumers, and blight for cities. Thankfully, TRACARTS is a company stepping forward with a technology-informed solution, working to significantly reduce the number of carts that are stolen or otherwise go missing every year and save retailers time and money.
With each cart taken from a retailer, that retailer stands to lose upward of $180. Yet the millions of carts that go missing each year are a significant hit to not only the retailer but also the shoppers because when retailers seek to recoup funds lost due to missing and stolen carts, they are often forced to raise their prices.
TRACARTS has considered the human element of the cart problem with its system. In fact, the psychological aspect of the TRACARTS system is likely why it works so well.
“There are those retail stores that protect their carts by charging money from customers, who get a refund once their cart is returned,” says Chaya Grosinger, Chief Administrative Officer for TRACARTS. “In the U.S., this security measure is utilized with a quarter. If you wanted a cart, would you be willing to spend or lose 25 cents for a cart that costs upwards of $180?”
There is also an altruistic side to the cart problem that TRACARTS is leveraging. Recently, the question of whether one returns a cart after use has become a social media test of moral righteousness. The general consensus seems to be that “good people” return carts, while those who don’t face society’s harsh judgment.
This litmus test is part of TRACART’s multifaceted approach to solving the missing cart problem. TRACARTS is betting on the good feeling that comes from returning one’s cart.
Along with psychology, TRACARTS is also using technology to address the issue of missing and stolen carts. They know that the psychological pull of a “good deed” may not be sufficient to truly address this costly issue, so the solution must be multifaceted.
The user-friendly TRACARTS system makes it easy to track and secure shopping carts while they’re in use and when they are returned. The TRAC hub — a series of customizable shopping cart trains arranged in one, two, or three multidirectional lines — can be installed in any retail store’s parking lot. Strategic placement of the TRAC hub allows for easy access to the carts without having to weave through parked cars.
When the carts are not in use, the TRAC system locks them into place. They can be released through the system’s smart technology program, with the TRAC kiosk acting as the customer interface that dispenses or accepts them. Shoppers can use a White Label app, a fob, a PIN code, or a phone number, among various other identification forms, to release the carts.
Returning the carts is hassle-free. Shoppers simply place the carts back into the TRAC hub without further interaction with the kiosk.
The integration of smart technology is another facet of the TRACARTS system that makes it stand apart from other solutions. It provides retailers with valuable data analytics and ways to engage with their shoppers. TRAC dashboard is entirely customizable, allowing it to meet the specific needs of different retailers depending on what date they are interested in collecting. Shoppers are incentivized to return their carts and given access to special promotions, such as VIP discounts and rewards, culminating in a positive shopping experience for both the shopper and the retailer while also gamifying the cart return process.
While the issue of stolen and missing carts will not vanish overnight, TRACARTS is deploying advanced technology, social consciousness, and psychology to help retailers save time and money. As more retailers realize that this problem will not simply go away on its own, they will turn to solutions like TRACARTS to help them manage their cart inventory and make shopping at their stores a more pleasant experience.
Business
MetaWorx: Building Full-Stack AI Teams, Not Just Automation

Automation still dominates most headlines, yet the returns often fail to meet expectations. A sprawling chatbot rollout might shave a few support tickets, but it rarely shifts the profit-and-loss statement in a lasting way.
McKinsey’s 2025 workplace survey pegs AI’s long-term productivity upside at $4.4 trillion, but only one percent of enterprises say they’ve reached true “AI maturity.” MetaWorx, a Dallas, Texas-based AI employee agency founded by Rachel Kite, argues that the shortfall has nothing to do with models and everything to do with people.
“Treat AI like a point solution and you’ll get point-solution results,” shares Kite. “You need a roster that can carry the ball from raw data to governance, or the whole thing stalls at the proof-of-concept phase.”
The pod blueprint
When a plug-and-play automation script collapsed under real-world data drift, costing Kite a lucrative contract, she sketched the six-person “pod” that now anchors every MetaWorx engagement:
- An infrastructure architect to tame compute costs.
- A data engineer to secure and shape pipelines.
- An applied scientist to prototype models against live feedback loops.
- An MLOps engineer to automate rollback and retraining.
- A domain product lead translates forecasts into features users actually notice.
- Ethics and compliance analysts to stress test outputs for bias and keep the audit.
The team’s first sprint still delivers a quick-win bot — “small enough to calm the CFO,” jokes Kite — but the roadmap quickly pivots to reliability, explainability, and eventually optimization. By tying every algorithmic decision to a quantifiable business metric, the pods turn AI from a science project into a growth lever.
Recruiting for curiosity, not credentials
With Bain & Company predicting a global AI-skills crunch through 2027, MetaWorx has stopped chasing unicorn résumés. Instead, it hires “adjacent athletes”: a computer-vision PhD who hops from medical imaging to warehouse surveillance, or a former journalist who recasts her nose for story into prompt-engineering finesse.
“Domain expertise expires fast,” Kite says. “What doesn’t expire is the instinct to ask better questions.” The result is a lattice of overlapping skills that stays flexible when models wander into the long tail of edge-case data.
A culture of rapid experiments
Inside MetaWorx, every idea faces the same litmus test: ship something — anything — into a user’s hands within 21 days. The “three-week rule” forces prototypes into the wild early, where failure is cheap and feedback is swift. Post-mortems, including cost overruns, are circulated company-wide, erasing any stigma associated with missteps.
That laboratory mindset powers velocity. “Our first model is almost always wrong,” Kite admits, “but version 1.0 is the tuition we pay for version 2.0.” The philosophy echoes her TEDx talk on resilience: progress is iterative, not heroic.
How leaders can steal the playbook
Executives itching to replicate MetaWorx’s results don’t need a blank check. Kite offers a five-step sequence:
- Inventory pain points, not tools: Walk the P&L line by line and tag the friction you can measure.
- Map the stack to the problem: A recommendation engine, for instance, requires behavior data, retraining triggers, and feedback capture — automation alone won’t suffice.
- Stand up a pod: Reassign existing talent into a cross-functional tiger team before hiring externally; the chemistry test is free.
- Measure the story, not just the statistic: Pair model accuracy with human-scale metrics like ticket backlog or employee churn.
- Budget for the boring: Reserve at least 30 percent of spend for MLOps and governance; Stanford’s HAI review links most AI failures to neglected upkeep.
Taken together, those steps shift AI from a pilot novelty to an operational habit that compounds value rather than topping out after an initial PR splash.
Character still scales faster than code
MetaWorx plans to double its headcount this year, yet Kite insists the secret isn’t a proprietary framework or a monster war chest. It’s credibility. Clients see a founder who has wrestled with the same outages and surprise bills they face. That authenticity converts skeptics faster than any algorithmic novelty.
“Tools level out,” Kite says. “Culture compounds.”
The insight lands in a marketplace still dazzled by generative fireworks. Yes, MetaWorx ships models and dashboards, but its true product is a mindset: resilience over rigidity, questions over credentials, experiments over edicts. In Kite’s world, automation is merely the appetizer. The main course is a full-stack team that knows why the model matters to the business and who owns its success after launch day.
And that, Kite argues, is how AI finally graduates from cost-cutter to growth engine, one curious pod at a time.
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