How This 16-Year-Old Company Is Making AI Accessible for Small Businesses
▼ Summary
– AI is being genuinely applied in supply chain and logistics, with companies like Flexport, Uber Freight, and startups developing tools for both large and small businesses.
– Netstock’s generative AI tool, the Opportunity Engine, integrates with customer dashboards to provide real-time inventory recommendations using ERP data.
– The tool has delivered one million recommendations, with 75% of customers receiving suggestions valued at $50,000 or more, saving businesses significant money.
– Bargreen Ellingson, a family-run company, initially hesitated but now uses the AI cautiously, empowering less-senior staff and improving decision-making without full automation.
– Netstock focuses on customer outcomes and avoids over-reliance on generative AI to prevent inaccuracies, maintaining human oversight for all recommendations.
While much of the conversation around artificial intelligence centers on whether we’re witnessing a bubble, certain sectors, like supply chain and logistics, are demonstrating real, practical applications. Small businesses often struggle to adopt advanced technology, but companies like Netstock are changing that narrative. Founded in 2009, this inventory management specialist has introduced a generative AI tool designed specifically for smaller enterprises, helping them optimize operations without the complexity or cost typically associated with AI integration.
Netstock’s new feature, called the Opportunity Engine, integrates directly into its existing customer dashboard. It draws data from a company’s Enterprise Resource Planning system and delivers real-time, actionable recommendations. According to the company, this tool has already generated over one million suggestions, with 75% of customers receiving advice valued at $50,000 or more. These aren’t abstract promises, businesses are reporting tangible savings and smarter inventory decisions.
Take Bargreen Ellingson, for example. This family-run restaurant supply company has been in operation for 65 years. Like many established firms, they were initially hesitant to embrace AI. Jacob Moody, the company’s chief innovation officer, admitted that “old family companies don’t trust blind change.” Introducing a “black box” system to warehouse managers wasn’t an option. Instead, Moody presented the tool as optional, something staff could use if they found it helpful. This cautious, voluntary approach allowed the team to gradually build confidence in the technology.
Moody soon observed meaningful benefits. The AI helped sift through dense reports and identify patterns that humans might miss, especially during off-hours. While not flawless, the system excelled at “creating signals from the noise.” Perhaps more importantly, it empowered less experienced employees. Moody highlighted a warehouse worker with a high-school education who quickly learned to interpret AI-driven insights based on his hands-on experience. “He feels empowered,” Moody noted, illustrating how the tool complements human expertise rather than replacing it.
Netstock’s co-founder, Kukkuk, understands the skepticism surrounding AI. Many products, he acknowledges, are little more than glorified chatbots tacked onto existing software. What sets the Opportunity Engine apart is its foundation in over a decade of industry-specific data, rigorously protected under ISO standards. The system uses a blend of open-source and proprietary AI models, and it learns not only from explicit user feedback, thumbs up or down, but also from whether recommendations are actually implemented.
Kukkuk is careful to note that his incentives differ from those of social media platforms. “I don’t really care about eyeballs,” he says. “We care about outcomes for the customer.” This focus on practical results over engagement metrics shapes Netstock’s design philosophy. The AI suggestions are prominent but easy to ignore, a far cry from the aggressive, all-in approach seen in some consumer applications.
The company is also mindful of the limitations of current generative AI. Allowing too much user interaction, Kukkuk warns, could lead to inaccuracies or “hallucinations.” For now, the tool is designed to support, not supplant, human decision-making. At Bargreen Ellingson, every AI recommendation is reviewed by a person before any action is taken. “We’re not letting the AI engine make any inventory decisions that a human hasn’t screened,” Moody emphasized.
This measured adoption reflects a broader caution. Even as Moody sees value in the technology, he expresses concern about its long-term implications. He wonders whether increased reliance on AI might reduce the need for in-house data experts, though he hopes it will instead free them for more strategic roles. What matters most, he insists, is retaining deep institutional knowledge and ensuring that someone always understands why the AI makes the recommendations it does.
Netstock’s approach offers a blueprint for how AI can be responsibly integrated into small and medium-sized businesses. By focusing on transparency, user control, and measurable outcomes, the company is making advanced technology accessible, and trustworthy, for organizations that might otherwise be left behind.
(Source: TechCrunch)
