Innovations in CPG Packaging and Labels

When WIRED reported this week that Apple engineers have been traveling to Vermont to help a small label manufacturer implement computer vision systems for quality control, the story resonated across the manufacturing world. Apple’s Manufacturing Academy, launched in partnership with Michigan State University as part of the company’s $600 billion U.S. investment commitment, is providing hands-on technical support to small and medium-sized businesses. One of its first success stories is unexpectedly modest: catching bacon labels with the wrong shade of beige before they reach customers.

ImageTek, a Vermont-based manufacturer, collaborated with about ten Apple employees to develop an automated system that detects color errors on CPG packaging labels during production. According to company president Marji Smith, the system identified defective bacon labels with an incorrect pinkish beige before shipment, preserving a major customer relationship. As Smith put it, “We’re not a gigantic company, and we don’t have any AI or software team. What Apple is doing is positively impactful for us.”

This is exactly the kind of practical AI application that validates what many in enterprise technology have argued for years: computer vision in manufacturing is not about replacing human judgment. It is about catching the subtle variations that human eyes might miss, at a scale and consistency that manual inspection simply cannot achieve. Apple’s willingness to share lessons learned from its own manufacturing challenges, including candid discussions of its 2014 “bendgate” experience, demonstrates a level of corporate transparency that benefits the entire industry.

For compliance professionals, the story gets even more compelling: color matching is just the beginning.

Color Is the Simplest Problem on CPG Labels

Apple’s work with ImageTek addresses a binary quality control challenge: Does this color match the reference sample? The camera captures the output, the algorithm compares it to the standard, and any variation beyond tolerance is flagged. This use case demonstrates computer vision’s value for CPG packaging inspection, catching subtle variations in ink application or machine calibration that would pass unnoticed by a human inspector reviewing thousands of labels per shift.

Yet, while color accuracy is critical for brand consistency and customer satisfaction, it is the least regulated aspect of CPG packaging. When a bacon label ships with slightly off-brand beige, the consequence is an unhappy retail buyer. When a product label ships with incorrect regulatory information, the consequences escalate: FDA enforcement actions, market withdrawals, consumer health risks, and reputational damage that can end careers or companies.

According to Loftware’s analysis of FDA enforcement data, label errors were the leading cause of food recalls in 2024, accounting for 45.5% of the 422 recall events—nearly three times more common than listeria contamination. The estimated direct cost to the industry was $1.92 billion, based on the industry-standard calculation of $10 million per recall event. This figure excludes lawsuits, reputational damage, and lost sales.

Most strikingly, 83.85% of those CPG label errors stemmed from undeclared allergens. This is not a color-matching problem. It is a regulatory intelligence problem that demands a fundamentally different kind of AI.

From Spot-Checking Colors to Understanding CPG Label Regulations

At Signify, we have spent years building what we believe is the next evolution of computer vision for CPG packaging compliance. Our proprietary models go beyond simple image comparisons to reference samples. They understand what labels are supposed to say, in accordance with the specific regulatory frameworks governing each distribution market.

A compliance-aware computer vision system must evaluate on a single CPG packaging label:

• Ingredient declarations: Are all required ingredients listed, in the correct order? Do allergen statements meet FDA requirements for prominence and positioning? Does the declaration match the formulation on file?

• Nutritional information: Is the Nutrition Facts panel formatted correctly? Do declared values match laboratory analysis within regulatory tolerances? Are serving sizes appropriate for the product category?

• Claims verification: Are marketing claims substantiated and permissible? Does a “low sodium” claim meet quantitative thresholds? Is an “organic” claim backed by certification?

• Market-specific requirements: Does the packaging meet EU regulations that differ from FDA standards? Are front-of-pack nutrition labels compliant with local mandates? Do country-of-origin declarations meet destination market requirements?

• Brand compliance: Are logos positioned according to brand guidelines? Do trademark symbols appear correctly? Is the packaging consistent with approved artwork?

• Certification marks: Are certification logos (kosher, halal, organic, non-GMO) displayed correctly, and do they match the manufacturer’s certifications?

This is not a comparison problem. It is an interpretation problem. The system must understand regulatory requirements, cross-reference them against label content, and flag discrepancies invisible to anyone without deep regulatory expertise. It must do this across multiple regulatory frameworks, because the same product shipping to the U.S., EU, and U.K. may face three different sets of requirements.

The Architecture of Regulatory Intelligence for CPG Packaging

Signify’s solution goes beyond traditional computer vision into what we call regulatory agents. These are AI systems that combine visual analysis with regulatory knowledge bases to make compliance determinations that previously required human experts.

Our approach integrates several layers of intelligence:

• Computer vision models trained specifically on CPG packaging extract text, identify label elements, detect logos and certification marks, and analyze visual hierarchy. These are not generic OCR systems. They understand the structure of regulatory labels and can distinguish between ingredient lists, allergen warnings, and marketing copy.

• Regulatory knowledge bases encode requirements from FDA, EU, and international frameworks in machine-readable formats. When regulations change, the knowledge base updates, and every product in the system is re-evaluated against current requirements.

• Reasoning agents connect visual analysis to regulatory requirements, making compliance determinations and generating explanations that compliance professionals can review and act upon. When the system flags an issue, it explains which requirement is potentially violated and why.

The result is a system that can analyze a photo of finished CPG packaging and determine, within minutes, whether that package is compliant for each target market. It catches issues that might take human reviewers hours to identify, if they catch them at all.

Why Enterprise CPG Packaging Compliance Demands Production-Ready AI

Apple’s Manufacturing Academy is a model for technology transfer and education. Sending engineers to work directly with small businesses and building custom solutions delivers real value. The bacon label color-matching system Apple built for ImageTek solves a real problem for a specific company.

But enterprise CPG packaging compliance operations require something different: systems that work across thousands of SKUs, multiple product categories, dozens of distribution markets, and constantly evolving regulatory frameworks. These organizations cannot wait for custom engineering. They need production-ready AI applications that integrate into existing workflows, scale with operations, and maintain compliance as regulations change.

That is why Signify has invested in building not just AI models, but complete CPG label compliance automation platforms. Enterprise CPG companies trust our systems because they deliver:

• Consistency at scale: Every label receives the same level of scrutiny, whether it is the first review of the day or the ten-thousandth.

• Regulatory currency: When MoCRA requirements change or new allergen labeling rules take effect, the system updates without custom engineering.

• Audit trails: Every compliance determination is documented, explainable, and traceable. This is critical for regulatory interactions.

• Integration capabilities: The platform connects to product lifecycle management systems, artwork management tools, and quality processes.

• Human-in-the-loop workflows: AI handles detection and analysis, while compliance professionals make the final calls on flagged items.

Freeing Compliance Professionals for What Matters

What is most striking about Apple’s work with small manufacturers is the philosophy articulated by Jamie Herrera, the Apple director overseeing the academy: “It takes more than a training session. You have to turn learning into a real application.”

The same principle applies to CPG packaging compliance automation. The goal is not to replace the regulatory expertise companies have built. It is to free compliance professionals from repetitive tasks, allowing them to focus on the strategic decisions that require their expertise.

Today, highly educated professionals spend hours checking label text against requirements, comparing artwork to brand guidelines, and verifying that nothing has changed since the last review. Industry data shows that 99% of regulatory affairs professionals hold university degrees, with 44% holding master’s degrees or higher. These experts are often performing work that properly trained AI systems could handle, while strategic analysis goes underserved.

Signify’s platform transforms this equation. When AI handles the initial review, human experts focus on edge cases, judgment calls, and strategic positioning of new products in complex regulatory environments. The technology does not diminish their role. It elevates it.

The CPG Packaging Regulatory Landscape Is Only Getting More Complex

Regulatory trends are clear: requirements are becoming more stringent, enforcement is becoming more aggressive, and penalties for non-compliance are becoming more severe. The Modernization of Cosmetics Regulation Act (MoCRA), new FDA food labeling requirements, evolving EU regulations, and country-specific mandates are creating a compliance environment that manual processes cannot manage effectively.

In the first quarter of 2025 alone, the FDA recorded 45 food and beverage recalls, nearly half of which were due to undeclared allergens. Major brands, including Frito-Lay, Nestlé, Quaker, and Trader Joe’s, were affected. These are sophisticated companies with established compliance programs, yet even they are challenged by the complexity of managing label accuracy across vast product portfolios.

The companies that will thrive are those that embrace AI-powered CPG packaging compliance as an amplifier of human expertise. Computer vision that understands regulations, not just colors. Automated review that catches errors before they become recalls. Systems that scale with product portfolios, not compliance headcount.

From Bacon Labels to CPG Packaging Compliance: The AI Journey Continues

Apple’s Manufacturing Academy exemplifies corporate citizenship that strengthens American manufacturing. By sharing lessons learned and providing hands-on technical support, Apple is democratizing access to technologies once reserved for the largest manufacturers.

The color-matching system that helps ImageTek catch defective bacon labels before shipment is a perfect example of AI delivering tangible business value. It validates the approach: computer vision can catch what human eyes miss, at speeds and consistency that transform quality control.

Signify brings the next evolution of this principle to enterprise CPG companies, applying it to the far more complex domain of packaging and label regulatory compliance. Our proprietary computer vision models and regulatory agents extend automated visual inspection into territory that requires not just pattern matching, but regulatory understanding.

We are not just checking colors. We are verifying CPG label compliance across complex regulations, multiple markets, and product portfolios with thousands of SKUs. We help compliance professionals spend less time on mechanical review and more time on the strategic decisions that drive business success.

That is what production-ready AI for enterprise CPG packaging compliance looks like. And that is the future Signify is building, one compliant label at a time.

Ready to See What AI-Powered CPG Label Compliance Looks Like?

Signify’s platform is helping enterprise CPG companies automate packaging and label compliance review across global markets. Request a demo to see how our computer vision models and regulatory agents can transform your compliance operations.

Sources: WIRED, 9to5Mac, Apple Newsroom, Loftware FDA Analysis, New Food Magazine