AI often carries a shadow of suspicion in industries like food and beverage, especially when it comes to compliance and product safety processes. The perception that AI is a “black box” persists for a reason. Large language models (LLMs) can provide remarkably accurate answers, yet understanding how those answers were derived can feel nearly impossible. These models execute trillions of calculations behind the scenes, producing coherent responses in seconds. But tracing the exact path from input to output is extraordinarily complex.

Research has shown that while LLMs can often solve mathematical problems with reasonable accuracy, they don’t approach them the way humans do. Instead of applying standard calculations, LLMs lean on pattern recognition, generating responses that are statistically likely based on their training data. Often, this produces the correct result, but not through genuine mathematical reasoning. And when asked to explain their method, LLMs tend to default to textbook-style answers, offering a plausible narrative rather than a true reflection of how the solution was reached.

This underscores the inherent challenge of AI in high-stakes environments: LLMs generate results without an innate understanding of their own logic. They are statistical word generators operating under algorithmic rules, capable of producing useful insights yet often unable to explain the path taken to arrive at them. In food and beverage (F&B), where traceability, accuracy, and auditability are critical, this characteristic can understandably foster skepticism.

Knowledge architecture: Illuminating the black box

Fortunately, the field is evolving rapidly. Confidence in AI outputs depends on traceability and explainability, especially when decisions impact compliance, safety, or product quality. Two key approaches are reshaping how AI operates in practice:

1. Knowledge graph integration

Knowledge graphs (KGs) organize information into structured, semantic relationships, defining how data elements interconnect. For example, a KG can encode the rule that an apple can be part of a recipe, but a recipe cannot be part of an apple. When LLMs reference these structures, their outputs gain grounding and traceability. Responses can include paths taken through the KG, allowing humans to verify reasoning and understand the sources behind each conclusion. This helps create explainability in several ways:

• Grounding: Verifying facts or augmenting context with structured data.
• Traceability: Showing the sources of information (e.g., “this fact comes from this specific data structure in the KG”).
• Structured Query Explanation: When an LLM accesses a KG, it can return responses with the paths it followed, giving humans confidence in the information.

2. Neurosymbolic AI

Neurosymbolic AI combines the predictive power of neural networks with the logical clarity of symbolic reasoning. In this approach, AI models are supported by rules, calculations, and knowledge structures, which allow outputs to be explained, audited, and validated. Key capabilities include:

• Reasoning Chains: Clearly articulated steps from input to output. 
• Modular Architecture: Transparency in whether decisions stem from neural networks or symbolic rules.
• Constraint Enforcement: Rules ensure calculations are accurate and compliant, reducing the risk of hallucination and building auditability into processes.

For F&B teams, these advances show that AI can move beyond opaque outputs, providing actionable, defensible insights that integrate seamlessly into compliance, quality, and product development workflows.

Transforming AI into a trusted partner

TraceGains is actively investing in these innovations, ensuring that AI is not only accurate but also explainable. Our proprietary knowledge architecture makes outputs auditable and reliable, creating confidence in outcomes without sacrificing efficiency. This means AI is no longer a mysterious black box: it becomes a partner that augments human expertise while respecting regulatory rigor.

The potential extends across the enterprise. TraceGains’ Supplier Compliance with Intelligent Document Processing (IDP) can accelerate supplier onboarding, verify ingredient and packaging data, and flag potential compliance risks automatically. By embedding advanced AI into structured, traceable workflows, F&B companies can move faster while reducing the chance of errors.

Toward a transparent, connected AI future

At TraceGains, we’re committed to moving AI beyond the “black box” perception so that the experts—those who safeguard quality, compliance, and safety—remain the true guardians of every decision. Our role is to provide transparent, intelligent, and actionable AI that supports their expertise, helping insights flow seamlessly across compliance, quality, and product development. This is the connected ecosystem we’re building: one where AI empowers the people shaping the future of F&B.

Learn more about Intelligent Document Processing (IDP) from TraceGains.