On July 9, 2025, a webinar hosted by Michelle, a design leader at 1848 Ventures, discussed how AI can empower small and mid-sized food businesses.

The webinar, lasting about 30 minutes with an open Q&A session, was born out of a clear observation at the Food Safety Summit earlier this year: a significant and widespread curiosity about AI's potential. The primary goal was to demystify AI, explain its relevance to food safety and compliance, and showcase its value, while also inviting participants to share their own challenges.

Michelle was joined by Will and Yanyan, the co-founders of BruceAI, an 1848 Ventures portfolio company that specializes in using AI to simplify supplier compliance for food SMBs. Their expertise provided a deep dive into the technology and its specific use cases within the food industry, making the webinar a valuable resource.

Why AI for Food? BruceAI's Origin Story

Yanyan, with a background in early-stage startups, emphasized the underserved market of small and mid-sized food businesses. "They're essential to the food supply chain but often short on resources and underserved when it comes to tools and technology," he noted. BruceAI was born from conversations with these manufacturers, consistently pointing to one major pain point: supplier management and compliance.

Will, who has dedicated his career to applying new technology to the food industry, saw the recent advancements in AI, particularly Large Language Models (LLMs), as a natural fit. "Food manufacturing and food safety are perfect for LLMs," he explained. "Food safety in particular lives at this intersection between language and science."

Unpacking Large Language Models (LLMs)

So, what exactly are LLMs? Will clarified that LLMs are a specific type of AI that works with "language" – answering questions, translating, summarizing, rewriting, formatting, and extracting information from text. Unlike traditional computing that relies on precise formulas, LLMs are built on understanding word relationships and natural language.

LLMs' Strengths:

1. Language Transformation: Excellent at summarizing, rewriting, formatting, and extracting information from text.

2. Adaptability: Can be trained on a broad set of general knowledge and adapted to specific subject areas with proper context.

LLMs' Limitations:

1. Precision Mathematics: Not ideal for tasks requiring exact mathematical or scientific results.

2. Contextual Nuance: Can be confused by industry-specific terminology that has broader meanings (e.g., "audit," "recall" in food safety).

Practical Applications of LLMs in Food Manufacturing

Yanyan and Will highlighted several immediate and impactful use cases for LLMs in food manufacturing:

1. Document Generation: Automating the creation of HACCP plans, SOPs, policies, and training manuals, significantly reducing manual effort and expert input. One customer even drafted a complete HACCP plan using ChatGPT in under an hour.

2. Improved Compliance: Extracting and processing information from unstructured data like Certificates of Analysis (COAs), audit reports, and IoT sensor data to transform fragmented information into actionable intelligence.

3. Enhanced Employee Training: Making company knowledge bases more accessible and searchable through natural language queries.

4. Regulatory Compliance: Rapidly understanding the impact of new regulations and policies, helping businesses adapt faster.

5. Speed and Affordability: AI's ability to process vast amounts of information quickly and at a lower price point makes these capabilities accessible to companies of all sizes, democratizing advanced technology.

The Compliance Crunch for SMBs

Despite the potential of AI, small and mid-sized food manufacturers face significant challenges in compliance:

1. Rising Costs: Compliance consumes a higher percentage of revenue for smaller companies.

2. Time-Consuming Audits: Scattered or paper-based documents make audit preparation a lengthy process.

3. Lack of Resources: Difficulty in building a strong food safety culture without dedicated staff or ongoing training.

4. Regulatory Volatility: Constant changes make it hard for small teams to plan and adapt.

5. Traceability Gaps: Many struggle with FSMA 204 readiness and accurate allergen control.

Of these, supplier compliance stands out as a top concern. Will explained, "Supplier compliance touches everything; it can delay production, trigger recalls, and involves managing hundreds of documents that users often have the least control over." It's a highly administrative task, often taking valuable time away from core food safety expertise.

Recent supply chain disruptions have further emphasized the need for robust supplier compliance. Companies are now diversifying suppliers, onboarding backups, and strengthening supplier programs to ensure reliability. LLMs can support these best practices by:

1. Streamlining SOPs: Assisting in creating, refining, and enforcing Standard Operating Procedures.

2. Benchmarking: Comparing internal practices against external standards to identify compliance gaps.

3. Personalization: Allowing for dynamic customization of documents like supplier questionnaires and scorecards, adapting to each business's unique needs without extensive software setup. LLMs can easily process paperwork in various languages, too, saving time and effort.

Getting Started with AI: Practical Advice

For small and mid-sized food manufacturers considering AI, the experts offered practical advice:

1. Start Small: Begin by identifying repetitive, time-consuming, or error-prone tasks.

2. Focus on High-Value, Low-Effort Cases: Choose a problem where the value is easy to measure (e.g., time saved, cost reduced).

3. Define Success: Establish measurable KPIs to track the impact of your AI initiatives.

4. Phased Implementation: Start with a small pilot, test effectiveness, and scale based on proven results.

5. AI as an Assistant: Remember, AI tools are assistants, not replacements for human expertise. They enhance productivity, allowing existing staff to achieve more.

6. Explore Free/Low-Cost Options: Leverage free AI tools or features already built into existing software (ERP, QMS).

7. Prioritize Data Security: Always review terms and conditions regarding data usage. Choose solutions with clear policies that don't use your data to train models.

8. Measure ROI Holistically: Look beyond just cost savings; consider intangible benefits like employee satisfaction and innovation.

9. Choose Wisely: Established providers offer comprehensive solutions, while newer players may offer more agile, specialized, and affordable options. Prioritize flexibility as your operations evolve.

Ultimately, AI and LLMs are here to stay and are transforming how work gets done. By embracing them thoughtfully, small and mid-sized food businesses can work smarter, enhance compliance, reduce risk, and contribute to a safer food supply chain.

Watch the full webinar recording HERE