How swarm intelligence solves routing problems in 20 seconds without training data

Fred Gertz completed his PhD in electrical engineering under the inventor of the modern magnetic hard drive, then left academic research to solve a problem that's stumped manufacturers for decades: how to optimize complex operations when you have almost no data. At Collide Technologies, he's applying swarm intelligence to tackle NP-hard scheduling and routing problems that LLMs fail at spectacularly.

His approach comes from an unexpected place. While most AI startups chase massive datasets and GPU clusters, Fred turned to ant colonies. These insects solve complex logistics problems without central coordination, training data, or computing power. Their collective behavior cracks the same mathematical challenges that paralyze manufacturing floors: which routes minimize delivery time, how to assign hundreds of workers to shifting tasks, what machine parameters balance throughput against reliability.

The methodology borrows from operations research and Taguchi's philosophy, which Fred positions against Six Sigma's dominance. Where Six Sigma optimizes for low variation, Taguchi argued customers deserve the best possible product every single time. That shift in thinking leads to different math: instead of reducing standard deviations, you map how every process parameter mathematically connects to business outcomes like profit or quality. The problem? Operations research textbooks are dense enough to intimidate PhD holders. Collide's swarm algorithms make those techniques accessible to companies running on spreadsheets.

Topics discussed:

• Ant colony optimization combining search functions and route optimization to solve scheduling problems in 20 to 30 seconds

• Operations research and Taguchi methods versus Six Sigma's statistical process control approach for manufacturing optimization

• Delivering ROI with spreadsheet data instead of requiring IoT sensors and six month data collection projects

• IQ OQ PQ validation frameworks from pharmaceutical robotics applied to AI model deployment in regulated industries

• Why NP complete problems are better AI targets than tasks humans already perform well

• Agent coordination across 500 enterprise agents as swarm intelligence's next application beyond LLM reasoning models

• Generating structured outputs from API calls without training data or few shot examples

• Rate limiting and context window management for stateful applications like production planning tools

• Manufacturing data environments spanning paper maintenance logs to live vibration sensors in the same facility

• Evaluating AI without numeric metrics when outputs are text based recommendations rather than classifications