Plain-language guides to the ideas behind KernelFold: why one model isn't enough, how routing picks the right model per task, and how independent verification catches errors.
Multi-model AI orchestration coordinates several large language models on one request — planning, routing, parallel specialists, verification, and synthesis — instead of trusting a single model's answer.
6 min read
How an LLM router decides which AI model should handle a request: capability scoring, cost and latency budgets, learned accuracy, and automatic failover when a provider degrades.
5 min read
LLMs hallucinate confidently, and a model cannot reliably grade its own work. Independent cross-model verification catches errors before you see them — here is how it works and what it can and cannot fix.
5 min read
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