Two of our co-founders were running Crafting Tomorrow, an EdTech company comparing school curricula across twelve countries. The work needed an AI that could read all 8,200+ documents, some as long as 2,000 pages, all at once. That corpus runs to roughly 120 million tokens. The best models on the market could work with about 0.2 percent of it, once context windows and the degradation of usable context inside them are both accounted for.

While this gap is a huge problem, the worst part is that the AI read the small fraction it could reach and answered with complete confidence. It did not know what it did not know.

Today's AI gets you most of the way. But it will not get you the rest. It is probabilistic, not deterministic, and no amount of more closes the gap. Not more data, not more compute, and certainly not more context. For most things, most of the way is fine. For a bridge, a rocket launch, or a medical decision, it is 100 percent or nothing.

That problem was too important to leave inside an edtech company, so we spun it out. Lodestone Labs builds the missing layer: AI grounded in verified fact, that knows when it does not know, and tells you exactly what it needs to answer. Compact enough to run from a robot offshore to a Raspberry Pi on a satellite. Efficient enough to ground any LLM in your own domain knowledge and rival frontier-model quality, at a fraction of the cost.

The approach is not new work. It builds on decades of practice, and on research that started long before AI was a household name. For the technical case, read The 16 Core Challenges of Current RAG Technologies.

We started in education and in real-time interactive systems, and we are heading into heavy industry. That order is deliberate. Education carries its stakes through importance, scope, and complexity. Interactive systems carry theirs through latency, where a wrong or late answer is visible immediately. Both sectors move quickly and want the innovation, so a young company can reach real data and a real engagement in weeks. Industrial operators cannot move that way, and should not. The compliance runway before an outside team touches production data is long, and it is long for good reasons.

AI is moving from drafting to deciding, and into machines that act on their own. The foundation underneath it must be reliable, efficient, and domain-specific.