Two of our co-founders were running 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. The best models on the market could see less than two percent of them.

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 core of our approach is not a weekend idea we've rushed to market. It builds on decades of experience, and research that began long before AI was a household name. For the technical case, read The 16 Core Challenges of Current RAG Technologies.

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. We are proudly building from Norway and are currently a part of RunwayFBU's AI and Robotics Lab, where we're developing alongside the industrial partners ambitious enough to solve this problem first.