About Us
Break The Web is a research and product lab building a persistent post-training knowledge layer for AI systems.
The problem
Models are trained on a fixed snapshot of information. Once training ends, the world keeps changing, but the model doesn’t. A gap immediately grows between what the model “knows” and what is true.
Today, we paper over this gap with web search. But search returns documents, not ground truth. Without persistent knowledge of the present, the model must reconstruct reality from scratch for every query.
This comes at a high cost. Model makers spend more money and compute to produce answers about the present that are slower and less reliable. Users are left with fewer present-day tasks they can trust AI to perform. It is a major blind spot in otherwise highly capable AI systems.
What we've built
LiveLM, a persistent post-training knowledge layer for AI systems.
Rather than requiring models to search the web at query time, our proprietary technology transforms the live web into a continuously updated knowledge graph of real-world facts, events, relationships, and change.
Refreshed every 30 minutes, LiveLM maintains a living ground truth of the world from training cutoff to real time, allowing models to reason over the present with the speed, efficiency, and fluency of their own memory.
Why it matters
Lower costs. Knowledge is computed once and reused, eliminating the repeated retrieval, parsing, and compute required by web search.
Faster results. Relevant facts are injected directly into the model’s context, bypassing external web calls and document interpretation.
Consistent ground truth. LiveLM reconciles information across sources and tracks changes over time, providing a more reliable view of reality than any individual document.
Proactive understanding. LiveLM gathers information continuously, building an evolving understanding of the world before a query is made.
Offline accessibility. LiveLM can run on-device, locally, or in closed environments, bringing current knowledge to systems that can’t—or shouldn’t—connect to the open web.
Where it's going
We believe AI systems will rely on a three-layer knowledge stack: model weights for reasoning and stable knowledge, LiveLM for persistent knowledge of the changing world, and web search for raw sources and long-tail detail.
LiveLM does not replace search. It fills the gap between search and the model by maintaining the knowledge that would otherwise be reconstructed at query time.
The larger idea is simple: models should not have to reconstruct the present for themselves on every query. Current knowledge of the world can be maintained once, updated continuously, stored outside the model, and shared across models, agents, and products.
The world changes by the minute. Every AI system’s knowledge should, too.
