Cyberhill Launches Cerebro to Bridge Frontier LLMs and Enterprise Data

The core challenge for large organizations lies in fragmented data stored across dozens of systems, where institutional knowledge often remains siloed. Cerebro operates as a truth engine, connecting to existing systems of record to build an ontology of business terms and rules. Instead of forcing models to rely on broad internet training, the system supplies only the necessary context for each specific query. This architecture allows companies to retain their data in place while maintaining full ownership of the resulting semantic layer as intellectual property.

Efficiency gains are a primary focus of the new deployment. By limiting the data sent to a model, Cyberhill reports that token costs can drop by up to 80%. Performance metrics are equally stark: in internal tests, average response times plummeted from 112.3 seconds to 14.9 seconds. Accuracy also saw a significant shift, with performance on a 74-question enterprise set climbing from 25.7% to 93.2% when utilizing the semantic layer. For CEO Rob Buller, the shift is fundamental, arguing that the competitive advantage in enterprise AI no longer rests on the model itself, but on its ability to understand and justify decisions based on a company’s specific operations.