The End of Black-Box AI.

Hearth Insights provides the Holt platform: a system for auditable, graph-based agentic workflows designed specifically for highly regulated environments. In healthcare, finance, and bioinformatics, an AI system is only as defensible as its audit trail. We change the paradigm by ensuring every single action is recorded on an immutable ledger. This guarantees compliance, whether you are operating under clinical validation requirements or within the FCA Digital Sandbox. Current automated pipelines routinely fail enterprise implementation because they cannot prove how they arrived at an answer. Holt enforces absolute provenance.

How does Holt provide an audit trail for clinical validation?

Our architecture is not theoretical. In a recent peer-reviewed study published in Gut (British Medical Journal), the Holt framework enforced an auditable pipeline mapping free-text clinical notes to standardised Human Phenotype Ontology (HPO) terms for Inflammatory Bowel Disease (IBD) research.

• The Challenge: Manual coding is too slow, but existing automated pipelines lack the comprehensive audit trails required for regulated clinical environments.
• The Holt Engine: A workflow utilising four agents operating in a secure, air-gapped environment with minimal privileges. Every step: processing text, retrieving information, creating prompts, and executing the model, was recorded immutably.

Why do edge models outperform massive cloud models in graph-based workflows?

The study yielded a critical architectural insight: model size is largely irrelevant in an auditable, graph-based pipeline.

When tested across 15 different large language models, a compact 3-billion parameter edge model (Ministral-3) outperformed a massive 120-billion parameter cloud model (GPT OSS) in overall precision and recall.

By orchestrating AI through Holt and decomposing complex clinical workflows into discrete, verifiable steps, institutions can deploy highly secure, locally-hosted edge models that deliver superior accuracy. The result is a substantially reduced AI carbon footprint, eliminated cloud compute costs, and zero data egress.