Traditional general-purpose models often falter when handling complex, domain-specific data due to their broad but shallow training. Trunk Tools, a construction project management company, tackled this challenge by developing a specialized three-layer architecture—perception, semantics, and agents—that processes highly detailed industry data with precision. Their system reads and interprets messy documents, builds semantic knowledge graphs, and employs AI agents to automate workflows. This approach has dramatically reduced review cycles from around 60 days to 10, preventing costly errors and enabling documents to be analyzed at scale with up to 95% accuracy.
The perception layer deciphers symbolic and nuanced construction details, the semantic layer links information contextually, and agents execute highly relevant workflows, such as reviewing submittals for missing or conflicting data in seconds. These agents work collaboratively, autonomously managing tasks and follow-ups. The results include significant time savings in various project tasks and avoidance of costly mistakes, such as unrecorded structural changes or pricing discrepancies. Trunk Tools’ methodology provides a powerful blueprint for other industries handling large volumes of unstructured, highly specialized data, emphasizing modularity and domain-specific fine-tuning over generic models.
