Trunk Tools' stack cut document review from 60 days to 10 by ditching general-purpose models

Most verticals aren’t clean, well-oiled Saa S databases; the reality is ugly documents, proprietary schemas, implicit workflows, and long‑running tasks that most general-purpose models struggle with. This prompted construction project management company Trunk Tools to build a specialized, three-layer architecture — perception, semantics, agents — based on highly-detailed data to support high-accuracy, highly-relevant industry automation.Their purpose-built stack has shrunk review cycles from months to days, prevented costly field errors, and given autonomous agents the ability to reason over millions of pages of documentation, Trunk says. “We really set out to take the data from dispersed systems, pre-process it, structure it, go through our ontology into a knowledge graph, and then train AI models,” said Sarah Buchner, Trunk’s founder and CEO and a former carpenter. For builders in other verticals, Trunk’s approach could serve as a blueprint for transforming data chaos into agent‑ready, industry-specific workflows. Where general-purpose LLMs break down on industry data Foundation LLMs, while powerful, are optimized for breadth, not always depth. “General-purpose LLMs are trained to be okay at everything, so they're weak at anything niche,” said Kriti Faujdar, a senior product manager working in AI infrastructure, agentic AI, security, and LLM platforms. For instance: Rare terms, domain-specific reasoning, the unspoken context that any practitioner “just knows.” Web, app, and software developer Sébastien De Bollivier agreed that the biggest bottleneck is reliability on data that is “jargon-dense, abbreviation-heavy, and format-specific.” “A GPT-4-class model can understand a French legal contract, but will fumble the specific article references practitioners need to cite,” he said. Besides, the most valuable enterprise data never made it into pretraining anyway, Faujdar pointed out. It's sitting in internal systems and proprietary formats. “RAG helps a litt