Hyve Applied Intelligence develops AI software for construction, legislative research, and software development. Our products connect domain data, document understanding, and coordinated AI workflows to help people make informed decisions.
Our Products
Each Hyve product addresses a specific workflow. Hyve Core provides the shared foundation underneath them: knowledge, permissions, source tracking, and AI workflows.
BuildIQ
BuildIQ runs a builder's jobs from the first lead to the final invoice: bids, contracts, schedules, crews, changes, and job costing in one record. Plan reading, which turns architectural plan sets into quantities by trade, is in development.
Legis
Legis brings bills, votes, sponsors, and roll calls from Congress and eight states into one research foundation, with AI-written summaries that record the model and prompt behind them.
HyveVibe
HyveVibe coordinates planning, implementation, and review across AI coding tools. Each change runs from an issue through a plan, the work, tests, and an independent review before a person merges it.
Also from Hyve: FieldIQ for field teams, Echo AI chat agents, and the Knowledge Base behind them.
Hyve Core
Hyve Core is the shared architecture our products are built on. Each organization's knowledge and records stay in their own scope, and the products use AI from more than one provider against records kept in Hyve's own database.
Technology
Our development work combines document understanding, structured data, and AI orchestration. We focus on the connection between a source document, the information extracted from it, and the decision it supports.
Developing a plan reader for architectural plan sets, and a line-by-line take-off that records the sheet, detail, measurement, and formula behind every quantity.
Organizing information with context, permissions, and source references, so applications can retrieve the evidence behind an answer.
Coordinating specialized tools and models through explicit tasks, verification, and human review.
Research and validation
We test extraction quality and workflow behavior against defined tasks and keep the evidence. We welcome conversations with domain practitioners and research groups interested in validating document understanding, vision models, and applied AI workflows.