Premier Construction Software is a global, AI-powered construction ERP that helps growing general contractors control job costs, cash flow, and risk across every project in one easy-to-use cloud platform.
Replace disconnected accounting, project management, and field tools with a single source of truth for WIP, change orders, and forecasting, so your team can shorten billing cycles and keep margins on track.
How we're different:
• Built for general contractors, owners, and land developers managing multi-project, multi-entity portfolios.
• Advanced construction accounting with detailed job costing, real-time WIP reporting, and cash-flow forecasting to spot issues before they hit the P&L.
• Project and field management with RFIs, subcontracts, drawings, and change orders tied directly to the budget and schedule.
• Automated billing, approvals, and payroll to reduce manual entry and speed up collections.
• Unlimited entities, consolidated reporting, and role-based dashboards.
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Windocks is a leader in cloud native database DevOps, recognized by Gartner as a Cool Vendor, and as an innovator by Bloor research in Test Data Management. Novartis, DriveTime, American Family Insurance, and other enterprises rely on Windocks for on-demand database environments for development, testing, and DevOps. Windocks software is easily downloaded for evaluation on standard Linux and Windows servers, for use on-premises or cloud, and for data delivery of SQL Server, Oracle, PostgreSQL, and MySQL to Docker containers or conventional database instances.
Windocks database orchestration allows for code-free end to end automated delivery. This includes masking, synthetic data, Git operations and access controls, as well as secrets management.
Windocks can be installed on standard Linux or Windows servers in minutes. It can also run on any public cloud infrastructure or on-premise infrastructure. One VM can host up 50 concurrent database environments.
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QDeFuZZiner
Project is basic entity in QDeFuZZiner software. Each project contains definition of two source datasets to be imported and analyzed (so-called "left dataset" and "right dataset"), as well as variable number of corresponding solutions, which are stored definitions of how to perform fuzzy match analysis. On creation, each project is assigned unique project tag. During raw data importing to server, corresponding input tables get that tag appended in their name. This way, imported tables are always tagged by the project name, which ensures their uniqueness. During importing and also later on, during solutions creation and execution, QDeFuZZiner is creating various indexes on the underlying PostgreSQL database, which facilitate fuzzy data matching. Datasets are imported from source spreadsheet (.xlsx, .xls, .ods) or CSV (comma separated values) flat files to server database, where corresponding left and right database tables are then created, indexed and processed.
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