Dillygence provides an AI‑driven digital twin platform that creates a real‑time virtual replica of a factory’s equipment, processes, and material flows. By ingesting sensor data and running continuous simulations, it identifies hidden losses, quantifies carbon and cost impacts, and delivers actionable recommendations through design and operation optimizers to improve throughput, reliability, and energy efficiency.
Funding
Funding not disclosed
Founders
Product
Problem
Factories often suffer from low throughput, high downtime, excess scrap, and inefficient internal logistics, leading to elevated production costs, wasted energy, and increased carbon emissions. Traditional optimization relies on manual analysis and isolated tools, making it difficult to achieve holistic, data-driven improvements across the entire asset lifecycle.
Solution
Dillygence offers an AI-powered digital twin platform that creates a virtual replica of a factory’s equipment, processes, and material flows. By continuously ingesting real-time sensor data, the platform simulates operational scenarios, identifies hidden losses, and recommends actionable changes to boost throughput, reliability, and energy efficiency. The solution includes a Design Optimizer for early-stage layout and capacity planning, and an Operation Optimizer that monitors OEE, scrap rate, and internal transport to drive continuous improvement. Insights are presented through dashboards that link performance metrics directly to carbon and cost impacts, enabling plant managers and ESG leaders to align productivity gains with sustainability goals.
Target Audience
Primary customers are manufacturing plants and industrial sites seeking to improve production efficiency, reduce energy waste, and meet sustainability targets, including plant managers, operations engineers, and corporate ESG officers.
Features
- Real-time digital twin model that synchronizes with PLCs, SCADA, and IoT sensors for end‑to‑end process visibility
- AI-driven analytics that quantify the carbon and financial impact of downtime, scrap, and internal logistics
- Design Optimizer module for virtual layout planning, flow simulation, and capacity forecasting before physical implementation
- Operation Optimizer that continuously monitors OEE, synthetic yield, and reject rates, providing root‑cause diagnostics and automated improvement suggestions
- Integrated dashboard linking key performance indicators to energy consumption, CO₂ emissions, and inventory levels
- API and data export capabilities for seamless integration with existing MES, ERP, and ESG reporting systems