CodeGig
Built in Louisiana to help industrial operators predict emissions, reduce costs, and simplify compliance.
CodeGig is a SaaS platform that helps engineers manage complex industrial datasets and extract actionable insights from real-time operational data. Designed for emissions monitoring, process optimization, and predictive modeling, the platform automates calculations, flags compliance risks, and generates export-ready reports. By replacing manual analysis with statistically sound automation, CodeGig helps teams focus on problem-solving instead of paperwork.
About the Builder Team
CodeGig is led by Kellen Francis from Luling, Louisiana. Kellen brings together technical expertise and operational insight shaped by real-world industrial challenges. The work is motivated by the growing cost, complexity, and risk facing engineers responsible for emissions compliance and process performance across the Gulf Coast.
Challenge Area
Industrial Emissions & Compliance
Industrial facilities across Louisiana face rising regulatory pressure, growing data complexity, and increasing costs tied to emissions management. Most operators still rely on manual workflows, fragmented datasets, and delayed reporting cycles, even as expectations for accuracy and speed continue to rise.
Steve Jobs
Apple Inc.
The Problem
The Problem
Engineers are forced to manually search across multiple datasets, build calculations by hand, and generate compliance reports through time-consuming processes. In some facilities, emissions reporting can take hours to complete and remains error-prone, especially during abnormal operating conditions. These inefficiencies delay decision-making and increase regulatory risk.
Steve Jobs
Apple Inc.
The Solution
The Solution
CodeGig addresses this challenge by automating emissions calculations and predictive analysis using artificial intelligence and machine learning. The platform highlights exceedances in real time, predicts future risk, and generates export-ready reports for regulatory agencies. This approach reduces manual labor, improves accuracy, and gives stakeholders immediate visibility into emissions performance.
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