Learn how to integrate BugFlows directly into your workflows! Our new API guide provides step-by-step instructions for training models and making predictions via API. Perfect for developers looking to automate and customize their defect management process.
Bugflows is an AI/ML layer that auto-predicts bug fields like assignee, priority, and resolution based on your historical data—no setup needed. Upload a CSV, and within seconds, get accurate predictions to eliminate triage delays.
BugFlows predicts the right assignee for each issue using historical data — with 86% accuracy. No more guesswork, delays, or uneven workloads. Faster resolutions, fairer distribution, smarter engineering.
Misassigned bugs cost enterprises millions annually in delays and developer time. BugFlows uses AI/ML to predict the right assignee, reducing triage time by up to 70% and saving $100K+/year.
Predictive QA transforms historical bug data into a powerful asset for preventing defects, optimizing workflows, and reducing costs. Bugflows helps engineering teams move from reactive fixes to AI/ML-driven insights, boosting speed, accuracy, and ROI.
Fortune 500 companies can cut bug resolution time by up to 40% using ML-powered tracking that auto-assigns issues, prioritizes critical bugs, and forecasts fix times. Bugflows enables this transformation in under two weeks — no data science team required.
Traditional defect management systems can’t keep up with modern software complexity, leading to misassignments, delays, and developer burnout. AI/ML-powered tools like Bugflows fix this by predicting assignees, prioritizing issues, and accelerating resolution with real-time intelligence.
Use machine learning to predict the right developer for every bug using historical Jira data. This reduces triage time, improves resolution speed, and boosts team productivity with minimal setup.
Bugflows lets you train AI/ML models on sensitive bug data without compromising privacy, control, or compliance. Deploy securely on-prem or in your VPC, with zero data storage, federated learning, and full explainability.
Learn how to build a FastAPI + XGBoost microservice that predicts bug resolution time using Jira-like data. This end-to-end guide helps you train, deploy, and test a real-time ML model for smarter sprint planning.
Integrate Bugflows' ML models into your CI/CD pipeline to predict bug risks, resolution time, and assignment complexity before merging. With GitHub Actions and minimal code, you get real-time QA insights directly in your pull requests.
A 25-person dev team cut their bug backlog by 60% using Bugflows' AI/ML for automated triage, smart assignment, and resolution forecasting. The result: faster delivery, improved team morale, and sustained operational efficiency.