Bilanc is a platform that leverages AI to measure and enhance engineering productivity through detailed insights and reports.
Features
AI Impact Measurement
Quantifies the impact of AI on engineering productivity by calculating adoption rates, productivity boost percentages, and evaluating the effectiveness of different models for various tasks.
Pre-built Reports
Provides context-rich reports with metrics benchmarked against industry peers, including DORA and AI-powered metrics to estimate the effort of code, facilitating a shift from measuring mere activity to true productivity.
Engineering Resource Allocation
Uses AI to automatically categorize tasks and provide a detailed breakdown of how developers are allocating their time, eliminating the need for manual tracking and setup.
Centralized Data Integration
Integrates with tools like GitHub, GitLab, JIRA, and more, centralizing data from various developer tools for a comprehensive analysis.
Scheduled AI Summaries
Allows scheduling of AI-generated summary reports for individuals or teams, useful for meetings and updates, ensuring everyone stays informed about productivity and task allocation.
SOC 2 and GDPR compliance
Bilanc is undergoing a SOC 2 Type II audit and complies with GDPR, ensuring high standards of data security and privacy.
Data Encryption
All customer data in Bilanc is encrypted at rest with AES-256 and secured in transit via TLS, providing robust protection for user data.
Vulnerability Management
Bilanc conducts regular penetration testing and continuous vulnerability scanning to identify and mitigate potential security threats.
Incident Response
Security incidents are prioritized and managed through a structured process to ensure swift resolution and minimal impact.
Single Sign-On
Bilanc supports Single Sign-On with providers like Google, Microsoft, and Okta, facilitating seamless and secure access for users.
Employee Security Training
Bilanc mandates annual security awareness training for all employees, conducted by an external provider to maintain high security standards.
PR Effort Estimation
Uses LLMs to estimate the effort required for each merged PR, providing metrics such as Cycle Time and a productivity score between 0 and 100.
PR Metadata Indexing
Runs workflows to index and analyze PRs, retrieving relevant code and external task information from tools like JIRA & Linear.
Contextual Analysis Agents
Uses various agents to summarize PRs, categorize tags, identify risks, and estimate effort, all of which contribute to an overall productivity score.
LLM-based Validation
Employs LLMs for evaluation and validation of productivity results, with manual annotations to ensure accuracy.