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GitHub-based engineering analytics that tracks contributions, automates performance reviews and adds gamification for dev teams.
AI codebase assistant that chats with your repos to search, debug, review PRs, and generate docs and unit tests.
IBM's open, hybrid data lakehouse that connects, governs and optimizes enterprise data to make it AI-ready across clouds and on-premises.
Long-standing provider of human-labeled, expert-validated training data and model evaluation services for building frontier AI.
No public pricing
Free trial available
No public pricing
Free trial available
No public pricing
- ✦Contribution and work-quality analytics
- ✦Automated, AI-powered performance reviews
- ✦Retrospective insights
- ✦Operational bottleneck alerts
- ✦Gamification with XP, levels and leaderboards
- ✦Uses Git metadata without accessing source code
- ✦Commits and Pull Requests Dashboard
- ✦Advanced Developer Skills Analysis
- ✦Strategic Investment Balance Monitoring
- ✦Collaborative Developers Map
- ✦Benchmarking Comparison with Other Teams
- ✦Smart Notifications
- ✦Chat with your repositories
- ✦Natural-language codebase search
- ✦Fast code indexing
- ✦AI pull-request and commit review
- ✦Automated documentation generation
- ✦AI unit-test generation
- ✦Open hybrid data lakehouse
- ✦Connects data across clouds and on-prem
- ✦Governance, lineage and access controls
- ✦Business-context enrichment
- ✦AI-ready data for analytics and models
- ✦Frontier alignment data (RLHF, SFT, red teaming)
- ✦Speech and audio data
- ✦Multimodal / VLM annotation
- ✦Physical AI data (LiDAR, robotics, sensor fusion)
- ✦Model integrity, bias and hallucination audits
- ✦1M+ vetted contributors, 500+ locales
- ✦SOC2 and ISO 27001 certified
- →Automating developer performance reviews
- →Spotting delivery bottlenecks
- →Generating retrospective insights
- →Motivating teams via gamification
- →Visualize historical graphs of code evolution
- →Assess development team performance using RSI and EMA
- →Understand developer skills and identify areas for improvement
- →Categorize commits by type (fixes, refactoring, etc.) to analyze investment balance
- →Identify individual and collective contributors within the team
- →Compare team performance with industry benchmarks
- →Receive weekly and monthly reports with AI-extracted insights
- →Onboard new developers to a codebase
- →Resolve bugs faster
- →Generate docs and tests automatically
- →Review pull requests with AI
- →Unifying fragmented enterprise data
- →Governing data for AI workloads
- →Moving AI pilots to production
- →Powering analytics with trusted data
- →Source training data for AI models
- →Evaluate and benchmark models
- →Annotate multimodal and sensor data
- →Run safety and bias audits