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IBM's open, hybrid data lakehouse that connects, governs and optimizes enterprise data to make it AI-ready across clouds and on-premises.
Agentic AI platform with a coding desktop app, CLI, and cloud agents for autonomous software development and office work.
Big-data and AI platform (Foundry, Gotham, AIP) for integrating and analyzing large datasets across government and enterprise.
Talent marketplace linking AI labs with 257,000+ vetted data labelers and trainers for RLHF, red-teaming and evaluation.
No public pricing
Free trial available
No public pricing
No public pricing
Free trial available
No public pricing
- ✦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
- ✦Fast tensor operations
- ✦Differentiable tensors for gradient-based optimization
- ✦Network connectivity
- ✦Integration with Bun and Flashlight
- ✦Support for GPU computation with CUDA (Linux) and CPU computation (macOS)
- ✦Multi-agent collaboration for end-to-end tasks
- ✦Persistent memory and custom rules
- ✦Extensible skills and plugins
- ✦Rich context across code, images, and directories
- ✦Automatic codebase documentation generation
- ✦Terminal-native CLI and JetBrains IDE plugin
- ✦Cloud-hosted agents for enterprise use
- ✦Gotham platform for government/defense
- ✦Foundry enterprise data integration
- ✦AIP for operational AI and LLMs
- ✦Large-scale data integration and modeling
- ✦Analytics and decision support
- ✦Data security and governance
- ✦Network of 257,000+ pre-vetted AI data experts
- ✦AI-matched shortlists with skills tests and interviews
- ✦Bring talent into any annotation platform, no lock-in
- ✦Self-service or fully managed engagements
- ✦Job feed aggregating 20+ platforms for freelancers
- →Unifying fragmented enterprise data
- →Governing data for AI workloads
- →Moving AI pilots to production
- →Powering analytics with trusted data
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Autonomous feature development in large codebases
- →Terminal-based AI pair programming
- →Cross-department task automation for legal, finance, HR
- →Onboarding developers to unfamiliar codebases
- →Government, defense and intelligence analytics
- →Enterprise data integration
- →Operational decision-making
- →Deploying AI on proprietary data
- →Sourcing experts for RLHF and model evaluation
- →Staffing red-teaming and data-labeling projects
- →Scaling annotation teams into existing tools
- →Finding AI training gigs as a freelancer