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Jupyter-native AI agent that remembers a data project across sessions and reads chart/plot outputs, not just code.
Self-hosted cloud development environments and AI-agent governance, letting enterprises run coding agents on their own infrastructure.
AI app builder that turns chat prompts into working web apps and sites, with credit-based build and deploy.
Test management platform unifying manual and automated test results with AI-assisted case generation, for scaling QA teams.
No public pricing
No public pricing
Free trial available
No public pricing
Free trial available
- ✦CSV to API conversion
- ✦Data parsing (CSV to JSON)
- ✦Filtering capabilities
- ✦Cross-session project memory recalling prior decisions and state
- ✦Autonomous execution of long, multi-step notebook tasks
- ✦Reads cell outputs (plots, tables, metrics), not just code
- ✦In-notebook cell-level assistance and error fixing
- ✦Installs directly into existing JupyterLab via pip, no new editor
- ✦Concept explanations with runnable example cells
- ✦Self-hosted workspaces with desktop and web IDEs
- ✦Coder Agents run coding agents on isolated infrastructure
- ✦AI Governance gateway for LLM usage control
- ✦SSO (OpenID Connect) and role/group sync
- ✦Audit logging and resource quotas
- ✦Multi-organization access controls
- ✦High availability and workspace proxies
- ✦Chat-to-app and website generation
- ✦Real-time prototype building
- ✦One-click deploy and hosting
- ✦Templates to start projects
- ✦Credit-based building with shared workspaces
- ✦You own your code and data
- ✦Central test case repository with reporting dashboards
- ✦AI conversion of manual test cases into automated test scripts
- ✦CI/CD-connected automated test orchestration
- ✦Requirements-to-test traceability reporting
- ✦MCP server for connecting AI agents to test data
- ✦20+ integrations including Jira, GitHub, and Slack
- →Sharing CSV data with a team via an API
- →Creating a public API from CSV data
- →Filtering and accessing specific data within a CSV file programmatically
- →Data scientists running multi-week model iteration projects
- →Domain experts (e.g. risk/fintech) who know the problem but not deep Python
- →Researchers wanting an agent that remembers project context across days
- →Analysts needing help understanding unfamiliar algorithms or libraries
- →Standardize developer environments
- →Run AI coding agents securely on-prem
- →Enforce governance and compliance
- →Cut VDI costs
- →Speed up developer onboarding
- →Build web apps without coding
- →Prototype product ideas quickly
- →Create landing pages and sites
- →Ship internal tools
- →QA teams consolidating scattered CI, manual, and automated results
- →Engineering orgs converting manual test backlogs into automation
- →Enterprises needing audit-ready traceability for regulated software
