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Jupyter-native AI agent that remembers a data project across sessions and reads chart/plot outputs, not just code.
AI codebase assistant that chats with your repos to search, debug, review PRs, and generate docs and unit tests.
Agentic AI platform ('Aiden') that automates incident response, infrastructure-as-code and observability tasks with policy-based governance.
AI API regression-testing solution for streamlined QA and faster releases.
Automated AWS usage optimization platform giving engineers 150+ recommendations across 50+ services, averaging ~10% savings.
No public pricing
No public pricing
Free trial available
No public pricing
No public pricing
Free trial available
- ✦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
- ✦Chat with your repositories
- ✦Natural-language codebase search
- ✦Fast code indexing
- ✦AI pull-request and commit review
- ✦Automated documentation generation
- ✦AI unit-test generation
- ✦Automated service discovery and dependency topology mapping
- ✦SLO-based alert triage and prioritization
- ✦AI-driven root cause analysis with pre-built workflows
- ✦Human-approved remediation with full audit trails
- ✦Works alongside existing tools like Datadog, Grafana, New Relic
- ✦Governance and policy enforcement layer for agent actions
- ✦AI-Powered Test Case Generation
- ✦Automated API Regression Testing
- ✦Collaborative Bug Tracking
- ✦Integrations with Project Management and CI/CD Tools
- ✦Effortless API Documentation
- ✦Unified Platform for Different Testing Phases
- ✦150+ recommendations across 50+ AWS services
- ✦Zombie and unused resource cleanup
- ✦Over-provisioned rightsizing
- ✦Idle-resource scheduler
- ✦SpotBot for ECS Fargate spot/on-demand switching
- ✦AWS console extension with Slack/Teams alerts
- →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
- →Onboard new developers to a codebase
- →Resolve bugs faster
- →Generate docs and tests automatically
- →Review pull requests with AI
- →SRE teams reducing mean-time-to-resolution during incidents
- →Platform engineers wanting policy-governed AI infrastructure management
- →Enterprises needing SOC 2 / PCI / HIPAA-compliant AI operations
- →AI-Powered Test Case Generation: Devzery’s AI engine analyzes your PRD or project details to generate API test cases.
- →Test Suite & HTTP Coverage Metrics: Track test coverage and HTTP distribution metrics to ensure all critical aspects of your APIs are covered.
- →Precise Bug Tracking with AI: Automatically generate detailed bug reports with status, related test cases, expected vs. actual results, and error descriptions.
- →Cutting AWS spend automatically
- →Rightsizing over-provisioned resources
- →Scheduling idle resources off-hours
- →Giving DevOps in-console cost recommendations