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Automated AWS usage optimization platform giving engineers 150+ recommendations across 50+ services, averaging ~10% savings.
Local deep packet inspection and network intelligence giving businesses full visibility into application, VPN and Tor traffic.
Cloud-agnostic AI/ML workflow orchestrator that runs pipelines inside a customer's own infrastructure for compute-heavy teams.
One-click bug-reporting tool that auto-captures console, network logs and repro steps for developers.
AI QA agent that writes and runs API, UI, and OWASP security tests automatically on every pull request.
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- ✦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
- ✦Local deep packet inspection agent
- ✦Application identification data feeds
- ✦VPN and Tor IP datasets
- ✦Network informatics and analytics
- ✦Developer documentation
- ✦Python-native dynamic workflow authoring
- ✦Automatic failure recovery, caching, and versioning
- ✦Zero Trust architecture keeping data inside customer's cloud
- ✦Real-time inference and agentic-AI workflow support
- ✦High-throughput scaling (tens of thousands of actions per run)
- ✦Local development environment matching production behavior
- ✦One-click bug capture via browser extension
- ✦Automatic repro steps
- ✦Console, network and device logs
- ✦Instant replay of recent activity
- ✦Backend tracing and an AI debugger
- ✦Integrations with Jira, Linear, GitHub and Slack
- ✦AI-generated and self-maintaining test scenarios
- ✦API and UI test runs on every pull request
- ✦OWASP-aligned security probes
- ✦Import from OpenAPI, Postman, and spreadsheets
- ✦Shared, versioned scenario library
- ✦Velocity analytics on review time and coverage
- ✦CI/CD, Slack, Jira, and Cursor integrations
- →Cutting AWS spend automatically
- →Rightsizing over-provisioned resources
- →Scheduling idle resources off-hours
- →Giving DevOps in-console cost recommendations
- →Classify application traffic on a network
- →Detect VPN and Tor usage
- →Feed DPI data into security and analytics tools
- →ML teams orchestrating training and inference pipelines at scale
- →Biotech/geospatial companies needing GPU-heavy pipeline orchestration
- →Enterprises migrating off Airflow for ML workflow management
- →Teams requiring workflows that never send data outside their own cloud
- →Filing detailed bug reports
- →Reproducing issues faster in QA
- →Sharing debug context with engineers
- →Triaging support bug reports
- →Automating regression testing for API changes
- →Catching security issues before merge
- →Replacing spreadsheet-based test tracking
- →Continuous QA for fast-shipping engineering teams