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AI video social-listening platform that scans social video content to surface untagged brand mentions, sentiment, and creator leads.
Automated AWS usage optimization platform giving engineers 150+ recommendations across 50+ services, averaging ~10% savings.
AI coding platform pairing a browser IDE, multi-model chat and an AI website/app builder with GitHub sync and instant deploy.
Vibe-coding builder creating full-stack apps by chatting with AI.
Cloud-agnostic AI/ML workflow orchestrator that runs pipelines inside a customer's own infrastructure for compute-heavy teams.
No public pricing
Free trial available
- ✦AI video analysis of audio, visuals, and captions
- ✦Untagged brand mention and UGC detection
- ✦Creator sourcing by niche, audience, and brand fit
- ✦Outbound comment engagement tracking
- ✦Competitive benchmarking across brands
- ✦Brand sentiment and safety monitoring
- ✦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
- ✦Built-in AI IDE and code generator
- ✦Access to 15+ AI models in one platform
- ✦AI website/app builder from prompts
- ✦GitHub repository sync
- ✦Runs Python, React, Next.js and Node apps
- ✦Instant deploy to Vercel
- ✦Bring-your-own API keys for higher limits
- ✦CodeFlying enables full-stack app creation via chat in minutes
- ✦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
- →Finding untagged user-generated content about a brand
- →Sourcing creators that match specific audience criteria
- →Measuring campaign impact across social video
- →Benchmarking a brand's social presence against competitors
- →Cutting AWS spend automatically
- →Rightsizing over-provisioned resources
- →Scheduling idle resources off-hours
- →Giving DevOps in-console cost recommendations
- →Building full-stack web apps with AI
- →Generating landing pages and WordPress plugins
- →Iterating on a synced GitHub codebase
- →Prototyping app clones quickly
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- →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