Features
Autonomous ML engineering from single task prompt
Multi-step reasoning with self-correction and iteration
LLM evaluation and benchmarking across models
Dual-LLM automatic prompt optimization loop
Fine-tuning of LLMs and ML models
RAG pipeline building and optimization
Agent swarm creation and coordination
Integration with VS Code, Cursor, Claude Code, OpenVSX
Bring your own LLM (BYOK) support
GPU sandbox for running experiments on your compute
Versioned artifact management and reporting
Synthetic data generation and dataset engineering
Lite mode and Pro mode for different compute profiles
Credit-based usage metering (per month)
Can run autonomously for days
Live knowledge graph from code, commits, issues, and docs
Feasibility analysis: flags buildable vs risky items
Technical design document generation grounded in service topology
Impact assessment maps services, APIs, and dependencies across repos
Auto-scoping epics into Jira/Linear stories with effort estimates
One-shot production code generation grounded in service patterns
AI code reviews with cross-repo impact analysis
Production issue triage via MCP
Conversational learning from Slack and Jira
Accelerated onboarding via system-level Q&A in coding agents
Create Jira tickets and merge requests from Slack
MCP server for integration with Cursor, Claude Code, Codex
On-prem or cloud deployment
No code storage or model training
