Features
RL-trained code search subagent
Isolated context window for search results
~3.8 steps average to find code
8 parallel tool calls per turn
Reduces context rot by 70%
Speeds coding tasks by 40%
MCP integration for Claude Code, Codex, OpenCode
OpenAI-compatible SDK (TypeScript, Python)
Auto-detects and configures editors (Claude Code, Cursor, Codex, VS Code)
One-command setup via npx
Search across entire codebase with natural language
Zero search reruns due to context overflow
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
