Reduce engineering toil: prompt-driven code management, debug & context

Reduce engineering toil: prompt-driven code management, debug & context targets a $72.0B = 25M developers x $2,880 ARPU (annual spend on dev tools, platforms, AI assistants) total addressable market with medium saturation and a year-over-year growth rate of 15-20% projected annual growth for dev tools and AI-assisted developer tooling.

Key trends driving demand: Large language models for code -- LLMs now provide usable code reasoning, enabling automated debugging and context-aware suggestions; Shift to remote/distributed engineering -- distributed teams increase demand for automated onboarding, context transfer and centralized code intelligence; Platformization of dev workflows -- companies prefer integrated tools that connect CI/CD, issue trackers and observability, enabling richer automation; Increasing dependency complexity -- monorepos and multi-package apps drive need for tooling that traces and contextualizes failures across stacks.