DevLoops
Codebase comprehension infrastructure for AI coding agents
THE PROBLEM
AI coding agents are amnesiac. Every session starts from zero. Every prompt re-reads the codebase. Context rot, hallucinated APIs, and brittle answers are the ceiling on agent reliability — and the bottleneck on adoption.
THE FIX
DevLoops ingests git history into a persistent skill graph — descriptions of what code achieves, stored in dedicated Redis Cloud per customer, queried via vector search. Agents call our API, get grounded answers in cents, never re-read the codebase.
Why this wins
Infrastructure, not application
We don't compete with Claude Code, Cursor, or spec engines. We're the comprehension layer they all integrate.
Defensible architecture
Skill formation, AST-anchored evidence, multi-tenant Redis isolation. Months of engineering, hard to replicate.
Real economics
Standard mode: ~$0.03. Deep security reviews: ~$0.15. Margin room for pass-through pricing to dev tool partners.
Redis Cloud
Strong partnership. Provisioning, billing, isolation handled. Distribution channel into existing Redis customers.
Why now
Agentic coding tools just hit the codebase comprehension wall. Long-context models help but lose focus in large codebases. The market needs a persistent memory layer — exactly when frontier models got smart enough to populate it. Window is open.
THE THESIS
Every AI coding tool needs codebase comprehension. Most will rent it rather than build it. DevLoops is that rental layer — and it makes Claude meaningfully better at the work that matters most to Anthropic's roadmap.