Adrenaline lets developers chat with their codebase: import a repository, ask a question, get an explanation or a fix. Behind the chat sits an LLM-powered indexing and retrieval system tuned for code. We helped the Adrenaline team build it from MVP through AWS-scale production, and the lessons apply to any AI product that has to reason over large structured corpora.
The brief
Adrenaline came to us with a strong product idea, a working prototype, and the kind of growth pressure that breaks prototypes. They needed three things: an MVP architecture that could survive Product Hunt, an indexing pipeline that scaled to large repositories, and a deployment story that didn't require a platform team to maintain.
What we built
- A repository ingestion pipeline that chunks code semantically, not by line count
- A retrieval layer tuned for symbol-aware lookups, not just embedding similarity
- An LLM orchestration layer that routed questions to the right context window strategy
- An AWS deployment with autoscaling, cost guardrails, and per-tenant rate limits
- An eval harness that ran against real bug reports, not synthetic queries
The hard part: scaling indexing
Naive embedding-based retrieval works on a 10,000-line repo and falls apart on a 1-million-line one. We invested early in symbol-aware chunking, hierarchical retrieval, and a caching layer keyed to commit hashes. The result was sub-second retrieval on real-world repositories, the difference between a usable product and a science project.
AI products win or lose on retrieval. The model is a commodity; the context you put in front of it is the moat.
What changed in production
Three things we learned only after real users arrived. Repository imports are bursty, autoscaling on GPU-backed services is non-negotiable. Cost-per-query varies wildly by repo size, we added per-tenant token budgets early. Users ask follow-up questions far more than first questions, caching the retrieval, not just the embedding, was the single largest latency win.
The result
- MVP shipped to Product Hunt in 8 weeks
- Indexing throughput improved 12× from the prototype baseline
- Per-query cost reduced by 60% via retrieval caching
- Zero-downtime deploys on AWS, with cost guardrails approved by finance
Adrenaline is one of the cleanest examples we've shipped of an AI product earning its place in a developer's daily workflow. The architecture choices we made in the first six weeks are the ones still paying off today.