Adrenaline is a groundbreaking AI-powered debugger developed by GetAdrenaline in partnership with 7Code. Leveraging the capabilities of OpenAI Codex, Adrenaline is designed to make the debugging process faster and more efficient, diagnosing and fixing code issues in seconds, and transforming how developers interact with their codebases.
The challenge: debugging at the speed of development
Modern software teams lose enormous amounts of time to debugging cycles that haven't fundamentally changed in decades. GetAdrenaline came to us with a clear vision: an AI debugger that could import a codebase from GitHub, analyse it in context, and surface explanations and fixes instantly, without requiring the developer to context-switch into a separate tool.
What we built
- A repository ingestion pipeline that chunks code semantically for accurate, context-aware retrieval
- An LLM orchestration layer using OpenAI Codex tuned for symbol-aware lookups across large codebases
- An AWS deployment with autoscaling, per-tenant rate limits, and cost guardrails approved by finance
- An eval harness running against real bug reports, not synthetic queries, to measure quality continuously
Seamless developer workflow integration
Adrenaline integrates directly into a developer's workflow: import your repository from GitHub, ask a question or describe a bug, and receive an instant, contextual explanation and proposed fix. The system drastically reduces debugging time and enhances overall productivity, not by replacing developer judgement, but by accelerating it.
AI products win or lose on retrieval quality. The model is a commodity; the context you put in front of it is the moat.
From MVP to AWS-scale production
We helped Adrenaline go from a working prototype to a production deployment capable of handling Product Hunt traffic. Naive embedding-based retrieval worked on a 10,000-line repo and fell apart on a 1-million-line one, so we invested early in symbol-aware chunking, hierarchical retrieval, and a caching layer keyed to commit hashes, delivering sub-second retrieval on real-world repositories.
Results
- MVP shipped to Product Hunt in 8 weeks
- Indexing throughput improved 12× over the prototype baseline
- Per-query cost reduced by 60% via retrieval caching
- Zero-downtime deploys on AWS with finance-approved cost guardrails