CodeMinder turns fragmented engineering signals into prioritized, evidence-backed actions. Its Context Engine connects your logs, security events and scanner output to business impact, so leadership decides from evidence, not from whichever alert was loudest.
Logs, security events, scanner output and cost data live in different tools, and AI-written code adds more every week. Each tool sees its own slice. Deciding what deserves engineering attention is still done by hand.
Every tool sees one fragment. Nobody connects them to each other, or to what the business actually cares about.
Database pressure, recurring costs, coverage gaps. They build slowly and never cross an alert threshold. CodeMinder tracks how they grow.
Leaders decide what to fix through triage, escalations and instinct. Quiet, expensive issues never make the list.
No agents to install. No data to move. Upload files to start, or have your IT team share a read-only S3 bucket.
Upload files or share a read-only S3 bucket. Your data stays in your account.
The Context Engine classifies each signal, merges duplicates and recurring issues, and connects related events. PII is masked before analysis begins.
Every finding is scored on six dimensions (SIGNAL) and ranked into a short weekly list, with the evidence behind each item.
Push findings to Jira or Linear, or export them. Next: hand them directly to AI coding agents.
We ran CodeMinder on our own production stack and found 62% noise, a silent database choke, and exposed AWS credentials, all from application logs alone.
We're working with a small number of engineering organizations to shape CodeMinder. Design partners get early access, a direct line to the founder, and a say in what we build next.
Accepting a small number of design partners.
Become a Design Partner →Share your application logs, WAF events or scanner output. We'll show you what to fix first, and why. No long setup. No commitment.