Why we built LogRite
Every org has a coding standard. Almost none has a logging standard.
That gap is where incidents drag on, audits surface violations no one caught, and the log you needed was never written. LogRite exists to close it, from your code to your AI.
The afterthought we set out to fix
Logging is the one engineering practice that has been neglected for decades. Developers add logs when something breaks, every team invents its own format, and nobody measures whether the logs that matter actually exist. Microservices made it worse: dozens of services each logging their own way.
The founding insight was simple: observability is garbage in, garbage out. Every monitoring platform assumes the logs flowing into it are good, and nobody ensures they are. So we built LogRite to close that gap before runtime — measuring log coverage (a metric no other tool computes) and generating the missing logs in your exact format, with compliance built in rather than bolted on.
Why our logs are worth reading
Context is what makes a log worth anything
A generic logger can tell you a function was entered or an error was thrown. It can't tell you which customer's payment was being authorized, which step of a workflow failed, or what transaction a request belonged to. LogRite reads the surrounding code at the code level and writes that meaning into every line, so when an incident hits, the log already explains what happened instead of leaving you to piece it together after the fact.
7% → 100%
Log coverage, measured and closed on a real repository
30–40%
Faster root-cause analysis
Before runtime
Logs written at the code level, not after the outage
Context-aware
Business meaning written into every line
Where bad logging costs you
The same gap shows up in three places, and all three are expensive.
Incidents
Developers guess where to log, so key operations stay invisible until production breaks. Then investigations crawl, because the context was never captured.
Compliance
PII leaks into logs in raw text. Card numbers, SSNs, and tokens sit in log stores, and the violation surfaces during the audit, not before it.
Drift
Even teams that clean up their logging watch the standard decay, with every push, every new hire, and every legacy system whose authors left years ago.
One platform, three teams
The same trustworthy logs serve everyone who depends on them.
Developers
Debug with logs that already explain what happened, in business terms, instead of reconstructing it after the outage.
Security & compliance
Hand auditors the logs instead of running a compliance project, with sensitive data masked before it is ever stored.
Executives
Turn every external call into a view of vendor spend, and see logging maturity as a metric instead of a guess.
The LogRite difference
One logging discipline applied in two places: to your code, generating the logs that should exist, and to your AI, governing the calls your systems make. Code logging is the established core; AI governance is the live, differentiated second engine. Together they close the complete logging loop.
Find missing logs
Spot a risky AI call
Generate the logs (PR)
Govern it in real time
Logs flow to prod
See what was sent
Refine templates
Tighten policy
Coverage holds
The gap shrinks
Engine 1 — Code Logging
Generate the logs your code is missing, across existing, new, and legacy code, enforced in CI/CD.
Engine 2 — AI Governance
AI Warden governs every routed AI call in real time, so nothing sensitive leaves unseen.
Unified observability
Discover, dashboards, anomaly detection, and alerting in one platform.
AI-powered investigation
Cortex AI is designed to bring semantic search and root-cause analysis to incidents.