# Prilog.ai

> Prilog turns production issues into reviewed pull requests by connecting observability, code context, and human-approved remediation workflows.

## Core Pages

- [Home](https://prilog.ai/): Product overview, integrations, pricing, security, and trial CTA.
- [Blogs](https://prilog.ai/blogs/): Essays on production debugging, observability, incident remediation, and self-healing software.

## Posts

- [Your Engineers Should Be Building, Not Babysitting](https://prilog.ai/blogs/engineers-should-be-building-not-babysitting/): Engineering teams lose too much product time to on-call supervision, triage loops, repeated regressions, and work that agents can prepare in seconds.
- [From MTTR to Time-to-Reviewed-PR](https://prilog.ai/blogs/from-mttr-to-time-to-reviewed-pr/): For AI-assisted remediation, the most useful operational metric may be how quickly a production signal becomes an evidence-backed pull request ready for review.
- [Why AI-Generated Fixes Need Rollback Thinking](https://prilog.ai/blogs/why-ai-fixes-need-rollback-thinking/): AI-generated production fixes become safer when the workflow thinks about reversibility, blast radius, and review evidence before a patch is merged.
- [The Cost of Waiting on Production Bugs](https://prilog.ai/blogs/cost-of-waiting-on-production-bugs/): Production bugs get more expensive as context decays, support load compounds, releases freeze, and the fix becomes harder to review.
- [Stop Asking On-Call Engineers to Be Search Engines](https://prilog.ai/blogs/stop-asking-on-call-engineers-to-be-search-engines/): On-call work should start with a prepared incident brief, not a tired engineer manually stitching logs, traces, deploys, and code ownership together.
- [How to Use AI Remediation Without Losing Engineering Control](https://prilog.ai/blogs/ai-remediation-engineering-control/): A practical model for using AI to draft production fixes while keeping evidence, review, tests, and ownership in the hands of engineers.
- [From Production Issue to Reviewed Pull Request: A Better Remediation Loop](https://prilog.ai/blogs/production-issue-to-reviewed-pull-request/): A practical workflow for moving from noisy production alerts to code-level fixes that engineers can review, merge, and trust.
- [Why Observability Needs Code Context to Actually Fix Bugs](https://prilog.ai/blogs/observability-needs-code-context/): Observability tells teams what failed; code context explains where to fix it. Here is how to connect logs, traces, ownership, and pull requests.

## Contact

- Email: hello@prilog.ai