Hireology builds safe, scalable AI features.
In less than 13 seconds, I can test 3 verticals, 10 tests each with LaunchDarkly. In the time it takes to generate one job description, I’ve tested all iterations programmatically.
Introducing Release Autopilot: Automated release control for AI-built code — Read the announcement
Development teams
Control how every change reaches users, measure its impact, and roll it back without redeploying when something breaks. Run releases yourself, or automate the work with Release Autopilot.

Ship
Release changes behind guardrails, detect issues early, and automatically stop unsafe behavior before it impacts users.
Push code continuously. Release Autopilot automatically flags changes that need safeguards and manages rollouts within policies you control.
Detect regressions at the first sign of degradation using real production signals.
Trigger rollback or disable behavior instantly when thresholds are crossed—no redeploy or manual intervention required.

Track behavior at the change level using logs, traces, and real-time context.
Understand what broke and why using change-level correlation and real production data.
Skip manual investigation and go straight from detection to resolution with change-level insight.
See how behavior shifts in production, identify issues instantly, and pinpoint the root cause.
When something breaks, roll back changes, adjust behavior, or reroute traffic instantly without manual intervention.

Roll back changes, disable features, or reroute code or agent behavior instantly when problems occur.
Detect and remediate issues as soon as they appear, before they impact users broadly.
Revert to a previous known-good state instantly—no redeploy required.
Automate flagging, rollouts, rollbacks, and cleanup for every change that needs it, with no extra work for your team.
Explore Release Autopilot
Learn
Use real production data to test changes, promote what works, and apply fixes faster—without slowing down delivery.

Run experiments in production and measure real-world impact instead of assumptions.
Shift traffic to the best-performing variation based on real data.
Use change-level insight to understand issues and generate or suggest fixes you can act on immediately.