Every other reliability tool waits for the outage. Revelara surfaces risks in the AI-generated code your team is shipping, so you spend less time firefighting and more time on the engineering work you actually want to do.
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Most engineering teams don't have a dedicated SRE. Developers own reliability themselves, but without the frameworks or incident knowledge to do it well. Revelara changes that.
Every AI SRE tool on the market helps after something breaks. Revelara is pre-incident: it surfaces reliability risk before code ever reaches production.
Revelara doesn't just flag risks. It explains the "why" behind every recommendation, grounded in real incidents. You become a better engineer as a side effect of using it.
Auto-generated evidence, controls mapped to services, and a compliance matrix that stays current. Show your board, or your customers, that reliability is governed.
Revelara connects your incidents, architecture, and team knowledge into a continuous reliability loop.
Install the CLI in 30 seconds. Revelara scans your codebase, maps risks, and delivers findings as slash commands right in your coding agent. No context switching, no dashboard to check.
/risks – See open risks for the current project/scan – Run a risk scan on your codebase/fix – Get remediation guidance for a specific risk/review – Review code changes for reliability risksRevelara matches your code against patterns from 2,000+ real-world incidents. Every recommendation shows its reasoning: what pattern was matched, what went wrong, and why it matters for your service.
Revelara continuously analyzes your reliability risks, classified by category, scored by severity, and linked to the services they affect. No more guessing what to prioritize.
Revelara turns risk findings into sprint-ready actions with embedded remediation controls. Approve, defer, or route to the right team, all from one place.
See every control mapped to every service: what's implemented, what's partial, and where the gaps are. Always audit-ready, always up to date.
Posts on reliability work, the AI-generated code teams are shipping, and what the public postmortem corpus actually shows.
Two GitHub incidents 74 minutes apart, same underlying cause. A reading of the public report through two lenses, one heuristic and one systems-theoretic.
Across nearly 3,000 public postmortems, medium-severity incidents consume roughly thirteen times more engineering time than critical ones. The implication is upstream.
The DORA 2026 ROI report names the productivity dip of AI adoption and prices it. The verification tax it names is the same cost vibe coding pushes onto a tired reviewer.
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