AI on-call that triages every production alert, opens fix PRs at calibrated confidence, and proves it is working.
Production alert fatigue burns out on-call engineers, and most AI fix tools close the loop without proving it, leaving teams to pattern-match away bandaid PRs. They triage by vendor severity tag rather than real customer impact, react only after an alert fires, and lock regulated teams out with SaaS-only delivery.
Noiselytics is an AI-driven on-call platform that turns production noise into measurable signal. It connects to Sentry, Datadog, and GitHub in about two minutes with no SDK or code changes, then triages every alert for severity, root cause, git blame, deploy correlation, and users affected. From there it stays investigate-only, proposes draft fix PRs, or merges autonomously per repo behind confidence gates you control. Built-in analytics on rule health, drift, and SLO burn-rate prove the system is earning its keep.
Rule-health, drift detection, SLO burn-rate, and per-repo confidence dashboards show the noise reduction instead of asking you to take it on faith.
Three tiers of investigate, propose, and autonomous run per repo and per error class behind confidence gates, so automation only merges when the codebase has earned it.
Run the same engine inside your VPC with a Helm chart, FIPS posture, customer-managed keys, and bring-your-own-LLM, with no fork and no vendor lock-in.
A PostHog joiner shows distinct users hit by each alert so the queue is re-ranked by who is affected, not by vendor severity tags.
Every alert gets dedupe, severity, root cause, git blame, and deploy correlation. Semantic and stack-trace clustering collapse duplicates into single incidents.
High-confidence triages open a draft pull request with the fix, reasoning, and a test. PRs are deduplicated per alert so a recurring issue never spams new ones.
Opt a repo in and approved PRs are marked ready-for-review with a label and a comment listing every gate they cleared. Your branch-protection rules do the actual merge.
Track noisy and dead rules, on-call burden, MTTD and MTTR trends, and the true cost of an alert in engineer-minutes plus agent tokens.
Deploy correlation auto-flags the commit that broke prod, log-embedding drift surfaces emerging issues before they alert, and SLO burn-rate forecasts when budget runs out.
Incident timelines and Slack threads are turned into draft postmortems, and ingested runbooks make the agent smarter on your specific systems.
Bidirectional GitHub Issues sync opens a tracker issue with the triage summary, and issue close, reopen, or delete keeps the alert state in lockstep.
Three roles of owner, admin, and member with every state change written to an immutable audit log for a SOC 2 and SOX-ready posture.
Paste a webhook URL into Sentry, Datadog, or GitHub in about two minutes, with no SDK changes and no code instrumentation.
Every alert runs through dedupe, severity, root cause, who introduced it, and how many users it hit via PostHog.
The agent posts a diagnosis, opens a draft PR in propose mode, or clears autonomous gates per repo when the codebase has earned trust.
Rule-health, drift detection, SLO burn-rate, and confidence dashboards let you tune the noise out and prove the system works.
No. You connect a webhook from Sentry, Datadog, or GitHub in about two minutes, and it sits on top of what your team already runs with no SDK and no instrumentation.
Not on its own. It defaults to investigate-only, propose mode opens draft PRs, and autonomous mode is opt-in per repo behind confidence gates. Even then your own branch-protection settings perform the merge, never Noiselytics directly.
Yes. The same engine self-hosts via Docker Compose or Helm with FIPS posture and customer-managed keys, and you can bring your own LLM across Anthropic, OpenAI, Bedrock, Vertex, Azure, or Ollama.
Reach out for early access, a live demo, or a partnership conversation.