Most engineering organizations have more observability tooling than ever — and slower incident response than they should. SwiftCatch finds the operational gaps your dashboards are hiding, then closes them.
Most engineering teams have Datadog, PagerDuty, Grafana, and a Confluence full of runbooks. None of it is the problem. The problem is that nobody is measuring what matters: how fast problems are detected, how consistently runbooks are followed, and what the SLO drift is actually costing in customer impact.
On-call engineers spend 60–70% of their incident time on triage — not remediation. Alert volumes are high, signal is low. The same incident patterns recur because postmortems produce action items that never get automated. The gap between what your observability stack shows and what your org actually does with that information is where the leakage lives.
SwiftCatch assesses that gap, quantifies it in hours and dollars, and deploys the automation that closes it.
Conservative numbers for a team running 8 engineers with 3 on a shared on-call rotation. Run your exact numbers on an assessment call.
Every implementation starts from the assessment findings. We only build what your scorecard says will generate the highest return.
Pattern-based anomaly detection on your metrics and log streams — catching incidents 30–65 minutes earlier than threshold-based alerting. Fewer customer-reported outages, more engineer-caught ones.
Burn-rate alerting and error budget dashboards that make SLO drift visible before it becomes a customer SLA conversation. Executives and on-call engineers see the same numbers, in real time.
The 20% of failure patterns that cause 80% of your MTTR get automated. Restart sequences, scaling responses, cache flushes, dependency checks — all triggered automatically on detection, with full audit trail.
Alert correlation and deduplication that reduces page volume without reducing coverage. Engineers get paged on things that need humans — not on conditions that resolve themselves in 60 seconds.
Unified metrics, traces, and logs across your stack — with routing, transformation, and cost optimization built in. The right data reaching the right destination without the cardinality bill.
LLM-assisted first-response that classifies incoming incidents, surfaces the relevant runbook, identifies probable blast radius, and drafts the initial status update — before the on-call engineer opens their laptop.
Before we recommend anything, we diagnose. The Engineering Operational Maturity Assessment scores your org across all six pillars with engineering-specific benchmarks — MTTD, MTTR, SLO adherence, alert signal-to-noise, runbook coverage, and AI readiness.
You receive a scored report with dollar-impact analysis for every gap, benchmarked against engineering orgs at your stage and scale, plus a prioritized roadmap ranked by engineering hours saved and customer impact prevented.
Full 6-pillar assessment, written report, 60-min session. Credited toward any implementation.
Includes MTTD/MTTR benchmarking, observability audit, alert analysis, SLO review, runbook coverage assessment, and AI ops readiness. Scope and price set after discovery call.
Full implementation of highest-ROI findings from the assessment. Retainer available for ongoing optimization and monitoring.
The same process runs every engineering engagement. Results are tied to the baseline the assessment establishes — so improvement is measurable, not anecdotal.
We run the full Operational Maturity Assessment with engineering-specific benchmarking. MTTD, MTTR, alert analysis, SLO review, runbook coverage, observability audit. You receive a scored report before we propose anything.
Every finding gets a dollar number — engineering hours wasted, customer impact risk, SLA exposure. The roadmap is ranked by return, not complexity. You decide what to tackle first.
We build and deploy the systems that close your highest-impact gaps — anomaly detection, runbook automation, SLO dashboards, on-call intelligence. Most implementations go live within two weeks. Results measured monthly against the assessment baseline.
Our on-call rotation was unsustainable — engineers averaging four interruptions a night on P3s that auto-resolved. The assessment scored us 18 on Automation and identified the exact runbooks that needed to exist. Six weeks after implementation we were under one page per engineer per week. The team morale difference is hard to overstate.
We had 99.9% SLO targets that nobody was actually measuring against. The assessment called it out immediately as a Visibility gap — and showed us the customer impact risk we were carrying blind. Three months later we have proper error budget dashboards and we caught two breaches before customers noticed. That's a first for us.
Book a 30-minute call. We'll walk through your current MTTD, MTTR, and alert landscape — and show you what the gaps are costing before we propose anything.