ParityDeals powers purchasing power parity pricing for thousands of SaaS companies worldwide. When their infrastructure sneezes, thousands of checkout flows are affected, directly impacting customer revenue.

The problem: alert storms and war rooms

Before Redplum, ParityDeals’ on-call rotation was brutal. A single deployment could trigger hundreds of correlated alerts across microservices. Engineers would spend 45 minutes just triaging which alerts were real versus which were cascades from one root issue.

“We’d get paged at 2am with 200 alerts firing,” recalls James Liu, Staff SRE at ParityDeals. “By the time we figured out which one was the actual problem, we’d already called in half the engineering team.”

“Redplum reduced a 200-alert storm to a single root cause notification. Our on-call engineer got the RCA before anyone else even woke up.”

Rolling out Redplum

ParityDeals connected Redplum to their Datadog instance, GitHub, Kubernetes cluster, and Slack in under a day. The dynamic knowledge graph built itself from day one, mapping every service, dependency, and deployment pattern automatically.

The first real test came three days later when a deployment introduced a subtle connection pool misconfiguration. Redplum correlated the deploy event, identified the pool exhaustion, traced the dependency chain to downstream pricing endpoints, and posted the full root cause in Slack in 4 minutes and 11 seconds.

The results

Alert noise dropped by 94%. The team has had zero false major incidents. Average triage time went from 45 minutes to under 7 minutes. Engineer morale improved. On-call became sustainable again.

Kelviq builds the data infrastructure layer for enterprise analytics teams. Uptime directly translates to customer business outcomes.

The experience gap problem

Like many scaling companies, Kelviq had a mix of senior SREs who understood the system deeply and junior engineers who were still learning. Incidents that a senior could resolve in 20 minutes might take a junior 3 hours.

“We had a runbook gap we couldn’t close,” says Sandeep Kumar, VP Engineering at Kelviq. “Every time a senior engineer left, we lost institutional knowledge. Junior engineers were flying blind.”

“Redplum is essentially a senior SRE that never forgets. Every past incident, every runbook, every root cause. It has it all, and deploys that knowledge instantly.”

How Redplum changed the picture

Kelviq deployed Redplum across their full stack. Within 48 hours, Redplum had built a comprehensive knowledge graph. The first real test came during a complex Kafka consumer lag incident. A junior on-call engineer received Redplum’s investigation report before he had even opened his laptop. Total time from alert to resolution: 18 minutes. Previously this class of incident would have taken 4+ hours.

The results 90 days later

MTTR dropped by an average of 4 hours across all P1 incidents. Junior engineers are now as effective as seniors on first response. On-call experience moved from the bottom quartile to the top in engineer satisfaction surveys.

Hootz AI builds intelligent automation tools for enterprise workflows. As an AI-native company, they needed their own infrastructure to be bulletproof.

The startup SRE dilemma

Like most early-stage companies, Hootz AI could not afford to build a full SRE team. Their two senior engineers were also the people building the product. Asking them to be permanently on-call was a recipe for burnout.

“Redplum gave us the coverage of five SREs. We get paged when a decision needs to be made, not to figure out what’s happening. That’s Redplum’s job now.”

A single-day deployment

Hootz AI deployed Redplum on a Tuesday afternoon. By Wednesday morning it had mapped their entire microservices architecture and connected their Datadog dashboards. That Thursday, a memory leak in their inference service triggered a cascade. Redplum detected the anomaly, traced it to a specific endpoint, and flagged the PR that introduced the regression, all in under 6 minutes.

Scaling without scaling the team

Over the next quarter, Hootz AI scaled from 50,000 to 500,000 API requests daily. Their team size did not change. Their reliability improved. “We tell people we have a world-class SRE function,” says Maya. “What we have is Redplum and two great engineers.”