Insurance Tech Platform, a leading insurance and CRM platform used by Fortune 500 insurers, deployed Applicare across their multi-cloud infrastructure to correlate platform performance with customer business outcomes for the first time.
Insurance Tech Platform operates a multi-tenant SaaS platform serving insurance carriers across 14 countries. Their engineering team had strong infrastructure monitoring — they knew when a server was overloaded. What they lacked was the connection between infrastructure performance and what it meant for their customers' policy workflows, claims processing, and agent productivity.
A 400ms API slowdown wasn't just a metric — it was a claims adjuster unable to close a case before end of day. A database connection pool exhaustion wasn't just an alert — it was a policy renewal workflow failing silently for 200 agents. The team needed observability that understood business context, not just system metrics.
Insurance Tech Platform’s largest customer was processing 12,000 policy renewals per day through the platform. A 2-hour performance degradation was silently failing 800 renewals — discovered only when the customer called. The team had no way to see the business impact of infrastructure events.
Applicare deployed across Insurance Tech Platform's AWS and Azure multi-tenant infrastructure, building an entity graph that included not just servers and services, but business process entities — policy workflow queues, claims processing pipelines, and agent session health as first-class monitored objects.
Every infrastructure event was now correlated with its downstream business impact. A database connection pool saturation didn't just trigger an infrastructure alert — it showed which tenant's workflow queues were backing up, how many transactions were at risk, and the estimated business impact in real time.
Multi-tenant platforms create a particularly difficult root cause problem: an incident affecting one tenant might share a database cluster, message queue, or API gateway with 50 others. Traditional monitoring can't distinguish whether a slowdown is caused by a noisy tenant, a platform bug, or a shared infrastructure constraint.
ArcIn's entity graph models tenant isolation explicitly. When Insurance Tech Platform's on-call team asked "why is Tenant 23's claims API slow?", ArcIn traversed the graph and identified that the shared document storage service was being saturated by a batch export job running on Tenant 07's account — and immediately isolated the two workloads.
For the first time, we could tell a customer exactly why their platform was slow, what we were doing about it, and what their business impact was. That conversation went from defensive to confident overnight.
With business process entities in the graph, Insurance Tech Platform could for the first time quantify the business value of their reliability improvements. The 67% reduction in P1 incidents, combined with the 3.1h → 22min MTTR improvement, translated to a measurable reduction in failed policy transactions and claims processing delays.