Outcomes that redefine what's possible.

The challenge

A mid-market fintech with $4B AUM was running a legacy monolithic deployment pipeline that produced 3-week release cycles, frequent production incidents, and an engineering team spending 40% of their time on deployment-related firefighting.

Our approach

Convergile.AI redesigned the CI/CD architecture end-to-end — introducing AI-assisted canary analysis, automated rollback logic, and a self-service deployment platform for engineering teams. We also implemented predictive failure detection trained on 18 months of deployment history.

The challenge

A healthcare SaaS company processing 2M+ patient records daily had manual IT operations consuming 60% of engineering bandwidth. Alert fatigue was severe — over 800 alerts per day with a 12% actionability rate — leaving no capacity for product development.

Our approach

We deployed a full AIOps layer across their infrastructure stack — implementing intelligent event correlation, automated triage workflows, and self-healing runbooks for the 40 most common failure modes. We also redesigned their observability architecture with unified telemetry and AI-powered anomaly detection.

The challenge

A B2B SaaS company with 300+ enterprise customers had cloud infrastructure costs growing at 2.4× their revenue growth rate. They had no visibility into cost drivers, no enforcement mechanisms, and a finance team manually reviewing cloud bills each month.

Our approach

Convergile.AI deployed autonomous cost optimization agents across their AWS and Azure environments — continuously right-sizing compute, identifying idle resources, and enforcing spend policies without manual review. We also implemented a real-time cost intelligence dashboard and automated anomaly alerting.

The challenge

A 1,200-person IT services organization had deployed 47 AI models across their operations over 18 months — with no inventory, no governance framework, and no visibility into model performance or compliance status. A regulatory audit was 90 days away.

Our approach

Convergile.AI deployed our AI Asset Management framework — running automated discovery across all environments, cataloging every model and agent, establishing a governance policy layer, and building a compliance dashboard ready for audit. We also implemented model observability for all production systems.