Real Results. Real Teams.

The Challenge

A fast-growing SaaS company was running Jira Server 8.x with 47 Marketplace apps, 12,000 issues, and a heavily customised permission scheme. Their IT team had attempted a migration internally and failed twice — each time losing custom field data and breaking automation rules. They needed the migration completed before their Server license expired.

Our Approach

• 1.Audited all 47 apps — 31 had Cloud equivalents, 9 had partial parity, 7 had no Cloud version and needed replacement
• 2.Ran 3 complete dry-run migrations over 3 weeks, fixing data integrity issues at each stage
• 3.Built a custom field mapping document and pre-configured the Cloud instance before cutover
• 4.Replaced 7 unsupported apps with native Jira Automation rules, eliminating licensing costs
• 5.Executed production cutover on a Saturday night — 4.5 hours total with zero issues

Results

The Challenge

An e-commerce platform running on AWS EKS had seen their monthly cloud bill grow from $8,000 to $31,000 over 18 months as they scaled. Their engineering team was focused on feature delivery and had no visibility into what was driving costs. They suspected over-provisioning but had no data to act on.

Our Approach

• 1.Deployed Kubecost to get per-namespace, per-service cost visibility within 24 hours
• 2.Identified that 3 development namespaces were running 24/7 at full production scale — unnecessary outside business hours
• 3.Right-sized 23 deployments using 4 weeks of actual p99 memory and CPU data
• 4.Implemented Karpenter to replace the fixed node groups — auto-scaling to exactly the compute needed
• 5.Set up cluster autoscaler policies to scale down dev/staging clusters overnight and on weekends
• 6.Moved batch processing workloads to Spot instances (70% cheaper than On-Demand)

Results

The Challenge

A FinTech startup's deployment process was entirely manual — an engineer would SSH into production servers, pull the latest code, run database migrations by hand, and restart services one by one. The process took 3 hours and required a senior engineer to babysit it. Deployments happened fortnightly because they were so painful. The team wanted to ship daily.

Our Approach

• 1.Containerised all 4 microservices with optimised multi-stage Docker builds
• 2.Built GitHub Actions pipelines with automated testing, security scanning (Trivy + Semgrep), and Docker image builds
• 3.Set up AWS ECS with Fargate for zero-infrastructure container hosting
• 4.Implemented blue/green deployments so there is zero downtime on every release
• 5.Automated database migrations using Flyway, running as part of the deployment pipeline
• 6.Added Datadog for deployment tracking and rollback triggers on error rate spikes

Results

The Challenge

A consulting firm had been using Jira and Confluence for 4 years with no admin oversight. The result: 340 Confluence spaces (most with fewer than 5 pages), 89 Jira projects (60 abandoned), 200+ custom fields, and a permission scheme so complex no one understood it. New hires could not find documentation. Senior staff were duplicating work because they could not search effectively.

Our Approach

• 1.Audited all 340 Confluence spaces — consolidated to 18 active spaces with clear ownership
• 2.Archived 60 inactive Jira projects, preserving all data but removing them from active views
• 3.Reduced custom fields from 200+ to 34 by merging duplicates and deleting unused ones
• 4.Redesigned the permission scheme using 8 clean groups instead of 47 individual user assignments
• 5.Created a Confluence information architecture with templates, a label taxonomy, and a page ownership model
• 6.Delivered a 2-hour admin training session so the internal team could maintain the setup going forward

Results