Cloud and Platform Engineering
Impact We Make
400M+ global payments processed annually
The system provides the scalability required to handle millions of transactions while maintaining data integrity and administrative control.
120k+
Active Users in the UK, Canada, Europe, and Australia
400M+
Global Payments Processed Annually
350k+
Downloads Across iOS and Android
3x faster AI feature iteration
The modular backend architecture allowed the team to deploy new AI capabilities and personalized features significantly faster than industry benchmarks.
40%
Reduction in meal decision time
2x
Increase in daily active usage during pilot
3x
Faster iteration on new AI-driven features
50% reduction in manual validation cycles
The integration of automated benchmarking frameworks and AI-driven process automation also resulted in 30% faster internal testing workflows.
30%
faster internal testing
50%
reduction in manual validation
Our Core Capabilities
Multi-Cloud Strategy
- Terraform
- Kubernetes (K8s)
- Cross-plane
DevOps And CI/CD
- GitHub Actions
- ArgoCD
- Blue/Green Deployment
FinOps And Observability
- Prometheus/Grafana
- Cost Allocation Tags
- Distributed Tracing
Advantages of Partnering with GeekyAnts
Backend Engineering Services for Enterprises
Our Latest Thinking
From Kickoff to Delivery: How GeekyAnts Onboards Engineering Teams and Ensures Continuity
This blog explains how GeekyAnts onboards engineering teams through structured ramp-up, knowledge transfer, replacement planning, and continuity practices.
Dedicated AI Product Pod vs. In-House Hiring vs. Staff Augmentation: A Cost, Control, and Delivery Risk Comparison
Compare dedicated AI product pods, in-house hiring, and AI staff augmentation across costs, delivery ownership, management effort, and risk to choose the right model for your product roadmap.
How GeekyAnts Manages AI Models After Launch to Maintain Security, Control, and Flexibility
AI models don’t stay predictable forever. Discover how GeekyAnts’ documented projects keep model changes in check, catch performance issues, protect sensitive data, and switch providers when requirements change or issues arise.
How GeekyAnts Evaluates AI Systems for Production Readiness
This blog explains how GeekyAnts evaluates AI systems for accuracy, security, bias, cost, and performance before production, supported by real project examples.
How GeekyAnts Selects AI Tools and Model Providers While Managing Data Security and Client Requirements
Explore the AI tools and model providers used in GeekyAnts’ documented projects, including Claude Code, Gemini, OpenAI, and Ollama. Learn how tool selection, data retention, subprocessors, hosting options, and client requirements shape AI adoption.
How GeekyAnts Uses AI in Software Engineering: From Requirements to Release
This blog explains how GeekyAnts uses AI across software development while maintaining human oversight, quality checks, and engineering accountability.
