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
Building Local LLMs Using Dart FFI And llama.cpp: Beyond Wrapper Packages
Build local LLMs in Flutter with Dart FFI and llama.cpp, and see how native bridges, GGUF models, memory management, and token streaming enable private, on-device AI.
My Flutter App Froze With Three Photos on Screen. Here's What I Was Doing Wrong
This blog explains how rethinking Flutter’s image-processing architecture fixed severe performance issues and improved rendering efficiency.
AI in Wealth Management: What It Takes to Turn a Smart Demo Into a Production-Ready Product
Learn what it takes to turn an AI wealth management demo into a production-ready product. Explore production-readiness criteria, architecture, data foundations, governance, monitoring, rollout strategies, and AI product engineering considerations.
Building a Production-Ready Canva-like Editor with Konva.js, React 19 and Next.js 15
This blog explains how to build a production-ready canvas editor with Konva.js, React, and Next.js, covering architecture, performance, and key engineering decisions.
Why AI Agents Fail in Production: Building Systems That Recover | Pushkar
Pushkar’s thegeekconf mini talk explores why AI agents that perform well in demos often struggle in production, and how loud failures, clean context, step monitoring, guardrails, and better agent loops can make them more reliable and predictable.
GeekyAnts Launches AntFlow AI for Spec-Driven Software Engineering
This article covers the launch of AntFlow AI and its spec-driven approach to agentic software development.
