GCP Infrastructure That Scales With Your Company

Impact We Have Made

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

Reducing Carbon Footprints Through Digital Innovation
Modernized a construction sustainability platform that helps teams analyze environmental impact, streamline workflows, and make data-driven decisions to support greener building practices and long-term sustainability goals.
675
Users Onboarded
15+
Major Features Built
Our Core Capabilities
Explore these Technologies for Product Backend
Node.js / NestJS Engineering
PostgreSQL Architecture
Redis for Scaling
MongoDB Data Modeling
Advantages of Partnering with GeekyAnts for GCP Scalable Infrastructure
Complete Backend Engineering Services for Enterprises and Companies
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 Flutterโs 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
Pushkarโs 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.

