Data and Infrastructure Engineering

We build digital solutions for pipelines, warehouses, and databases that support decision-making. From database tuning to constructing pipelines, we create production systems with observability, governance, and cost transparency.

Messy data pipelines and under-optimized databases lead to slow queries and high storage costs. Running an open-source data warehouse with 30TB of data costs nearly $1 million every year. Without a standard framework for your data, decision-making slows down and teams lose trust in the company’s data projects.

GeekyAnts removes these data bottlenecks and storage wastes. We audit your current systems, find the performance blockers, and build a roadmap.

Impact We Make

View all

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

Increase in daily active usage during pilot

Faster iteration on new AI-driven features

$57,000+ Annual Savings

The project achieved a 60% reduction in monthly cloud costs (from $8,100 to $3,300) without any loss in transaction speed or system security.

60%

Reduction in monthly cloud costs

$4800

Saved per month

$57,000+

Annual savings

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

Database Reliability Engineering

We deep-tune PostgreSQL, MariaDB, and Redis to ensure performance and data safety.

Replication Setup
PITR Backup
Query Optimization

Data Pipelines (ETL/ELT)

We build robust pipelines to move, transform, and load data for analytics and machine learning.

CDC
Batch Processing
Stream Processing

Infrastructure as Data

Managing stateful workloads using advanced orchestration patterns.

StatefulSets
Persistent Volumes
Disaster Recovery

Data Engineering Services for Enterprises

PostgreSQL Infrastructure

Advanced tuning and scalable PostgreSQL architecture.

MariaDB Performance

High-availability MariaDB solutions and performance optimization.

Redis Cluster Management

Distributed caching and high-speed data structures

Advantages of Partnering with GeekyAnts

We engineer systems capable of managing Petabyte-scale data without performance degradation

Through the implementation of Materialized Views and specialized engines, we reduce dashboard load times from 30 seconds to under 500ms.

We prioritize Database Reliability Engineering to ensure total data safety.

Backend Engineering Services for Enterprises

Product Backend Studio

We build the "0-to-1" engine for new, high-growth applications.

Know More

Enterprise Backend

We specialize in transforming monolithic legacy systems into modern microservices.

Know More

Cloud & Platform Engineering

We treat infrastructure as code to balance performance with cost.

Know More

Data & Infrastructure Engineering

We build the high-speed pipelines that turn raw data into business intelligence.

Know More

Our Latest Thinking

Insight

Sep 11, 2026

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.

Insight

Sep 11, 2026

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.

Insight

Sep 10, 2026

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.

Insight

Sep 10, 2026

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.

Insight

Sep 10, 2026

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.

Insight

Sep 10, 2026

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.

Is your data team fighting high latency instead of shipping insights?

Book a discovery consulting call with our Data Architects today.

Contact us

Name*

Email*

Phone*

Company Name*

Country*

Requirement*

Tell Us About Your Project Requirements*

Attachment

Send me a copy of the NDA

Subscribe to our Geeky Newsletter

Acknowledge and agree to our Privacy Policy

Our Backend Stack

Working with clients across 20+ industries has verified one thing—cookie-cutter approaches do not work when it comes to backend. The backend needs to be tailored for the business and the goal. That is why we do not force one stack on you. We choose the right tool based on performance needs, ecosystem fit, and your team’s expertise.