AWS Consulting & Cloud Engineering Services

GeekyAnts operates as a specialist engineering firm — not a large system integrator where your project becomes a ticket, and not a small shop without the range to handle complex builds. With 600+ clients served, 1,000+ digital products delivered, and deep experience across 50+ industries, we bring cross-domain AWS expertise to every engagement. Our engineers work in AWS daily across production environments ranging from AI platforms and fintech systems to healthcare applications and enterprise supply chains.

AWS consulting engagements vary significantly based on scope, duration, and team size. A focused cloud cost optimization audit typically runs over a few weeks. A full cloud-native product build can span several months. Enterprise cloud migration and modernization projects are typically multi-phase, multi-month engagements. GeekyAnts scopes transparently before any commitment — book a discovery call for an honest assessment of what your situation requires.

A targeted migration of a single application or workload can be completed in 4–8 weeks. A broader enterprise migration with legacy modernization components typically runs 6–18 months, depending on the complexity of the existing environment and the number of systems being moved. GeekyAnts uses a phased approach — delivering incremental value throughout the engagement rather than requiring a single go-live event.

Yes — and this is the most common structure for our engagements. GeekyAnts engineers embed alongside your team, transfer knowledge throughout the project, and build systems your team can own and operate independently after delivery. If you need us to own the full build, we can structure it that way too. We adapt to what actually serves your organization.

Regularly. Some of our highest-impact work has involved stepping into live, production AWS environments and improving them without disrupting operations. The fintech engagement where we reduced cloud costs by 60% and saved over $57,000 annually is one example. If your current AWS environment is costing too much, performing inconsistently, or becoming difficult to manage — that's exactly the kind of problem we're built for.

Our most frequently deployed services include Amazon ECS and ECS Fargate for container orchestration, AWS Lambda for serverless workloads, AWS Bedrock for AI and large language model applications, Amazon S3 for scalable storage, Amazon CloudWatch for observability and monitoring, Amazon DynamoDB for NoSQL data management, Amazon EventBridge for event-driven architectures, Amazon ECR for container image management, and AWS IAM for access control and identity management. We work across the full AWS service catalog, but these appear in virtually every production architecture we deliver.

We run a structured FinOps audit: baseline your current spending, identify overprovisioned and underutilized resources, map inefficiencies to specific services and configurations, and deliver a prioritized savings roadmap. Implementation typically includes automated scaling policies, storage lifecycle rules, rightsizing recommendations, and monitoring configurations that prevent costs from drifting back up. The 60% cost reduction achieved for one fintech client is the standard we hold every engagement to.

Yes. GeekyAnts has built platforms in healthcare, financial services, and other regulated industries where compliance is a first-class requirement, not an afterthought. We design AWS environments with appropriate security controls, access management frameworks, audit trails, and encryption configurations from the start — because retrofitting compliance into a live production system is far more expensive than building it in correctly.

AWS Bedrock is Amazon's managed service for accessing and deploying large language models (LLMs) and foundation models in cloud applications. GeekyAnts uses AWS Bedrock to build production AI applications — including document intelligence platforms, automated content generation systems, and AI-powered workflows — where clients need the capabilities of large language models without the operational complexity of managing model infrastructure directly. Our Pillar Engine engagement is a live example of AWS Bedrock in a production AI document processing environment.