AWS Services

Most AWS trouble is not service trouble. It comes from an account structure that grew one request at a time, permissions granted once and never reviewed, and a migration that moved servers without changing anything about how they are operated. Iseyon sets the foundation first, then moves and builds workloads on top of it.

We work on new AWS estates, on migrations from on-premises or another cloud, and on accounts that already carry production but have outgrown the way they were originally set up.

Foundation and migration

Accounts, guardrails and network

We separate workloads and environments into their own accounts under AWS Organizations, with centralized identity, service control policies as guardrails, logging that lands outside the account being logged, and a network design that accounts for connectivity back to whatever stays on premises. Baselines are applied as code, so a new account starts governed instead of being corrected later.

Migration approach

We assess the portfolio application by application and map dependencies before touching anything. Each workload then gets a decision: rehost where it runs fine as it is, replatform onto managed databases or containers where the operational load is the real cost, refactor where the architecture is the constraint, and retire what is no longer used. Databases move with replication and a verified reconciliation step rather than a single export. Cutovers are rehearsed, batched into waves, and reversible.

Compute, storage and networking

We build with EC2, ECS or EKS, Lambda, S3 and EBS, inside VPCs designed for the traffic they carry rather than copied from a default. Auto scaling, multi-AZ placement, and backup and recovery targets are set against what the business needs, and failover is tested rather than trusted.

Data and analytics

Data engineering

We build lakes on S3 with deliberate partitioning, columnar formats and a catalog other tools can rely on, transform with Glue or Spark, and serve queries through Redshift and Athena. Pipelines are incremental and idempotent, and schema changes are handled in the pipeline instead of by a person at midnight.

Analytics and business intelligence

We point BI tools, Amazon QuickSight included, at modeled data rather than raw tables, so definitions live in one place and a dashboard change does not require another extract. What the business reads and what the pipeline produces stay the same thing.

Machine learning

We use Amazon SageMaker for the work around a model as much as the model itself: versioned datasets, reproducible training, deployment behind an endpoint or a batch job, and monitoring that watches drift and spend together. Every model in production has an owner and a rollback path.

Building and running

Serverless and event-driven services

We build with Lambda, API Gateway, EventBridge, SQS and Step Functions where the workload is event shaped, which keeps idle cost low and takes servers off the operational surface. Where a container fits the workload better than a function, we say so rather than forcing the pattern.

Security and governance

Permissions start from least privilege and attach to roles, not to users. We set up KMS encryption with keys the client controls, CloudTrail and Config for evidence and drift detection, GuardDuty and Security Hub for detection, and secrets in Secrets Manager or Parameter Store. Access review becomes a scheduled process with an owner instead of an annual scramble.

Cost management

We tag for allocation from the first account, set budgets and anomaly alerts, rightsize after watching real utilization, and commit to Savings Plans or reserved capacity only where usage has proven steady. Storage lifecycle rules and idle resource cleanup are automated rather than remembered.

DevOps and automation

Infrastructure is code, with review, environment promotion and the same pipeline for every stage. Application delivery gets build, test and deploy stages with a rollback path, so releasing stops being an event that needs a bridge call.

Integration and modernization

We integrate AWS with the systems that stay in place: on-premises databases, third party SaaS and internal applications, using managed messaging and API layers instead of point-to-point links nobody can safely change later.

How a migration runs

The goal is a client team that can add the next workload without us in the room.