REW Technology

REW Technology Innovative technologies for business optimization

Does your team know who owns the Kubernetes cluster at 2am?Most US mid-market companies adopt Kubernetes for a technical...
04/08/2026

Does your team know who owns the Kubernetes cluster at 2am?

Most US mid-market companies adopt Kubernetes for a technical reason. The cluster breaks for an operational one.
Two engineers build it. They understand it well.
6 months later they move on. The team that inherits it has no documented picture of how it was designed or where it breaks.
Every incident starts with an hour of archaeology.

Four configurations that separate stable clusters from fragile ones:

1. Namespace-per-team with enforced resource quotas -- one workload consuming all cluster memory evicts other teams' services without warning
2. GitOps via ArgoCD or Flux -- every cluster change version-controlled and reversible
3. Probe discipline -- misconfigured liveness and readiness probes cause more production incidents than any other single config issue
4. PodDisruptionBudgets on every production workload -- without one, a rolling node upgrade can take down all replicas simultaneously

↳ Observability stack before the first production workload, not after the first incident
↳ Resource requests and limits on every workload before you touch HPA
↳ ECS Fargate, Azure Container Apps, or Nomad may get you further if your team is under five platform engineers

The operating model has to exist before the cluster does.

📖 We put together a full breakdown of everything above – configurations, alternatives, autoscaling, and the VMware/VKS path if your org runs on VCF. It took a while to write and we think it covers the parts most Kubernetes guides skip. Worth a read: https://rewtechnology.com/study-case/case-study/kubernetes-project-succeed-fail-before-first-deployment/

Can your payment infrastructure absorb a new core banking system without losing track of transactions?A US financial ser...
03/19/2026

Can your payment infrastructure absorb a new core banking system without losing track of transactions?

A US financial services company processing billions in payments annually needed to integrate a new core banking platform into their existing stack.
Two problems surfaced before any code was written:

1. The new system processed payments in real time. The existing one ran on end-of-day batch cycles. Nothing bridged the gap.
2. Cross-border payments above a certain threshold required a compliance logging format the vendor didn't produce by default.

REW Technology ran a full discovery phase first:
↳ Transaction buffer built in Java + Spring Boot to sync timing between both systems
↳ End-of-day batch replaced with event-driven flow via Apache Kafka
↳ Compliance logging automated with validation before every record was written
↳ Rollback mechanism added to reroute transaction types without full redeployment

The result:
✔ Transaction sync window cut from end-of-day to under 12 minutes
✔ Zero compliance gaps in post-launch audit
✔ One rollback used in production — resolved and re-enabled the same day

📖 Read full case study here: https://rewtechnology.com/study-case/case-study/core-banking-payments-stack-integration-case-study/

Many marketplace teams deal with the same problem — people ask the same questions every day.A marketplace operator we wo...
12/19/2025

Many marketplace teams deal with the same problem — people ask the same questions every day.

A marketplace operator we worked with faced this at scale. The information existed, but it wasn’t easy to find.
Because of this, the internal team spent most of their day repeating the same explanations.

Before choosing any AI approach, we ran a proper discovery phase. We spoke with support teams, reviewed workflows, analyzed real vendor questions, and mapped what was actually needed. The goal was simple: build something useful — not something created just because “AI is hype.”

The outcome of the discovery was clear: the client needed fast, reliable answers that matched their official documentation.
So we built an AI chatbot focused only on that.

It could:
• Answer vendor questions using verified information
• Explain rules, fees, and onboarding steps
• Reduce repeated support tickets
• Help vendors complete tasks without waiting for a reply
• Run securely inside the client’s environment

Thinking about building a similar AI chatbot — one that gives correct answers without hallucinations?
We can help you design it properly, from discovery to delivery.

Full case study here:
https://rewtechnology.com/study-case/case-study/streamlining-marketplace-operations-with-ai-chatbots/

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