Outcome Verification

Real Engineering Systems.
Proven Business ROI.

We do not count "story points" or commits. We count cloud cost reductions, latency decreases, and system uptime improvements. Here is the technical documentation of our recent client outcomes.

Cloud & Kubernetes

SaaS Platform Cost Reclamation

A video analytics SaaS was running large workloads on Amazon Web Services EKS. Out-of-control NAT Gateway processing charges and idle VM capacity were destroying their margins.

We replaced their legacy autoscaler with Karpenter, established Interface Endpoints to bypass NAT Gateways, and tuned JVM container lifecycle settings to migrate stateless apps to Spot instances.

-$320k
AWS Cost/Yr
4 mins
Deploy Time (was 45m)
99.99%
Uptime Maintained
AWS EKS Karpenter Spot Nodes VPC Endpoints
Request Cost Review
Distributed Systems

Event-Driven Kafka Migration

A logistics platform was routing device tracking metrics directly into a centralized PostgreSQL database, causing intense write locks and API timeouts during peak delivery hours.

We decoupled their tracking ingest path using Apache Kafka brokers and implemented an asynchronous processing layer utilizing Akka Streams and Scala.

10M+
Daily Events
< 10ms
Ingestion Latency
0
Write-lock Errors
Apache Kafka Akka Streams Scala GP3 Storage Tuning
Request Architecture Review
AI Infrastructure

Low-Latency RAG Integration

An enterprise AI search client integrated third-party LLMs directly into their web servers. The average search returned in 4.5 seconds, and API token spend exceeded projections.

We built a semantic caching middleware layer, right-sized pgvector index parameters, and deployed open-source models using vLLM node groups on self-hosted Kubernetes.

-65%
API Bills Reduction
< 800ms
Search Response Time
35%
Cached Queries
vLLM Engine Qdrant Vector DB pgvector Llama-3
Request AI Stack Audit