No slideware. Each of these is a real engagement — a problem, the architecture we shipped on AWS, and the numbers it moved. From billing automation to lakehouse migration, this is the work.
Across FinTech, e-commerce, EdTech and travel — all on AWS, multi-cloud when the workload called for it.
A debt-collections platform billing 300+ lenders across SMS, WhatsApp, IVR, email and legal notices was computing every invoice by hand. We built a Redshift billing data mart with automated EMR/MWAA pipelines — turning a monthly manual grind into a daily, dashboard-ready feed.
A global FBA aggregator ran ~900 transformation queries inside Redshift, pushing compute to ~$40K/month. We re-architected to a hybrid lakehouse — Spark on EMR, Iceberg on S3, Airflow orchestration — and converted 350+ queries with a zero-downtime cutover.
An ethics-and-compliance training provider — 500+ courses, 70+ languages, 30M+ learners — had data trapped across Postgres, MongoDB and third-party systems. We built a secure, GDPR-compliant data lake with embedded QuickSight dashboards and language/sentiment analysis.
A hotel-booking SaaS ingesting 3.3 billion records a month was crushing its database with IOPS and connection drops. We replaced it with a near real-time, pay-per-use streaming pipeline on AWS — data queryable within five minutes, for under $1,000/month.
Across these engagements
Tell us what you're building or wrestling with. We'll come back with a one-page architecture sketch and a plan — the same way every one of these started.
Book a consultation →