Proof, not promises

NexaBank was losing $18M/year in maintenance costs on legacy mainframes, unable to ship new products faster than one release per quarter, and facing regulatory pressure to modernize. A previous vendor had failed to migrate after 3 years and $40M.

Our architects designed a phased strangler-fig migration: 47 event-driven microservices in Go, a Kafka-based ledger backbone, and a real-time fraud-detection model deployed on Kubernetes with an SRE-grade observability layer.

Full core banking replatform delivered in 18 months with zero customer-facing downtime. Release cadence went from quarterly to daily, fraud losses dropped 61%, and infrastructure costs fell 73%.

HealthVault had valuable labeled imaging data but no ML org. Radiologist workloads had doubled post-2020, and diagnostic error rates were creeping up. Off-the-shelf models were unusable — they failed regulatory review and never touched clinical data.

Our AI team fine-tuned three vision transformers on 4.2M labeled studies, wrapped them in a FastAPI inference service on Azure, and built a clinician workflow integration that surfaces suggestions inside existing PACS/RIS software — never around it.

Deployed live across 50 hospitals. Radiologists report 40% faster read times and a 22% reduction in missed findings on stroke and pulmonary embolism cases. FDA 510(k) submission pathway initiated.

A ministry-level agency needed to consolidate 14 disparate monitoring systems, ingest telemetry from 60,000+ endpoints, and cut mean-time-to-respond from hours to minutes — while operating entirely inside a sovereign cloud enclave.

We built a Splunk + Elastic SIEM backbone with custom detection engineering, ML-based anomaly detection for 30 threat categories, and SOAR playbooks that auto-contain 82% of incidents. A red team from CyForti stress-tested every playbook before go-live.

Live SOC blocks 10,000+ threats per month, meets an aggressive 4-minute mean-time-to-respond, achieved ISO 27001 & national CSF certification, and has passed 3 independent audits without a single finding.

SkyCommerce's recommendations were stale (batch-updated overnight), search relevance ranked ~4/10 in usability studies, and pricing decisions took weeks to A/B test. Their existing platform couldn't handle sub-second personalization at 6M MAUs.

A streaming personalization stack: Kafka + Flink for real-time features, a two-tower model for recommendations, learned-to-rank search built on Vespa, and a reinforcement-learning price optimizer tested via a bandit framework.

Revenue-per-visitor up 31%. Search-to-add-to-cart conversion up 44%. Cart abandonment down 18%. All page loads under 2 seconds despite full personalization.

FleetMind's aging fleet had a 14% annualized breakdown rate. Each roadside failure cost ~$4,200 in lost revenue, tow-truck fees, and driver-hour penalties. A previous IoT initiative had stalled at data collection with no predictive layer.

We modernized their telemetry pipeline (MQTT → TimescaleDB), trained gradient-boosted models on 3B+ historical sensor readings, and deployed edge inference to the vehicles themselves. A dispatcher dashboard turned predictions into scheduled maintenance windows.

62% reduction in unplanned breakdowns. 28% average fuel savings from route optimization. $12M in avoided operational costs in Year 1 — a 6.4x ROI.

TechCorp's data centers were reaching end-of-life. They had 200+ services spanning Java, Node.js, Python, and .NET — with tangled inter-service dependencies, undocumented network policies, and 2.4 PB of stateful data.

A migration factory approach: Terraform-first landing zones, service dependency mapping via eBPF, a phased strangler migration with dual-writes, and a custom cutover automation that let us migrate 12 services per weekend.

200+ services fully live on AWS in 8 months (3 months early). 67% reduction in infrastructure spend. Deployment frequency went from weekly to on-demand. Zero customer-reported downtime through the entire migration.