Engagements we can talk about
Client names are withheld under confidentiality agreements common in this line of work, but the numbers, the problems, and the approach below are real.
Cutting fraud review time from hours to minutes
Challenge
A growing payments platform had a manual fraud queue that couldn't keep pace with transaction volume, and analysts were drowning in false positives.
Approach
We built a real-time risk-scoring pipeline with a human-in-the-loop review dashboard, backed by a streaming data layer and continuous model monitoring.
Result
Review time dropped by more than half, false positives fell sharply, and the fraud team now spends its time on the cases that actually matter.
An AI scribe that gave clinicians two hours back a day
Challenge
Clinicians across a regional network were spending more time on documentation than on patients, driving burnout and after-hours charting.
Approach
We deployed an ambient documentation assistant integrated directly with the existing EHR, with accuracy guardrails and a clinician review step before anything is finalized.
Result
Charting time dropped sharply and adoption has stayed above 90% six months after rollout, with clinicians reporting more time for actual patient care.
Migrating a 15-year-old dispatch system without downtime
Challenge
A national carrier's dispatch system ran on aging on-premise servers that couldn't scale for peak season and were increasingly expensive to maintain.
Approach
We ran a phased migration to containerized services on the cloud, rebuilding the CI/CD pipeline alongside the migration so releases stayed safe throughout.
Result
The system now scales automatically during peak volume, infrastructure spend dropped by nearly half, and the team ships three times as often.
One source of truth for inventory across 300 stores
Challenge
Inventory data was scattered across regional spreadsheets and a legacy point-of-sale export, so reporting was always days out of date.
Approach
We centralized ingestion into a single data warehouse with real-time streaming from point-of-sale systems, and built self-serve BI dashboards for store managers.
Result
Store teams now see inventory in real time, stockouts dropped by nearly a fifth, and central operations gets one consistent view instead of ten conflicting ones.
“They didn't just hand us a model and walk away. Six months later they're still the team we call when something breaks at 2am.”