AI & Automation
Machine learning and automation solutions that turn data into decisions. From model development through production deployment and monitoring.
Practice areas
ML Model Development & Deployment
Custom model development, fine-tuning, and production deployment with the infrastructure to keep models accurate over time.
Data Pipeline Engineering
Lineage-tracked, reliable pipelines that move and transform data without the fragility of legacy ETL.
Predictive Analytics
Forecasting, anomaly detection, and classification systems built around your actual business metrics.
Process Automation
Automating repetitive, rules-based work across systems so your team focuses on what requires judgment.
Work in this practice
Data platform that gave a fuel carrier one view of outages, tolls, deliveries, and drivers
Consolidated six systems (DTN, Gravitate, Samsara, Bestpass, TMW, Google) into one data platform for the same mid-market fuel carrier. Dashboards track tank outages across 5,000+ sites, hold toll spend to policy and reclaim $200K+ a year, score every delivery window by GPS geofence, and keep driver hours honest.
Real-time TMS integration that gave 5,000+ c-stores live visibility into every fuel load
Connected a mid-market fuel carrier's Gravitate dispatch platform to the TMSs its 5,000+ c-store customers run, using Manifold, our TMS integration platform. Customers see live load status, cost, and ETAs in their own systems, place orders that land in Gravitate, and get a DTN-sourced digital BOL for every load.
Common questions
How is AI & Automation different from the Agentic AI practice?
This practice covers the machine learning and data side: pipelines, predictive models, and classic process automation. Agentic AI covers systems that act on their own decisions. Real programs usually draw on both, and the same team builds both.
What makes your data pipelines different from our existing ETL?
Lineage tracking, tested transformations, and reliability engineering. The goal is joins your team trusts enough to make decisions on, which is the difference between a warehouse and a liability.
Can you build on the data tools we already run?
Yes. We build on your existing warehouse and BI tooling where it holds up. One current platform runs on S3, Glue, Athena, dbt, and Metabase, unifying six source systems for a fuel carrier's daily operations.
How do you keep models accurate after deployment?
Eval baselines before launch, monitoring in production, and the retraining infrastructure to correct drift. A model without monitoring is a guess with a dashboard.