Fraud detection models
Catch fraud without blocking good customers.
- 1Builds real-time features
- 2Scores transactions
- 3Monitors model drift
Atlanta, GA · Data & AI Platforms
Data platforms and ML for Atlanta companies, from fraud and authorisation data to freight networks and health programmes, with governance built in.
Data & AI Platforms in Atlanta
Atlanta processes a large share of the country's card payments, moves freight through one of the world's busiest airports and a major logistics network, and is home to public health institutions and a big film and media industry. AI here runs on sensitive, high-volume data.
Where it fits
Catch fraud without blocking good customers.
Plan capacity across hubs and lanes.
Clean, shared data for programme reporting.
Built for Georgia rules
Card data kept out of AI prompts and logs, with tight access control.
A written security program covering every system that handles customer financial data.
Safeguards and a business associate agreement for anything touching patient data.
Security safeguards and prompt notice if personal data leaks.
Working together · Eastern Time
Your morning is our evening, so we meet live at the start of your day and build while you sleep.
How we work
We map your data sources, flows, owners and quality issues.
We design a platform that fits your cloud, scale and budget.
We deliver pipelines, models and dashboards in working increments.
We set up monitoring, alerts and governance your team can run.
Yes. We build streaming pipelines for high-volume data, with tokenisation so card data stays out of analytics.
Data versions, training runs and performance results are recorded automatically and kept with each model version.
MLOps is the set of practices and tools for deploying, monitoring and updating machine learning models reliably, much as DevOps does for software. It keeps models accurate as data changes.
We work with the major cloud providers and with on-premise environments, and design the platform around the tools and contracts you already have.
Not always. We often build the data foundations a specific AI use case needs first, then extend the platform as more use cases follow.
Let's talk
We help you navigate Enterprise AI, from first use case to production.
Let's connect