AI & Predictive Analytics
Agility builds predictive analytics, fraud detection, and AI-powered decision systems deployed in production on AWS SageMaker, Azure ML, and Google Vertex AI.
- Live accuracy, fraud model
- 77.8%
- Data assessment to production
- 6 to 10 wks
- Minimum historical data
- 12 months
- Data readiness assessment
- Week 1
The short answer
What does AI analytics from Agility include?
Case study
AI-powered fraud detection for a Caribbean food delivery marketplace
When a fast-growing Caribbean food delivery platform began losing significant revenue to fraudulent orders, they turned to us to build a machine learning solution that could detect and block fraud in real time. Using their transaction and order data along with our expertise in AWS SageMaker and delivery-management platforms like Yelo and Tookan, we delivered a model that achieved 77.8% accuracy in live operation and cut losses from chargebacks.
Fraud detection at a glance
- Model accuracy in live operation
- 77.8%
- Built on AWS SageMaker with Yelo and Tookan order data
- SageMaker
- Our typical timeline from data assessment to production model
- 6 to 12 weeks
The problem
Key challenges
High fraud volume
Fraudsters exploited rapid onboarding and lack of unified prevention rules, resulting in unauthorized orders and chargebacks.
Fragmented data
Financial data lived separately from order metadata, making end-to-end analysis difficult.
Real-time needs
Manual review was too slow, and batch scoring after the fact only reduced losses retrospectively.
Security and scale
The solution needed to run compliantly with low latency and scale during peak periods.
How we built it
Our solution architecture
Data pipeline
- Unified S3 data lake pipeline
- AWS Glue feature engineering
- Real-time data processing
ML development
- SageMaker model training
- Hyperparameter optimization
- Ensemble approach
Deployment
- Low-latency endpoints
- API Gateway integration
- Automated workflows
Monitoring
- Real-time metrics
- Automated retraining
- Performance analytics
FAQ
AI analytics: common questions
Timelines, data requirements and how models stay accurate after go-live.
What types of AI analytics solutions does Agility build?
Agility builds predictive analytics models, fraud detection systems, anomaly detection pipelines, demand forecasting engines, and real-time dashboards on AWS SageMaker, Azure ML, and Google Vertex AI. Every engagement starts with a data readiness assessment before any model architecture is chosen.
How long does an AI analytics implementation take?
A production ML model from data assessment to live deployment typically takes 6 to 10 weeks. Predictive dashboards on existing data: 4 to 6 weeks. Custom fraud detection or anomaly detection with real-time inference: 8 to 12 weeks. Timeline depends on data availability and model complexity.
What data is required to build a predictive AI model?
Minimum 12 months of historical transactional or operational data, a defined outcome to predict, and access to relevant feature variables. Agility conducts a data readiness assessment in week one to confirm model viability before committing to a build timeline.
How do you prevent AI model accuracy from degrading over time?
Every model Agility delivers includes an evaluation framework with defined accuracy metrics (F1, AUC-ROC, or RMSE depending on type), automated retraining triggers when accuracy drops below threshold, and a monitoring dashboard showing live prediction performance. The Caribbean food delivery fraud model runs at 77.8% live accuracy with automated retraining.
Have an outcome you want to predict?
Tell us about your data. We start with a data readiness assessment before committing to a build timeline.