---
title: "AI Analytics Solutions | Predictive Analytics | Agility"
url: https://agilitytech.ai/solutions/ai-analytics
description: "AI analytics solutions: predictive models, forecasting, and decision intelligence on your data. Production dashboards and ML deployed in 3 to 8 weeks."
publisher: Agility (agilitytech.ai)
---

AI analytics solutions

# 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.

[Book a free consultation](https://agilitytech.ai/contact)

Live accuracy, fraud model77.8%

Data assessment to production6 to 10 wks

Minimum historical data12 months

Data readiness assessmentWeek 1

The short answer

## What does AI analytics from Agility include?

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, and the typical timeline is 6 to 12 weeks from data assessment to production model.

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 operation77.8%

Built on AWS SageMaker with Yelo and Tookan order dataSageMaker

Our typical timeline from data assessment to production model6 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.

[Ask us something else](https://agilitytech.ai/contact)

**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.

[Book a free consultation](https://agilitytech.ai/contact)