Add Business-Specific AI to Your Application Without Building the Training Platform

01

Your application may already have the workflow

What it may not have is an easy way for every customer to teach the application how their business works. Imagine a SaaS product that processes customer requests. Customer A wants requests categorized as:

• Sales
• Support
• Billing
• Other

Customer B wants:

• Claims
• Policy Changes
• Renewals
• Cancellations

Customer C has ten categories unique to its organization. Building one generic model doesn't solve this very well. Building custom machine-learning infrastructure for every customer is even harder.

02

Let customers teach the model

StayCharted AMT separates model training from your application's primary workflow. A customer provides examples containing text and the correct labels. StayCharted AMT trains a customer-specific model. Once the model is ready, your application sends new data to the Prediction API and receives the result.

03

Runtime architecture

1. 1Your application: SaaS / CRM / ERP / portal / internal app
2. 2New record
3. 3StayCharted AMT Prediction API
4. 4Customer model
5. 5Prediction + confidence
6. 6Your application: categorize, route, tag, update or recommend

04

How the model gets created

Training happens separately from prediction.

1. 1Customer’s historical data (CSV / XLSX)
2. 2StayCharted AMT AI Model Trainer
3. 3Train + evaluate
4. 4Business-specific model
5. 5Promote
6. 6Prediction API

05

Keep responsibilities clear

Your application remains responsible for:

• User experience
• Business workflow
• Customer records
• Permissions
• Actions after the prediction

StayCharted AMT handles:

• Training data
• Model training
• Model versions
• Prediction
• Prediction confidence
• Model API access

Your application manages the workflow. StayCharted AMT supplies the prediction.

06

Start with exports, integrate when you're ready

An integration doesn't need to be the first step. You can first export historical customer data, train a model in StayCharted AMT and determine whether the prediction is useful. Once the use case is proven, connect the same model to your application's workflow through the API. That creates a practical path: Export → Train → Test → Integrate → Automate