What data does Tekst store and how does the learning process work?

Tekst processes customer data using a streaming architecture: the content of emails, tickets, and cases is handled ephemerally and never stored permanently. This article explains what metadata Tekst does store, how the feedback loop works, and how models are retrained.

What Tekst stores vs. what it does not store

Tekst does not persist any PII, message content, or confidential information. All content is processed in real time and discarded after classification.

However, Tekst does store metadata that is essential for monitoring and improving model performance:

Prediction values: The category, tag, or assignment the model predicted.
Feedback signals: Whether an agent accepted or corrected the prediction (also called "relabeling").
Identifiers: Ticket/case IDs and integration IDs (no content).
Timestamps: When the prediction was made and when feedback was received.
Model metadata: Model version, confidence score, and accuracy metrics.

This metadata contains no message body text, attachments, customer names, or other personal data.

How the feedback loop works

The learning process relies on a real-time feedback loop between Tekst and the customer's platform (e.g., Salesforce, SAP CEC, Outlook):

Step 1: Real-time classification

When a new ticket or email arrives, the customer's platform sends an event to Tekst. Tekst reads the content via API, runs its AI models, and writes the predictions (category, assignment, priority) back to the platform. The content is processed in memory and not stored. Only the prediction metadata is persisted.

Step 2: Feedback capture

When an agent reviews the ticket and changes a field that Tekst predicted (e.g., selects a different category), the customer's platform sends an update or closure event back to Tekst. Tekst records only the final metadata values and compares them to its original prediction. This tells Tekst whether it was correct or whether the agent made a correction.

Step 3: Model retraining

The accumulated feedback metadata is used to evaluate model accuracy. Retraining is not scheduled on a fixed cadence (e.g., nightly). Instead, it is triggered by two conditions:

Accumulated corrections: Enough new feedback has been collected to meaningfully improve the model.
Accuracy drop: Automated monitoring detects that model performance has declined.

During the initial onboarding phase, retraining happens more frequently to rapidly build accuracy. Once the model is stable, retraining occurs only when one of the above conditions is met. Retraining typically takes between 1 and 12 hours depending on model complexity.

Why update/closure hooks are needed

The webhook or API notification that the customer's platform sends back to Tekst (e.g., "case updated" or "case closed") is critical for the learning process. Without it, Tekst would have no way to know whether its predictions were accepted or corrected by the agent. These hooks transmit only metadata (final field values, IDs, timestamps), not ticket content.