AI Data Pipeline

Understanding how Xemlok prepares information for analysis is essential for understanding the reliability of its results. The AI model does not work with raw screenshots or unstructured values — it receives a clean, normalized dataset constructed in real time.

Overview of the Pipeline

The pipeline consists of four major phases:

1

Data Extraction

Collect raw inputs from sources such as screenshots, logs, and structured exports. Extraction identifies key fields and captures raw values for downstream processing.

2

Data Normalization

Clean and standardize extracted values: normalize units, parse dates, standardize text fields, and validate numerical ranges so the dataset is consistent and machine-readable.

3

Context Building

Enrich normalized data with contextual metadata (e.g., user/session info, timestamps, source identifiers) and assemble related records to form a coherent view for the model.

4

AI Request Execution

Prepare the final payload and invoke the AI model with the normalized dataset and context. Handle responses, post-process model outputs, and integrate results back into the application.

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