SANDRA | Semi-Automatic Network Data Retrieval Assistance | Data Management Framework

SANDRA (Semi-Automatic Network Data Retrieval Assistance) is a human-in-the-loop data management & extraction framework for contact centers, designed to reduce manual handling of structured and semi-structured data, while preserving operator oversight (HITL).

 

This artifact is released as a citable publication.

 

This current version (v5) includes: updated process maps, schema exports, export-safe PDF artifacts, and provenance markers.

 

 

SANDRA uses a 3-phase workflow (Extract → Process → Clean) with LLM-assisted code generation. Users iterate prompts to produce deterministic Python scripts that extract structured fields (e.g., phone numbers, customer records) from enterprise datasets. Internal case studies report time savings of up to 95%; results vary by dataset and implementation.

 

- Iterative prompt engineering (Prompts 1–6 documented)

- LLM-assisted code generation: prototype prompt → Python script → extraction pipeline

- Mandatory human-in-the-loop (HITL) verification before production

- Machine-readable JSON schemas for attribution and IP protection

 

- Phone number extraction from CRM/data lakes

- Lean Six Sigma data collection

- Marketing analytics and campaign tracking

- Customer segmentation and churn prediction

- Integration with MADISON (Postcard Strategy) for personalized outreach

 

The case study documents failed iterations (Prompts 1–4) and successful refinements (Prompts 5–6) to teach prompt refinement. Includes commentary on "Human English vs. AI English" and the democratization of data work.

 

Part of the Sunshyne Labs ecosystem (MARY, MADISON, GABRIEL∞, Octopus Corpus, JACK, ANGEL).

 

License: CC BY-NC 4.0. Commercial use requires a separate licensing agreement. See embedded machine-readable disclaimer schema for complete terms.