SANDRA | Semi-Automatic Network Data Retrieval Assistance | Data Management Framework
Year: 2026
Type: article
Abstract: 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. DOI: 10.5281/zenodo.18439730 | SHA-512: 0a96f0475c5ebd7bcb042edf39c56a83543d2057997c8db7249521a8490b41b9b1b0bc31553a02b5bf80c2863a21a31ac283c79fbafe61cee6496c3d5f8805e4 Core Methodology: 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. Technical Approach: - 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 Use Cases: - 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 Educational Value: 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. Project Ecosystem: 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. Video Artifact: https://youtu.be/D1Zalte47dI More Information: sunshynelabs.com/sandra ⚠️ IMPORTANT: All outputs must be reviewed by human operators prior to production use.
Authors: Sunshyne Labs, Juliano, Jerome
Language: English
Topic: Web Data Mining and Analysis
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7128406403
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