{
  "abstract": "Automating customer support tickets in the logistics domain requires high semantic understanding to accurately interpret unstructured user queries, alongside strict backend validation to ensure data security and integrity. This preprint presents the Enterprise-AI-Logistics-Classifier-Automated-Support-Email-Engine, an advanced hybrid pipeline that integrates a fine-tuned RoBERTa (Robustly Optimized BERT Pretraining Approach) deep learning model with deterministic rule-based checks and SQLite database lookups.\n\nThe system processes incoming customer messages, performs deep contextual intent classification to categorize queries (such as missing, malformed, invalid, or mismatched parcel numbers), validates tracking information against backend parcel records, and dynamically generates professional, personalized reply templates. Evaluated on a structured test dataset (test.xlsx), the hybrid RoBERTa architecture demonstrated exceptional performance, achieving 100% classification accuracy, precision, recall, and F1-score, with a competitive processing latency (~0.57 seconds per ticket). This repository provides a fully reproducible framework for scalable, AI-driven enterprise customer service operations.\n\nKey Features & Technical Architecture\n\nRoBERTa-Powered Semantic Classification: Utilizes a transformer-based RoBERTa model to capture deep contextual representations of customer support inquiries, moving beyond simple keyword matching to accurately classify complex or ambiguous text inputs.\n\nHybrid Rule-Based Validation Layer: Combines deep learning predictions with deterministic constraints (e.g., checking empty fields, validating 8-digit parcel constraints) to mitigate model hallucinations and enforce structural logic.\n\nBackend Database Integration: Interacts with a SQLite database (parcel_db.sqlite) to cross-reference parcel numbers against real records, extracting customer identifiers and tracking statuses (In Transit, Delivered, Delayed).\n\nAutomated Template Engine: Automatically maps classification outcomes and live database states to professional response templates, ensuring rapid, personalized communication loops.\n\nOpen Science Alignment: Built for full transparency and reproducibility, providing training datasets (train.xlsx), evaluation datasets (test.xlsx), modular source code (main.py), and execution logs (results.xlsx).\n\nMethodology & Model Pipeline\n\nInput Ingestion & Tokenization: Raw customer support queries from test.xlsx are tokenized using the RoBERTa tokenizer to preserve sub-word semantics and contextual nuances.\n\nContextual Inference: The fine-tuned RoBERTa classifier evaluates the text sequence to predict primary intent categories (e.g., valid query vs. missing parcel number).\n\nDatabase & Rule Validation: The extracted identifiers pass through a multi-tier logic gate:\n\nMissing/Malformed Check: Handled via rule-based filters.\n\nDatabase Lookup: Queried against parcel_db.sqlite to handle valid matches, invalid numbers, or cross-customer mismatches.\n\nDynamic Response Assembly: Validated outcomes trigger the corresponding markdown/text reply template, injecting live tracking variables before writing the final audit trail to results.xlsx.\n\nEvaluation Metrics & Results\n\nMetrics Tracked: Accuracy, Precision, Recall, F1-Score, Confusion Matrix, and Inference Latency.\n\nPerformance: Achieved a 100% success rate across all core classification metrics on the evaluation dataset.\n\nEfficiency: Maintained an average latency of ~0.57 seconds per ticket (processing a 20-ticket batch in 11.36 seconds, translating to a throughput of ~1.76 tickets/second).\n\nKeywords\n\nRoBERTa; Transformer Models; Logistics Automation; Customer Support; Natural Language Processing; Hybrid AI Pipelines; Open Science\n\nRepository Structure & Software Details\n\nProgramming Language: Python\n\nCore Libraries: PyTorch, Transformers (Hugging Face / RoBERTa), Pandas, SQLite3\n\nRepository Link: GitHub - VasudevanS1906/Enterprise-AI-Logistics-Classifier-Automated-Support-Email-Engine",
  "abstract_shown": true,
  "affiliations": [
    "Independent Researcher"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Vasudevan Swornampillai",
      "orcid": "0009-0004-4933-771X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fv8m64dmt8h5d.bib",
  "cited_by": 0,
  "doi": "10.5281/zenodo.23465855",
  "fulltext_url": null,
  "id": "fv8m64dmt8h5d",
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  "landing_url": "https://zenodo.org/records/23465855",
  "language": "eng",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-19",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fv8m64dmt8h5d.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:21:17Z",
      "native_id": "23465855",
      "source": "zenodo",
      "url": "https://zenodo.org/records/23465855"
    }
  ],
  "subjects": [],
  "title": "Enterprise AI Logistics Classifier & Automated Support Email Engine",
  "title_text": "Enterprise AI Logistics Classifier & Automated Support Email Engine",
  "type": "preprint",
  "updated_at": "2026-11-02T12:21:17Z",
  "url": "https://research.draconitic.com/paper/fv8m64dmt8h5d",
  "venue": "Zenodo",
  "versions": []
}