Overview Documentation Methodology Use Cases 🧪 Sandbox

NLP Document Intelligence Methodology

This methodology explains how the sandbox converts selected documents into searchable intelligence while minimising data movement. The design is intentionally browser-first: extract locally, chunk locally, retrieve locally, and answer only from selected document scope.

Data protection principle: for this portfolio demo, uploaded document content is analysed in the browser. The project code does not send the file to a backend server or external AI API. This minimises data movement and demonstrates a privacy-aware AI workflow.

Define the document intelligence problem

Many organisations hold useful knowledge inside long PDFs, Word reports, agreements, evaluations and operational notes. The problem is that users often need answers quickly, but manually reviewing bulky documents is slow, inconsistent and difficult to audit.

Method decision: build an interactive assistant that answers from selected documents, not from a generic knowledge base. This prevents confusion and makes every answer more relevant to the user’s selected source.

Keep documents local in the browser

The sandbox processes uploaded files in the browser session. PDF and DOCX text extraction happens client-side, then the resulting text is kept in memory for the current session. The document is not posted to a server by the portfolio code.

Security benefit: minimising document movement reduces unnecessary exposure of sensitive information. For production, this same approach can be extended with locally bundled parsing libraries, private-cloud deployment, encryption, RBAC, retention rules and audit logging.

Extract readable text

The tool reads supported document types and extracts text for analysis. PDF files are parsed page by page, DOCX files are converted to raw text, and TXT / MD / CSV / JSON files are read directly.

The interface shows processing states so users understand what is happening: reading file, extracting text, chunking content, vectorising chunks and document ready for querying.

Chunk the document for retrieval

After extraction, the document is broken into smaller searchable chunks using sentence and paragraph boundaries. This makes it easier for the assistant to retrieve focused evidence instead of scanning the entire document as one block.

Why it matters: chunking improves relevance, supports source traceability and allows the tool to show which retrieved sections informed the final answer.

Create lightweight vector-style retrieval

The sandbox uses tokenisation and relevance scoring to simulate a RAG retrieval layer. Query terms are matched against chunk terms, then intent-aware boosts are applied for summary, risks, recommendations, impact, ROI and comparison questions.

In production, this layer can be replaced or expanded with embeddings, a vector database, hybrid search and re-ranking. The browser demo intentionally keeps the logic lightweight so users can experience the workflow without backend infrastructure.

Generate structured, human-friendly answers

Answers are organised like a practical ChatGPT-style assistant response: short answer first, then key points, numbered steps, risks, recommendations, ROI logic or comparison sections depending on the question.

Response design: the assistant clearly states the selected document scope, formats information with headings and bullets, and ends with a concise conclusion. This makes the output easier to read for donors, recruiters, business users and decision-makers.

Show evidence and reduce hallucination

The assistant only answers from the selected document scope. Retrieved evidence chips show which source chunks were used, and if no document is selected, the assistant asks the user to select a source instead of giving a generic response.

Quality control: source scoping, explicit answer banners, evidence retrieval and conservative fallback responses help reduce unsupported answers.

Production extension methodology

To move this from portfolio sandbox to production, the recommended approach is:

Private deployment: host the app in a controlled environment with HTTPS and access control.
Local parsing: bundle document parsers locally instead of loading from CDN where strict data governance is required.
Secure storage: store extracted text and embeddings only when users explicitly need persistence.
Access governance: apply role-based permissions by document, team, project and organisation.
Audit trail: log who uploaded, queried and accessed document intelligence outputs.
Model governance: add human review, citation checking, sensitive-data controls and retrieval evaluation.

📄 Full Documentation 🤖 Open Sandbox →