AI-Optimized Document Conversion Service Design Document

Generated: 2025-07-22 09:28 UTC
Status: Complete
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Overview

This document outlines the design for an AI-optimized document conversion service (code-name "MarkdownifyAI") that converts complex business documents into clean, AI-ready Markdown with rich metadata.

1. Purpose

Provide developers and data teams with the fastest, simplest way to convert complex business documents—including macro-enabled spreadsheets—into clean, AI-ready Markdown plus rich metadata. The service (code-name "MarkdownifyAI") focuses on minimal configuration, one-endpoint access, and output specifically structured for Retrieval-Augmented Generation (RAG) workflows.

2. Background / Problem Statement

Large-language-model pipelines need source documents broken into semantically meaningful Markdown chunks . Existing converters rarely handle spreadsheets with formulas, macros, or multiple sheets , and few attach metadata required for token-efficient chunking . Users are forced to maintain brittle scripts or use heavyweight SDKs .

3. Goals & Success Metrics

*Structural accuracy measured against golden Markdown snapshots .

4. Target Users

• Data Engineers / ML Ops – need pipeline-ready Markdown
• LLM Integrators / Consultants – rapid POCs without custom ETL
• Technical Writers – export source docs to Markdown knowledge bases

5. Competitive Landscape (abridged)

Opportunity: No existing service simultaneously 1) evaluates formulas/macros, 2) outputs AI-optimized Markdown with metadata, and 3) offers a zero-config API .

6. Key Differentiators

1. Macro/Formula Evaluation – run spreadsheets in a sandbox to emit resolved values and optionally inline original formulas as comments
2. AI-Optimized Output – clean Markdown with YAML front-matter (source path, author, page, tokens)
3. One-Endpoint API – POST /convert with auto-type detection; synchronous stream or async job
4. Smart Chunking – optional chunk=true returns folders of ≤2 k-token chunks with hierarchy preserved
5. Developer Ergonomics – CLI (markdownify file.docx) and typed SDKs

7. User Stories (sample)

• DE-01 As a data engineer, I POST a 4-sheet .xlsm and receive a ZIP containing four .md files with computed values so I can upload them to my vector database
• WR-02 As a technical writer, I drag-and-drop a PDF and download a Markdown file with images extracted to /assets

8. Functional Requirements

• FR-01 Accept DOCX, PDF, PPTX, XLSX/XLSM, CSV, HTML, TXT
• FR-02 Evaluate spreadsheets including VBA macros in a secure VM
• FR-03 Extract and relink images, charts, and embedded objects
• FR-04 Generate YAML front-matter with configurable keys
• FR-05 Provide zip bundle or tar.gz streaming
• FR-06 Offer Webhooks for async jobs

9. Non-Functional Requirements

• Performance: < 5 s P50 for ≤10 MB files
• Scalability: 100 concurrent jobs per tenant
• Security & Compliance: Data encrypted at rest; auto-purge 30 min; ISO 27001 / SOC2 roadmap
• Reliability: 99.9 % monthly uptime SLA

10. API Specification (high-level)

POST /convert
Headers: Authorization, Accept: application/zip
Body:
  file (binary)
  options: {
    output_format: "markdown",
    chunk: boolean,
    target_tokens: int,
    include_formulas: boolean
  }
Response:
  200 application/zip  (sync)
  202 Location: /jobs/{id} (async)

11. Architecture Diagram

12. Metrics & Analytics

• Conversion success rate (automatic alert <95 %)
• Median processing time per MIME type
• Token count distribution for outputs

13. Pricing & Packaging (draft)

14. Roadmap (MVP)

15. Risks & Mitigations

16. Open Questions

1. Support for epub input?
2. Automatic language detection & code block highlighting?
3. Do we include Docx comments as footnotes?

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