AI-Driven Document Management Setup
Budget: ₹37,500 – ₹75,000 INR
I am ready to move from an unstructured file share to a true Document Management System built on an AI-ML backbone. This first assignment focuses on turning my three-phase outline into reality while leaving space for future growth.
Phase 1 – Foundation
• Remove duplicates from the current repository and deliver a clean master copy.
• Establish a straightforward folder hierarchy (Finance, HR, Sales, Projects, etc.) with a clearly separated archive for dormant content.
Phase 2 – Structured Management
• Introduce simple, consistent version control so the latest copy lives in the working folder and previous iterations move automatically to the archive.
• Apply intuitive labels such as year, client and department to every file.
• Document storage rules that anyone on the team can follow without training.
Phase 3 – AI Expansion
The platform of choice is AI-ML, not a traditional Windows, Mac or purely web installation. The intelligence layer must handle:
• Auto-classification of documents,
• De-duplication of files, and
• Smart search with instant summarization.
The system will eventually need to ingest every common business file—text documents, spreadsheets, presentations and more—so keep extensibility in mind when selecting tools or writing code.
Deliverables for this sprint
1. A cleaned and reorganised repository reflecting Phases 1 and 2.
2. A concise technical brief (architecture diagram, proposed tech stack, libraries or SaaS suggestions) showing how the AI layer in Phase 3 will plug in.
3. A maintenance schedule template that recommends periodic reviews and automated archive updates.
Acceptance criteria
• No duplicate files remain after migration.
• Folder structure and labelling rules are documented and demonstrably applied.
• Technical brief clearly maps the chosen AI-ML components to the required auto-classification, de-duplication and smart-search tasks.
If you have hands-on experience with file-system cleanup, metadata design and building ML pipelines for document understanding, I’d love your help to get this off the ground.
Phase 1 – Foundation
• Remove duplicates from the current repository and deliver a clean master copy.
• Establish a straightforward folder hierarchy (Finance, HR, Sales, Projects, etc.) with a clearly separated archive for dormant content.
Phase 2 – Structured Management
• Introduce simple, consistent version control so the latest copy lives in the working folder and previous iterations move automatically to the archive.
• Apply intuitive labels such as year, client and department to every file.
• Document storage rules that anyone on the team can follow without training.
Phase 3 – AI Expansion
The platform of choice is AI-ML, not a traditional Windows, Mac or purely web installation. The intelligence layer must handle:
• Auto-classification of documents,
• De-duplication of files, and
• Smart search with instant summarization.
The system will eventually need to ingest every common business file—text documents, spreadsheets, presentations and more—so keep extensibility in mind when selecting tools or writing code.
Deliverables for this sprint
1. A cleaned and reorganised repository reflecting Phases 1 and 2.
2. A concise technical brief (architecture diagram, proposed tech stack, libraries or SaaS suggestions) showing how the AI layer in Phase 3 will plug in.
3. A maintenance schedule template that recommends periodic reviews and automated archive updates.
Acceptance criteria
• No duplicate files remain after migration.
• Folder structure and labelling rules are documented and demonstrably applied.
• Technical brief clearly maps the chosen AI-ML components to the required auto-classification, de-duplication and smart-search tasks.
If you have hands-on experience with file-system cleanup, metadata design and building ML pipelines for document understanding, I’d love your help to get this off the ground.