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OMS

Role
MVP development
Duration
6 Weeks
Start
Jul 2025

Brand overview

OMS Chat is an advanced conversational backlink discovery and purchasing system built to provide a clean, user-friendly experience over one of the largest backlink databases in India. The platform is designed to simplify access to massive datasets while maintaining speed, clarity, and ease of use for both enterprise and power users.

The system’s core innovation lies in its chat-first, AI-driven workflow. Users can search, analyze, filter, and purchase backlink data entirely through natural language conversation—without navigating traditional dashboards or complex tables. Users can upload PDFs, and the AI automatically analyzes the content, extracts links, and applies relevant filters in real time, enabling a fully end-to-end buying journey inside chat.

  • Conversational search and purchasing over large backlink datasets
  • AI chat interface for discovery, filtering, cart, and checkout Vectorized RAG intelligence for accurate, contextual results
  • PDF parsing to extract links and auto-apply intelligent filters

Transforming backlink discovery
into a conversational
chat-native OMS experience.

01. Conversational Data

Designed a scalable OMS layer to power one of India’s largest backlink databases, optimized for chat-driven querying, fast retrieval, vector search, and real-time filter application through AI.

02. ⁠Chat-First UI/UX

Built a clean, modern messaging interface that replaces traditional tables and dashboards, allowing users to explore massive backlink datasets naturally through conversation with minimal cognitive load.

03. AI, RAG & PDF

Implemented a vectorized RAG pipeline with PDF parsing, enabling users to upload documents, auto-extract links, analyze context, and intelligently apply filters using natural language commands.

04. Purchasing, Automation

Enabled end-to-end buying directly inside chat from shortlist to cart to checkout while tracking user behavior and intent. Integrated n8n workflows to qualify leads, trigger Slack and email notifications, and continuously improve AI performance through usage patterns.