Enterprise Automations

1. Employee Onboarding Automation

The Challenge

THR teams often spend significant time reviewing and validating new hire information manually. Each onboarding request can take up to 15 minutes, slowing down the hiring process and creating bottlenecks during high-volume recruitment periods.

The Solution

Developed an intelligent onboarding assistant that streamlines the validation process for new hires. The system automatically processes submitted employee data and provides a preliminary decision, allowing HR teams to focus only on final confirmation instead of manual verification from scratch. This reduced the average review time from ~15 minutes to ~2 minutes per employee, significantly accelerating the hiring workflow while maintaining accuracy and consistency.

Key Impact

- reduction 85% in manual review time.
- Faster hiring decisions during high-volume periods.
- Reduced human error in data validation.
- HR shifts from data entry → decision-making.

Technologies Used

- n8n: Central orchestration engine.
- API Integrations: Google Workspace, Slack API, HR Software.

2. Medical Report & Appointment Classification

The Challenge

Healthcare providers receive a high volume of medical reports and appointment requests daily. Manually reviewing these documents to determine validity, urgency, and required action is time-consuming, error-prone, and can delay critical decisions.

The Solution

Developed an AI-powered classification and decision-support system that processes medical reports and appointment requests automatically. The system extracts and analyzes OCR text, applies rule-based validation using predefined medical criteria, and leverages an AI agent to determine the appropriate outcome (Accepted, Rejected, or Needs Human Action). It also validates appointment dates against real-time conditions to ensure accuracy and relevance. All results are structured and logged, enabling seamless tracking and reducing manual review effort.

Technologies Used

- OCR Engine: Medical text extraction
- AI Agent: Context understanding and classification.
- n8n: Workflow orchestration and automation.
- Rule-based Validation: Deterministic validation logic.

3. Business Card

The Challenge

At networking events and exhibitions, professionals often collect a large number of business cards in a short time. After the event, it becomes difficult to remember who each card belongs to, leading to lost context, missed opportunities, and ineffective follow-ups

The Solution

Developed an automated business card digitization bot that solves the problem of lost context. Users can capture or upload business card images during or after events, and the system extracts key details (Name, Company, Position, Contact Info) using OCR. Each entry is stored in Google Sheets with structured fields, along with an optional note added by the user at the time of upload — helping preserve context like where the contact was met or what was discussed. This ensures every contact remains clear, organized, and actionable for future follow-ups.

Technologies Used

- OCR: For image-to-text extraction.
- n8n : Workflow automation.
- Google Sheets: For storing structured data.

4. Fitness & Sport: Automated Macronutrient Tracking Bot

The Challenge

For athletes and gym-goers, manual calorie counting is the biggest barrier to hitting their goals. Most apps require tedious manual entry, leading to "tracking fatigue" and inaccurate data, which hinders muscle gain or fat loss progress.

The Solution

I developed a high-speed automation pipeline via a Telegram Bot specifically for fitness enthusiasts. Instead of searching through databases, the user simply sends a photo of their meal or a quick voice/text note to the bot.

Technologies Used

- Telegram Bot API: For a mobile-first user interface.
- n8n To orchestrate the flow between the bot, AI, and database.
- Gemini AI: For image recognition and nutritional estimation.
- Google Sheets: For storing structured data.

5. Customer Experience: AI-Powered WhatsApp Sales & Support Agent

The Challenge

Businesses struggle with high inquiry volumes and slow response times, leading to lost leads. Traditional chatbots often frustrate users with rigid, pre-defined menu options that fail to address specific, natural language questions.

The Solution

I engineered a sophisticated AI Agent integrated directly into WhatsApp. Unlike standard bots, this agent acts as a virtual employee. It is powered by a Large Language Model (LLM) via n8n, allowing it to understand complex customer intent, answer product questions, and provide personalized support 24/7.

The workflow uses n8n. It intercepts WhatsApp messages and processes them through AI. The AI logic gate uses Gemini or OpenAI. The system then responds with human-like accuracy. The agent can connect to a database to provide specific company information or check order statuses.

Technologies Used

- WhatsApp Business API: For official, scalable communication.
- n8n: To manage the logic, API calls, and "Agentic" workflows.
- AI Models (Gemini/GPT): For natural language understanding (NLU) and generating human-like responses.
- Webhooks & JSON: For real-time data exchange between WhatsApp and the AI.