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Aditya Raj
AI Engineer | Conversational AI, Agents & Backend Automation
New to Contra
Aditya is ready for their next project!
What happens when your assistant turns into your creative partner?
This is what we built few months back, a Conversational AI Design Assistant that could generate designs using natural conversation. The goal was to bypass traditional prompt engineering and replace it with natural conversation
The experience was fulfilling, with learning failure and overcoming everyday challenges. Looking forward to share more such project
If anyone is working on exciting projects, and believe I can contribute in any capacity, let's have a chat
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Competitor Price Monitor | n8n + Google Sheets
Keeping track of competitor prices becomes repetitive when a retailer manages several product categories and regularly adds new SKUs.
I built this automation around TechNest Accessories, a simulated electronics retailer, with a practical brief: monitor comparable competitor products while keeping the solution affordable and easy to manage.
The budget shaped the approach. Product discovery and matching stayed manual: the retailer enters its own SKU, selling price and chosen competitor URL in Google Sheets. This keeps control over which products are compared and reduces the complexity of the build.
Once a listing is selected, the repeated checking is automated.
The workflow reads active listings, retrieves competitor prices and availability, and compares each result with both the retailer’s price and the previously recorded competitor price. It then updates the Current Prices tab and appends a timestamped record to Price History.
The retailer can manage everything from the sheet—add listings, update selling prices, or pause monitoring with a Yes/No dropdown.
The workflow also handles failed page requests and price extraction. It records the error, preserves the last valid price and continues to the next listing. A successful later check clears the error.
I tested the build with two Portronics charger listings, confirming price comparisons, historical records and recovery after a deliberately failed check. The workflow supports scheduled daily checks and manual runs.
The result is a working prototype that brings selected competitor prices into one place, highlights meaningful differences and preserves a record for review—while leaving product selection and pricing decisions with the retailer.
Built with: n8n, JavaScript, HTTP requests and Google Sheets.
Independent portfolio project based on a simulated client brief. Images show recorded test data and simplified views of the working system.
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How I Built My Own Telegram Lead Capture System in n8n.
The goal was simple: create a Telegram-based lead capture system that could collect enquiries in a structured way, remember where each user was in the conversation, validate the information, normalize budgets across different currencies, classify leads, and finally store everything cleanly in Google Sheets.
I come from a Python background, so writing the chatbot logic in code would have been the easier route for me. But for this project, I deliberately chose to build the conversational flow using native n8n nodes instead of relying on a Code node. I wanted to understand how state management, branching, routing, and multi-step conversations could be handled visually inside n8n.
I only used code later where it made more sense—validating lead data, parsing budget information, detecting currencies, converting values to USD, and classifying higher-value leads.
At the moment, this system does exactly what I need it to do. If the volume of leads grows in the future, I’d extend it further—for example, automatically sending myself an email notification whenever a high-ticket lead comes in.
For now, though, I wanted to keep it practical rather than over-engineer it.
Built with: Telegram, n8n, JavaScript, Google Sheets, n8n Data Tables, and a currency conversion API.
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Where most Sales Manager go wrong isn't lack of data, it's getting overwhelmed with numbers and targets. What actually matters is not numbers, but "why" behind it.
So we built an Agentic AI Sales Engine that sits on your Telegram, dissects the "why" behind what's happening in your sales team. It doesn't just throws number but tells you why someone is falling short while another pulling ahead, surfaces the operational bottlenecks underneath the numbers, and suggests what can actually be done better, all in real time at 0 infrastructure cost.
Problem:
A Sales Manager needed a fast way to check teams performance, calls, leads, conversions, pipeline value, but without opening spreadsheets or chasing manual reports. But raw numbers alone don't tell you what to do. Two reps can have identical conversion rates for completely different reasons. The manager needed something that could reason about the data, not just report it but by answering questions like "who needs attention today?" or "why is Rahul underperforming?"
What We Built
A fully automated Telegram bot, powered entirely by a self-hosted n8n workflow, that reads live data from Google Sheets and combines two layers: hard KPI reporting on demand, and an AI reasoning layer that interprets those numbers into a story a manager can act on.
The "Why Layer"
This is a part that makes it more than a dashboard, instead of just telling Rahul 12% conversion, the engine reasons over calls, leads pipeline and conversion patterns together and drives an analysis thereby providing the right suggestive next steps.
Skills Demonstrated
:Workflow automation & API orchestration (n8n)
:OAuth 2.0 debugging and Google Cloud API setup
:Data processing / aggregation logic (JavaScript in Code nodes)
:LLM integration with grounded, hallucination-resistant prompting
:Conversational bot design (Telegram Bot API)
:Building production-usable tools on a strict zero-cost budget
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