AI systems
AI Chatbot, RAG and MCP Server Developer in Pakistan
I build chatbots and agents that sit on a real product, not on a blank prompt. The useful version can look up an order, cite a policy, or call a tool you already trust, and it can fail in a way a human can see. I am Aamir Ali, in Lahore, Pakistan. The production example I can talk about in public is the REConnect support chatbot, which handles about 90% of support tickets for a platform used by 15,000+ agents.
What I build
Three kinds of systems show up in this work. A support chatbot grounded in your help content and product data, with retrieval so the model is not inventing policy. An internal agent that calls tools: search a record, open a ticket, draft a reply a person still sends. An MCP server that exposes those tools to whatever client your team uses, with the same permission checks the product already has. I have built the third of those next to REConnect, along with the agents that take support load off the team.
I refuse the demo that only works while I am typing. The retrieval corpus has an owner and a refresh path. Tool calls are allow-listed. The model does not get a database password and a wish. Logs show the retrieved passages and the tool result, so when an answer is wrong you can see why. If the task is a refund, a permission change, or anything that moves money, the agent prepares the action and a person confirms it.
This sits on top of ordinary engineering. The chatbot is another client of your API. If the API is Laravel or Node, I use that. I do not stand up a second source of truth inside a vector database and hope it matches production. Vectors are an index. The product database remains the record.
Relevant projects
REConnect is the case study to read. It is an MLS data platform for 15,000+ agents. Support was a real queue, not a hypothetical. The agents and MCP-based chatbot now handle about 90% of those tickets. The platform itself is Laravel and React, which matters: the bot is only as good as the tools and the data underneath it. Tour27 is the counterexample of a product with structured content, listings and itineraries, which is the kind of corpus a retrieval bot can use without scraping the public web and hoping.
- REConnect case studySupport chatbot and MCP server on a live MLS platform, about 90% of tickets.
- Tour27 case studyStructured product content, the kind of source a retrieval system can trust.
Stack
The application side is Node.js or Laravel, plus the product API the agent must call. Retrieval is a RAG pipeline: chunking that respects the document, embeddings, and a store I can rebuild. The agent layer uses tool calling with explicit schemas. MCP is how I expose those tools to a client when the team wants a standard server rather than a one-off plugin. I keep prompts in the repository, reviewed like code. Hosting is Docker on AWS, or Kubernetes if that is already how the platform runs. I pick a model the team can pay for and replace, and I do not hard-wire the product to a single vendor's console.
- RAG over your content, with a rebuildable index
- Tool calling against your existing API
- MCP servers and clients
- Node.js or Laravel as the application host
- Logs for retrieved context and tool results
- Human confirmation for money, access, and irreversible changes
Process
I start with twenty real questions your support team already answers, plus the documents or APIs that hold the truth. The first milestone is a bot that answers that set and shows its sources, measured against those questions, not against a vibe. Then we add tools one at a time and decide which actions stay human.
You see failures, not only a satisfaction score. I work from Lahore (UTC+5) and overlap US East Coast mornings, so we can review a bad answer while your support lead is online. A new inquiry gets a reply within one business day.
Pricing and engagement
A grounded chatbot with a defined corpus is a fixed build. An MCP server plus ongoing tuning is closer to a retainer, because the tools and the documents will change. I will not sell a monthly token package that hides engineering. Email aamirallidev@gmail.com with the job the bot should do, where the answers live today, and what it must never do alone. The homepage contact section works as well. Remote from Pakistan, overlapping US East Coast mornings.
FAQ
Do you build a chatbot that only answers from a PDF?
I can start there, but production bots also need a refresh path and, usually, tools that read live product data. A stale PDF with no owner becomes a confident wrong answer.
What is an MCP server in this context?
A server that exposes your tools and data to an agent client using the Model Context Protocol, with the same permissions your product already enforces. It is not a second, looser admin API.
Can the bot issue refunds or change account access?
It can prepare the request. A person confirms anything that moves money, changes access, or cannot be undone. I will not ship an agent with a blank check.
Which model do you require?
None by brand. I use a model the team can pay for and swap, and I keep prompts and tool schemas in the repository so a model change does not mean a rewrite.
How do I hire you for an AI chatbot?
Email aamirallidev@gmail.com with sample questions, where the true answers live, and the actions the bot must not take alone. I am in Lahore and I typically reply within one business day.
Related services
Contact
Email aamirallidev@gmail.com or send a note from the contact section. I typically reply within one business day.