You will get a production-ready RAG chatbot trained on your docs or website
Project details
I build RAG chatbots that reach production and stay reliable, not demo bots that fall over on the third question.
I'm the lead engineer of a live US software platform where I built an AI assistant from scratch, so I know what it takes to make one actually work: answers grounded in your content, with citations, so it doesn't make things up.
What you get:
A chatbot trained on your docs or website, using OpenAI or Gemini and a vector database
Retrieval with reranking so answers stay accurate and on-topic
Source citations on every answer
Clean source code, deployment, and a proper handover
Higher tiers add testing, guardrails, and monitoring, so you can see how it's performing and what it costs to run.
I work in clear steps: set up your knowledge base, build and tune the retrieval pipeline, then test and deploy. You review as we go.
Every project ships through senior review, so quality stays high.
Tell me what you want it to answer and I'll tell you honestly what's realistic. Send your docs or site link and a few example questions to start.
I'm the lead engineer of a live US software platform where I built an AI assistant from scratch, so I know what it takes to make one actually work: answers grounded in your content, with citations, so it doesn't make things up.
What you get:
A chatbot trained on your docs or website, using OpenAI or Gemini and a vector database
Retrieval with reranking so answers stay accurate and on-topic
Source citations on every answer
Clean source code, deployment, and a proper handover
Higher tiers add testing, guardrails, and monitoring, so you can see how it's performing and what it costs to run.
I work in clear steps: set up your knowledge base, build and tune the retrieval pipeline, then test and deploy. You review as we go.
Every project ships through senior review, so quality stays high.
Tell me what you want it to answer and I'll tell you honestly what's realistic. Send your docs or site link and a few example questions to start.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Hugging Face, PyTorch, TensorFlowAI Models
ChatGPT, GPT-3, GPT-Neo, LLaMA, OpenAI CodexWhat's included
| Service Tiers |
Starter
$400
|
Standard
$850
|
Advanced
$2,000
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 14 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | |||
Detailed Code Comments | - | ||
Image Upscaling | - | - | - |
MLOps | - | - | |
Model Deployment | - | ||
Model Documentation | - | ||
Model Monitoring | - | - | |
Model Testing & Optimization | - | ||
Model Tuning | - | - | - |
Natural Language Processing | |||
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | |||
Source Code |
Frequently asked questions
About Talal
AI Engineer | LLM, RAG & Agentic Systems
Islamabad, Pakistan - 3:53 am local time
I'm the lead engineer of a live six-service US InsurTech platform. I built its first AI assistant from scratch (OpenAI GPT-4 with RAG) in 2024, then led its rebuild into a LangGraph agentic system with 59 role-scoped tools on a Qdrant vector database, with model routing, cost controls, and guardrails. Around it I built the FastAPI microservices, a SvelteKit and TypeScript app with 277 endpoints, a BigQuery analytics pipeline, and the observability and security stack that keeps it all running.
That's the difference I bring. I don't just wire up an LLM and hand you a notebook. I ship the security, testing, tracing, and cost governance that keep an AI feature working once real users hit it.
What I can build for you:
RAG and agentic assistants that reach production: permission-aware retrieval, reranking, tool use, model fallback
AI added to an existing product, without a rebuild
A flaky AI feature made production-safe: evals, guardrails, tracing, cost control
Stack: Python, FastAPI, TypeScript and SvelteKit, LangChain and LangGraph, OpenAI and Gemini, Qdrant, Google Cloud, Docker.
For anything sizeable I'd suggest starting with a short scoping call so we map exactly what you need before you commit.
Steps for completing your project
After purchasing the project, send requirements so Talal can start the project.
Delivery time starts when Talal receives requirements from you.
Talal works on your project following the steps below.
Revisions may occur after the delivery date.
Scope and knowledge base setup
We agree on scope and sources, then ingest your content: cleaning, chunking, and embedding it into a vector database so it's searchable.
Build the retrieval and answer pipeline
I wire up retrieval with reranking and connect it to the LLM, with prompts tuned to answer from your content and cite its sources instead of guessing.