You will get OpenClaw /n8n Workflows | LLM Services | Voice AI | Full-Stack AI
Project details
You will get a production-ready AI chatbot built end-to-end — from architecture to deployment — that actually understands your business and converts conversations into outcomes.
I'm a bioinformatics AI engineer with deep experience building agentic AI pipelines, RAG systems, and LLM integrations for real-world use cases. I don't just plug into ChatGPT — I architect chatbots that use your knowledge base, integrate with your existing tools (CRM, Slack, WhatsApp, your website), and scale reliably.
What you get:
• Custom chatbot trained on your data (PDFs, websites, databases)
• Choice of LLM (OpenAI, Claude, Gemini, or open-source)
• RAG pipeline with vector database for accurate, source-cited answers
• Clean integration into your website, app, or messaging platform
• Full deployment on your preferred cloud (AWS, Vercel, your server)
• Documentation and a handover session so your team can maintain it
I work transparently — daily updates, clear milestones, and no scope surprises. Most projects ship within 14–20 days. If you're not 100% satisfied, I'll revise until you are.
Message me with your use case and I'll reply within 24 hours with a scoped plan.
I'm a bioinformatics AI engineer with deep experience building agentic AI pipelines, RAG systems, and LLM integrations for real-world use cases. I don't just plug into ChatGPT — I architect chatbots that use your knowledge base, integrate with your existing tools (CRM, Slack, WhatsApp, your website), and scale reliably.
What you get:
• Custom chatbot trained on your data (PDFs, websites, databases)
• Choice of LLM (OpenAI, Claude, Gemini, or open-source)
• RAG pipeline with vector database for accurate, source-cited answers
• Clean integration into your website, app, or messaging platform
• Full deployment on your preferred cloud (AWS, Vercel, your server)
• Documentation and a handover session so your team can maintain it
I work transparently — daily updates, clear milestones, and no scope surprises. Most projects ship within 14–20 days. If you're not 100% satisfied, I'll revise until you are.
Message me with your use case and I'll reply within 24 hours with a scoped plan.
AI Development Type
Knowledge RepresentationAI Tools
Azure Machine Learning, BigDL, Deeplearning4j, Google AutoML, MLflow, NVIDIA AI Platform, OpenCV, PyBrain, PyTorch, TensorFlowAI Development Language
PythonWhat's included $1,500
These options are included with the project scope.
$1,500
- Delivery Time 25 days
- Number of Revisions 5
- AI Model Integration
- Detailed Code Comments
- Knowledge Graph
- Model Documentation
- Ontology
- Source Code
- Taxonomy
Optional add-ons
You can add these on the next page.
Additional Revision
+$20About Khalil
AI Solutions Engineer | LLM Integration, RAG, Voice AI, Web & Mobile
Islamabad, Pakistan - 5:37 am local time
Most "AI developers" can wire up a ChatGPT API call. Fewer can architect a chatbot that handles your customers reliably, integrates with your CRM, retrieves accurate answers from your knowledge base, and scales without breaking. That's the work I do.
𝗪𝗵𝗮𝘁 𝗜 𝗯𝘂𝗶𝗹𝗱:
✅ LLM Chatbots & RAG Systems — Custom AI chatbots powered by OpenAI, Anthropic Claude, Gemini, or open-source models (Llama, Mistral). Full Retrieval-Augmented Generation pipelines with vector databases (Pinecone, Weaviate, Qdrant, pgvector) so the bot answers from your actual data — not hallucinations.
✅ Voice AI Assistants — Real phone agents using Vapi, Retell AI, ElevenLabs, Deepgram, and Twilio. Inbound support, outbound sales, appointment booking, lead qualification — fully integrated with CRMs (HubSpot, Salesforce) and calendars (Google, Calendly).
✅ Agentic AI & Workflow Automation — Multi-agent systems with LangGraph, CrewAI, and LangChain. Workflow automation on n8n, Make, and Zapier. AI agents that reason, plan, use tools, and complete real tasks autonomously.
✅ Full-Stack AI Apps — End-to-end web and mobile apps with AI built in. React, Next.js, FastAPI, Node.js, React Native, Flutter. Deployed to AWS, Vercel, or your own infrastructure.
𝗠𝘆 𝗯𝗮𝗰𝗸𝗴𝗿𝗼𝘂𝗻𝗱:
I work as a bioinformatics AI researcher building agentic AI and causal AI pipelines for production research environments. I manage collaborative GitHub workflows, AWS EC2 infrastructure, and shipped systems used by real teams. This isn't a side hobby — I architect AI systems professionally every day.
𝗛𝗼𝘄 𝗜 𝘄𝗼𝗿𝗸:
🔹 Discovery call to understand what you actually need (not what's trendy)
🔹 Clear technical proposal with realistic timelines and tradeoffs
🔹 Daily updates and milestone-based delivery
🔹 Clean, documented, production-ready code that you own 100%
🔹 Handover session so your team can maintain it independently
𝗧𝗲𝗰𝗵 𝘀𝘁𝗮𝗰𝗸:
AI/ML: OpenAI · Anthropic Claude · Gemini · LangChain · LangGraph · CrewAI · Hugging Face · Ollama · vLLM
Vector DBs: Pinecone · Weaviate · Qdrant · Chroma · pgvector
Voice: Vapi · Retell · ElevenLabs · Deepgram · Twilio
Automation: n8n · Make · Zapier · LangGraph workflows
Backend: Python · FastAPI · Node.js · Express
Frontend: React · Next.js · Tailwind · React Native · Flutter
Cloud: AWS · GCP · Vercel · Docker
𝗪𝗵𝗮𝘁 𝘆𝗼𝘂'𝗹𝗹 𝗴𝗲𝘁:
Honest communication. Clean architecture. Working systems. No vendor lock-in. No half-finished projects. No surprises on the invoice.
If you have a real AI problem to solve — send me a message describing your use case. I'll reply within 24 hours with a scoped approach and an honest assessment of whether I'm the right fit.
Steps for completing your project
After purchasing the project, send requirements so Khalil can start the project.
Delivery time starts when Khalil receives requirements from you.
Khalil works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & requirements
We review your use case, target users, knowledge sources, and integration needs. I'll send a brief Loom video walkthrough confirming the scope and asking any clarifying questions.
Architecture & approach
I share a short technical plan: which LLM, vector database, framework (LangChain/LangGraph), and deployment platform I'll use, plus an estimated timeline broken down by milestone.