You will get a RAG & ChatBot Pipeline with Reranking , Evaluatoins for production Grade
Rising Talent
Project details
A powerful LLM is useless if it can't find the right information. I build RAG systems with hybrid search, smart chunking, reranking, and RAGAS evaluation using Pinecone, Qdrant, Hugging Face, and Cohere so your users get accurate, cited answers, not confident guesses.
Why work with me?
✓ Pipelines engineered for 95%+ grounded-answer accuracy
✓ Full documentation handover
✓ Ongoing support & revisions included
✓ You own your vector DB & API keys
✓ RAGAS/DeepEval evaluation reports included
What I deliver
✓ Chunking matched to your data: semantic, recursive, or LLM-based
✓ Hybrid search: dense vectors + BM25 + metadata filtering
✓ Source citations on every response, verifiable not plausible
✓ Reranking (Cohere) for sharper context
✓ Query enhancement: multi-query, HyDE, context memory
✓ LLM flexibility: GPT-4o, Claude, or your provider
You choose
✓ Vector DB: Pinecone, Qdrant, Chroma, or your stack
✓ Interface: chat UI, FastAPI, or embedded widget
Perfect for SaaS products and support teams that need trustworthy AI answers.
Ready to eliminate hallucinations? Message your use case and I'll map out a citation-backed RAG architecture.
Why work with me?
✓ Pipelines engineered for 95%+ grounded-answer accuracy
✓ Full documentation handover
✓ Ongoing support & revisions included
✓ You own your vector DB & API keys
✓ RAGAS/DeepEval evaluation reports included
What I deliver
✓ Chunking matched to your data: semantic, recursive, or LLM-based
✓ Hybrid search: dense vectors + BM25 + metadata filtering
✓ Source citations on every response, verifiable not plausible
✓ Reranking (Cohere) for sharper context
✓ Query enhancement: multi-query, HyDE, context memory
✓ LLM flexibility: GPT-4o, Claude, or your provider
You choose
✓ Vector DB: Pinecone, Qdrant, Chroma, or your stack
✓ Interface: chat UI, FastAPI, or embedded widget
Perfect for SaaS products and support teams that need trustworthy AI answers.
Ready to eliminate hallucinations? Message your use case and I'll map out a citation-backed RAG architecture.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI ChatbotAI Development Language
PythonAI Tools
Hugging Face, PyTorch, Streamlit, TensorFlowAI Models
ChatGPT, GPT-3, GPT-4, GPT-J, OpenAI CodexWhat's included
| Service Tiers |
Starter
$100
|
Standard
$400
|
Advanced
$1,500
|
|---|---|---|---|
| Delivery Time | 5 days | 10 days | 14 days |
Number of Revisions | 2 | 5 | |
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 Hamza
Agentic AI Engineer | Voice Agents | RAG | NLP | Claude Code | n8n
Rawalpindi, Pakistan - 10:41 am local time
I'm 𝐇𝐚𝐦𝐳𝐚 𝐒𝐚𝐥𝐞𝐞𝐦, an Agentic AI Automation Engineer specializing in 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐂𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐕𝐨𝐢𝐜𝐞 𝐀𝐈, 𝐑𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥-𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐞𝐝 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐨𝐧 (𝐑𝐀𝐆), 𝐌𝐮𝐥𝐭𝐢-𝐚𝐠𝐞𝐧𝐭 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 ( 𝐎𝐩𝐞𝐧𝐂𝐥𝐚𝐰), 𝐚𝐧𝐝 𝐧𝟖𝐧 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧. I build with full testing, evaluation, and production-readiness baked in from day one not prompt-wrapped demos.
𝗪𝗵𝗮𝘁 𝗜 𝗰𝗮𝗻 𝗵𝗲𝗹𝗽 𝘄𝗶𝘁𝗵:
• Real Time Voice Agents & AI Receptionists
• Embeded in Website and API Voice Integration
• Telephony Systems and Conversational AI
• Production Ready RAG Pipelines (90%+ Retrieval Accuracy / Latest Framework Evaluations)
• Knowledge Base Design, Embeddings , Reranking and Design Clean Interactive UI.
• N8N Workflow Automation for Sales, Support and Ops
• Multi Agent Pipelines (OpenAI AgentBuilder / Claude Code / OpenClaw)
• CRM, Calendar and Database Integrations
• MCP Server Setup and Third-Party Tool Connectivity
◆𝗛𝗼𝘄 𝗶 𝘄𝗼𝗿𝗸
✓ Start with a deep dive into your business goals, existing workflows, and bottlenecks
✓ Deliver a detailed implementation plan architecture, tools, APIs, costs, and approach before any development begins
✓ Build and deploy according to the agreed scope and timeline
✓ Run full testing and evaluation for reliability and accuracy before delivery
✓ You review and request adjustments
✓ 1 month of post-launch support included on every project
◆ 𝗦𝗸𝗶𝗹𝗹𝘀 𝗮𝗻𝗱 𝘁𝗼𝗼𝗹𝘀
𝐕𝐨𝐢𝐜𝐞 𝐀𝐠𝐞𝐧𝐭𝐬: Retell AI + LiveKit + SDK + Twilio + Deepgram
𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 & 𝐑𝐀𝐆: LangChain + LangGraph + OpenAI + Claude + Gemini + MCP Servers
𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧: OpenAI Agent Builder & SDK + n8n + FastAPI + WebRTC + Tavus
𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬:Pinecone + Qdrant + FAISS + Supabase + PostgreSQL+ MongoDB Atlas
𝐂𝐨𝐝𝐢𝐧𝐠 𝐓𝐨𝐨𝐥𝐬/𝐂𝐋𝐈𝐬:Claude Code (agentic coding) + Skills + Plugins + Hooks + MCP servers + CLIs
𝐂𝐡𝐚𝐧𝐧𝐞𝐥𝐬 : Whatsapp + Telegram + Discord other
𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐢𝐞𝐬: SaaS, Healthcare, Real Estate, Legal, Ecommerce, Coaching
Tell me which one is hurting most right now cold leads, repetitive support questions, or buried docs and I'll reply within 4 hours with how I'd actually solve it for your setup.
Steps for completing your project
After purchasing the project, send requirements so Hamza can start the project.
Delivery time starts when Hamza receives requirements from you.
Hamza works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & architecture
I review your data sources, use case, and infrastructure. We agree on the vector DB, embedding model, and LLM for your custom RAG pipeline.
Document ingestion & chunking
I build the parsing pipeline for your files (PDFs, DOCX, HTML, Markdown) with optimal chunk size, overlap, and preprocessing to maximize retrieval quality.