You will get a custom AI productivity workspace with RAG and data syncing
Top Rated
Top Rated
Project details
Stop drowning in scattered company data. I will build you a custom, enterprise-grade AI Workspace—a powerful alternative to Notion—supercharged by a Retrieval-Augmented Generation (RAG) engine.
While most developers just wrap ChatGPT around a basic chatbox, I build robust, full-stack systems. Depending on your tier, your platform will feature secure data syncing, seamlessly pulling in your Google Workspace, Microsoft 365, and AI meeting transcripts via automated background workers.
You will get a Notion-style, block-based text editor to organize projects. As you draft documents or sync files, the backend automatically indexes and vectorizes the text using PostgreSQL (pgvector) and OpenAI's embedding models.
You can ask the AI Chatbot questions like: "What did John say about the Q3 budget in yesterday's Zoom sync?" The RAG pipeline will instantly perform semantic vector searches across your personal data and stream a highly accurate answer.
Built with Python, FastAPI, PostgreSQL, and Docker, this is a production-ready application. Whether you need an internal company tool or the foundation for a SaaS startup, you will receive top-tier, scalable code.
While most developers just wrap ChatGPT around a basic chatbox, I build robust, full-stack systems. Depending on your tier, your platform will feature secure data syncing, seamlessly pulling in your Google Workspace, Microsoft 365, and AI meeting transcripts via automated background workers.
You will get a Notion-style, block-based text editor to organize projects. As you draft documents or sync files, the backend automatically indexes and vectorizes the text using PostgreSQL (pgvector) and OpenAI's embedding models.
You can ask the AI Chatbot questions like: "What did John say about the Q3 budget in yesterday's Zoom sync?" The RAG pipeline will instantly perform semantic vector searches across your personal data and stream a highly accurate answer.
Built with Python, FastAPI, PostgreSQL, and Docker, this is a production-ready application. Whether you need an internal company tool or the foundation for a SaaS startup, you will receive top-tier, scalable code.
AI Algorithms
Large Language ModelAI Applications
AI Chatbot, AI Content Creation, AIOps, Conversational AI, Natural Language Generation, Natural Language Understanding, Text RecognitionAI Development Language
PythonAI Tools
Azure OpenAI, Hugging FaceAI Models
ChatGPT, GPT-4What's included
| Service Tiers |
Starter
$1,500
|
Standard
$3,500
|
Advanced
$7,500
|
|---|---|---|---|
| Delivery Time | 14 days | 30 days | 45 days |
Number of Revisions | 2 | 4 | 6 |
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 |
Optional add-ons
You can add these on the next page.
Add Extra API Integration (e.g., Slack, Notion, Jira)
(+ 5 Days)
+$800
White-Glove Server Deployment
(+ 2 Days)
+$500Frequently asked questions
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WB
William B.
May 24, 2026
Contractor — Senior Web Scraping Engineer (Equipment Spec Data)
GT
Guhan T.
Oct 23, 2025
AI-Driven Automation
best developer
MM
Miye M.
Nov 19, 2024
Extract Scraping Content from Website
Hafiz is great to work with and provides very professional and high quality work.
I highly recommend Hafiz to anyone looking for a skilled, reliable, and dedicated freelancer. We look forward to collaborating on future projects!
I highly recommend Hafiz to anyone looking for a skilled, reliable, and dedicated freelancer. We look forward to collaborating on future projects!
MA
Mohammad A.
Sep 20, 2024
Arabic legal website scraping
VK
Vik K.
Jul 26, 2024
Scraping using Selenium and python
He really solved the issue which i thought was not quite possible. Hats off to him. Really talented guy.
About Hafiz Umar
AI Engineer | LLM | RAG | Agentic AI | Python | FastAPI | Gen AI | NLP
100%
Job Success
Okara, Pakistan - 2:04 pm local time
✅ 90% chatbot accuracy improvement ✅ 70% LLM hallucination reduction ✅ 10,000+ concurrent users in live production ✅ 5+ years AI / Python engineering
🤖 AI AGENTS & VOICE AGENTS
🔹 Agentic frameworks: LangGraph, CrewAI, AutoGen, multi-agent orchestration, tool use, persistent memory
🔹 Voice agents: ElevenLabs, Deepgram, Amazon Polly, Azure AI Speech, Retell AI, Whisper STT/TTS, LiveKit, Rasa AI, conversational IVR
🔹 Business automation: n8n, Make, Zapier with LLM backends, CRMs, and enterprise data — lead qualification, document routing, zero-touch workflows
📚 RAG SYSTEMS & VECTOR DATABASES
🔹 Frameworks: LangChain, LlamaIndex, LangGraph, LangServe, LangSmith — end-to-end retrieval pipeline design and deployment
🔹 Vector stores: Pinecone, ChromaDB, FAISS, Qdrant, Weaviate, Milvus, PGVector — ingestion, chunking, embedding pipelines
🔹 Retrieval: hybrid search, BM25 fusion, cross-encoder re-ranking, parent-child chunking, semantic caching, RAGAS / TruLens / DeepEval
🔹 Embeddings: OpenAI text-embedding, Sentence Transformers, Cohere, BGE, E5
🧠 LLM FINE-TUNING & OPEN-SOURCE MODELS
🔹 Fine-tuning: PEFT, LoRA, QLoRA, RLHF, DPO, SFT — Unsloth, Axolotl, HuggingFace AutoTrain, SageMaker, synthetic dataset generation
🔹 Open-source LLMs: LLaMA 3/4, Mistral, Mixtral 8x7B, Phi-3, DeepSeek, Qwen, Gemma, Falcon, CodeLlama
🔹 Quantization: AWQ, GPTQ, GGUF, INT4, INT8, PTQ — cost and latency optimized deployment
🔹 Fast inference: vLLM, TGI, TensorRT-LLM, Ollama, llama.cpp, BitsAndBytes
✍️ PROMPT ENGINEERING & LLM DESIGN
🔹 Multi-turn, few-shot, zero-shot, chain-of-thought, tree-of-thought, structured outputs, function calling, tool use, programmable pipelines
🔹 Hallucination reduction — grounding, citation enforcement, confidence scoring, output validation with Pydantic / Guardrails AI
🚀 LLM DEPLOYMENT PLATFORMS
🔹 AWS SageMaker, Hugging Face Inference API, Replicate, RunPod, Modal, GCP Vertex AI, Azure AI Services, Vercel AI SDK, Google Cloud Run
🐍 BACKEND & API DEVELOPMENT
🔹 Python: FastAPI, Django, Flask — async, microservices, background workers, streaming APIs, WebSockets
🔹 Node.js: Express.js, Nest.js — real-time, event-driven, REST and GraphQL APIs
🔹 API design: REST, GraphQL, OpenAPI, OAuth2, JWT, API Gateway, rate limiting, webhooks
🔹 Databases: PostgreSQL, MongoDB, Redis, MySQL, Firebase, DynamoDB, Supabase — schema design, query optimization
🔹 Task processing: Celery, APScheduler, RQ — async batch pipelines, queue management, retry logic
🔹 Web scraping: Crawl4ai, Playwright, Selenium, BeautifulSoup, Scrapy, Puppeteer
🔬 ADVANCED AI, NLP & DOCUMENT AI
🔹 NLP: Transformers, HuggingFace, SpaCy, NLTK — entity extraction, classification, summarization, intent detection
🔹 Document AI: OCR, PDF parsing, Gemini document parsing, Tesseract, Unstructured, table and form extraction
🔹 ML libraries: PyTorch, Scikit-learn, NumPy, Pandas, Matplotlib, Plotly
🔹 AI evaluation: RAGAS, TruLens, DeepEval, LLM-as-judge, A/B testing, retrieval benchmarking
🔹 Low-code AI: Flowise AI, LangFlow, StackAI
☁️ CLOUD & DEVOPS
🔹 AWS: EC2, S3, Lambda, RDS, ECS, EKS, SageMaker, API Gateway, CloudFront, Bedrock, SNS, SQS
🔹 Containers: Docker, Kubernetes, Docker Compose, NGINX, Gunicorn
🔹 CI/CD: GitHub Actions, GitLab CI, Jenkins — zero-downtime deployments
🔹 IaC: Terraform, CloudFormation
🔹 Multi-cloud: Azure AI Services, GCP AI Platform, Vertex AI, Google Cloud Run
🏗️ PRODUCTION PROJECTS DELIVERED
◆ Intelligent Workspace Assistant — Multi-source RAG platform aggregating Google Workspace, Microsoft 365, and Notion. Built with LangChain, Pinecone, FastAPI, Gemini, and OAuth2. Natural language querying with dynamic tagging and cross-platform retrieval.
◆ Real Estate Intelligence Platform — LLM-powered document parsing, automated competitor scrapers, and city-level market analytics on AWS with Docker and GitHub Actions CI/CD.
◆ AI Meal Plan Generator — Hybrid AI backend with async batch processing for 10,000+ weekly users, automated PDF pipeline, and AWS S3 storage.
🏢 INDUSTRIES SERVED
Healthcare · Real Estate · SaaS · FinTech · eCommerce · Legal Tech · EdTech · Logistics · Hospitality · Enterprise Automation
💡 WHY CLIENTS CHOOSE ME
→ Production-grade code from day one — Docker, CI/CD, async, fully tested — not demos
→ Real metrics behind every claim — accuracy, latency, uptime, load benchmarks documented
→ Always current — actively shipping with GPT-5, Claude 4, Gemini 3, LLaMA 4 on live projects
→ End-to-end ownership — design, build, deploy, monitor.
📩 Building an AI product? Need a RAG system, AI agent, or automation pipeline? Message me with your use case —I respond within 24 hours with a clear breakdown of approach, timeline, and and exactly what you can expect to ship.
Steps for completing your project
After purchasing the project, send requirements so Hafiz Umar can start the project.
Delivery time starts when Hafiz Umar receives requirements from you.
Hafiz Umar works on your project following the steps below.
Revisions may occur after the delivery date.
Architecture & Database Setup
I will initialize the project environment, configuring Docker, FastAPI, and the PostgreSQL database extended with pgvector for future AI embeddings.
Frontend UI & Rich-Text Editor
I will develop the sleek UI dashboard and the Notion-style project system, including the block-based rich text editor where you will draft your documents.