You will get n8n Automation Expert | Custom n8n Workflows & API Integrations
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Top Rated
Project details
Manual work is costing you time and money. I build n8n AI agents and automation workflows that handle it automatically, lead generation, CRM sync, customer support, sales pipelines, and any business process you want off your plate.
From simple API integrations and webhook routing to full RAG-based AI agents trained on your documents or website built on OpenAI, Claude, or Vapi, self-hosted or cloud, documented and ready to scale.
From simple API integrations and webhook routing to full RAG-based AI agents trained on your documents or website built on OpenAI, Claude, or Vapi, self-hosted or cloud, documented and ready to scale.
Programming Languages
PythonCoding Expertise
Cross Browser & Device Compatibility, Performance OptimizationWhat's included
| Service Tiers |
Starter
$250
|
Standard
$600
|
Advanced
$1,200
|
|---|---|---|---|
| Delivery Time | 4 days | 7 days | 12 days |
Number of Revisions | 1 | 2 | 2 |
Number of Pages | 1 | 2 | 4 |
Design Customization | |||
Content Upload | |||
Responsive Design | - | ||
Source Code | - | - |
Frequently asked questions
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NJ
Njoroge J.
Aug 6, 2026
30 minute consultation
Zeeshan understood the assignment. He is able to make strategic recommendations while adjusting based on our specific opportunities and constraints.
SP
Stefano P.
Apr 25, 2026
AI Engineer for ELENA
Zeeshan did an excellent job on Phase 1 of the ELENA project.
This was not a simple task. ELENA required both technical execution and strategic understanding: backend, frontend, AI integration, document upload, RAG/document intelligence, demo flow, and a clear foundation for future development. Zeeshan handled the work professionally and showed strong ownership throughout the project.
What I appreciated most was that he did not just complete tasks mechanically. He understood the vision behind ELENA and helped shape the first working demo into something usable and presentable. He was clear in communication, detail-oriented, reliable, and always willing to explain technical decisions when needed.
He also went beyond the initial scope by helping bring forward a working document intelligence/RAG foundation earlier than expected, which added real value to the demo. The final result gives ELENA a solid base to continue into the next phase.
I also appreciated his technical feedback and strategic thinking on the broader AI direction of the project. It is rare to work with someone who can contribute both as an engineer and as a technical advisor.
Overall, this was a very positive experience. Zeeshan is professional, committed, technically strong, and trustworthy. I would be happy to work with him again in the future.
This was not a simple task. ELENA required both technical execution and strategic understanding: backend, frontend, AI integration, document upload, RAG/document intelligence, demo flow, and a clear foundation for future development. Zeeshan handled the work professionally and showed strong ownership throughout the project.
What I appreciated most was that he did not just complete tasks mechanically. He understood the vision behind ELENA and helped shape the first working demo into something usable and presentable. He was clear in communication, detail-oriented, reliable, and always willing to explain technical decisions when needed.
He also went beyond the initial scope by helping bring forward a working document intelligence/RAG foundation earlier than expected, which added real value to the demo. The final result gives ELENA a solid base to continue into the next phase.
I also appreciated his technical feedback and strategic thinking on the broader AI direction of the project. It is rare to work with someone who can contribute both as an engineer and as a technical advisor.
Overall, this was a very positive experience. Zeeshan is professional, committed, technically strong, and trustworthy. I would be happy to work with him again in the future.
IG
Imran G.
Mar 19, 2026
OpenClaw AI Assistant Setup with Ollama Local Models
Zeeshan did an excellent job setting up the OpenClaw AI assistant with Ollama local models. He was knowledgeable, responsive, and delivered everything as promised. Highly recommended and I would gladly work with him again.
TA
Toomah A.
Aug 24, 2022
Image classification model
Recommend to work with 👍
MJ
Mr J.
May 17, 2022
Object Tracking (Parcel) Using Deep Learning
Dr. Zeeshan Gillani is an expert in various fields, machine learning and deep learning for instance. He has a quite impressive educational background with a Ph.D. degree. He is responsive and efficient, will explain whatever you would need to know about the implementation. So happy to work with him, and will definitely add him to my upcoming projects.
About zeeshan
AI Solutions Architect | RAG, LLM, MLOps, agentic, AI System Architect
100%
Job Success
Lahore, Pakistan - 9:48 am local time
I help startups and enterprises turn LLM capabilities into reliable, deployable architecture (retrieval, memory, tool use, orchestration, voice, evaluation, and scalable cloud infrastructure), not prompt-engineering experiments that fall over in production.
SYSTEMS I'VE SHIPPED
- PropAI: multi-tenant WhatsApp AI agent platform for real-estate agencies and agents (Meta Cloud API, Embedded Signup, Coexistence). Claude-driven LLM orchestration via n8n, with RAG + agentic responses on agents' live numbers. Deployed across African markets on region-isolated, data-residency-compliant AWS; property-market feasibility studies delivered for UK clients.
- Agentic financial-intelligence platform: LangGraph orchestration over structured financial data, generating personalized audio content end-to-end (LLM + TTS).
- CropSight: computer-vision platform for precision agriculture and corporate-farm compliance. Fuses satellite, drone, and mobile imagery with GAN-based super-resolution mapping low-resolution sources to high-resolution field detail. Deployed across African markets.
-Elena AILL (research collaboration): advising on architecture approach, technical direction, and MVP build for a from-scratch, non-transformer persistent-memory AI research project.
- Enterprise AI & telematics integration for PepsiCo, plus AI architecture and code audits for international clients.
WHAT I BUILD
- End-to-end RAG pipelines (hybrid retrieval, reranking, vector DBs)
- Agentic & multi-agent systems with tool use, memory, and orchestration
- Voice AI: TTS, voice-to-voice / voice conversion, and production voice-agent pipelines
- Fine-tuning and LoRA/QLoRA pipelines
- Persistent-memory and continual-learning systems
- Workflow automation at production scale (n8n, self-hosted)
- Scalable inference infrastructure and cloud-native ML systems on AWS
- CI/CD and MLOps workflows for AI products
- LLM evaluation and performance-benchmarking frameworks
STACK
ML/DL: PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, OpenCV · LLMs: Claude, GPT, Llama, Mistral, open-source models · Orchestration: LangGraph, LangChain, LlamaIndex, n8n (production, self-hosted), custom · Voice/Speech: TTS, voice-to-voice (RVC), Whisper STT · Vector/DB: Qdrant, pgvector/Supabase, Pinecone, FAISS, BGE reranker · Backend: FastAPI · Cloud: AWS (EC2, EKS, S3, IAM, monitoring) · Deployment: Docker, Kubernetes · WhatsApp Business / Meta Cloud API
Most developers build AI demos. I architect systems with clear technical decisions, cost-awareness, evaluation built in, and production-readiness from day one, and I work at the research edge of memory and continual-learning architecture, not just integration.
If you're building an AI-native product and need architectural clarity, scalability strategy, and senior technical leadership, I can structure and execute it. I also do fixed-scope AI architecture and code audits, a fast, low-risk way to start.
Steps for completing your project
After purchasing the project, send requirements so zeeshan can start the project.
Delivery time starts when zeeshan receives requirements from you.
zeeshan works on your project following the steps below.
Revisions may occur after the delivery date.
Here are:
Step 1: Requirements I review your documentation and analyze your business needs to design the optimal automation workflow. .
Step 2:
Workflow built, tested end-to-end, and revised until it runs exactly as needed.