You will get a manual business process automated end to end with n8n and Claude Code
Project details
Most automations break quietly a few months in, and nobody notices until someone asks where the report went.
Invoice approval, CRM sync, report assembly, onboarding. Whatever your team repeats by hand.
What sets it apart:
• Built in n8n as a canvas of named steps, not a script only I can read
• AI only where it earns its cost: drafting, classifying, routing. Plain code everywhere else
• Claude, Gemini or GPT at the model step, chosen per job, swapped later without a rebuild
• Validation on the way in and an alert when a run fails, so a break is loud instead of silent
• Deployed on your accounts with your keys, the source, and a recording of how to change it
Built repeatedly in n8n: a scheduled pipeline that plans, researches, writes and edits; an API-to-database sync that validates every record and alerts on failures; a research agent that answers from what it finds.
One honest note. Some steps should not be automated, and a few should be deleted instead. I will say so before building, even if it makes this job smaller.
Invoice approval, CRM sync, report assembly, onboarding. Whatever your team repeats by hand.
What sets it apart:
• Built in n8n as a canvas of named steps, not a script only I can read
• AI only where it earns its cost: drafting, classifying, routing. Plain code everywhere else
• Claude, Gemini or GPT at the model step, chosen per job, swapped later without a rebuild
• Validation on the way in and an alert when a run fails, so a break is loud instead of silent
• Deployed on your accounts with your keys, the source, and a recording of how to change it
Built repeatedly in n8n: a scheduled pipeline that plans, researches, writes and edits; an API-to-database sync that validates every record and alerts on failures; a research agent that answers from what it finds.
One honest note. Some steps should not be automated, and a few should be deleted instead. I will say so before building, even if it makes this job smaller.
AI Algorithms
Large Language ModelAI Applications
AI Content Creation, AI-Enhanced Classification, AI-Generated Code, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Gradio, StreamlitAI Models
ChatGPT, GPT-4What's included
| Service Tiers |
Starter
$300
|
Standard
$700
|
Advanced
$1,600
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 days |
Number of Revisions | 2 | 3 | 4 |
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.
Additional Revision
+$90
One extra workflow
(+ 3 Days)
+$200
30 days of support and fixes after handover
+$180
Live training session for your team
(+ 2 Days)
+$250Frequently asked questions
About Nisar
Claude AI Agent Developer | RAG Chatbots | Python | MS Data Science
Islamabad, Pakistan - 1:21 pm local time
What I build:
• AI agents and workflow automation — Claude and Claude Code, MCP servers, tool use, function calling
• RAG chatbots trained on your website, PDFs and database — with citations, so every answer can be traced back to its source
• Document AI and OCR — invoices, receipts and scans turned into structured data, including fully offline builds for material that cannot go to a cloud API
• AI web and mobile apps — Android and iOS with Flutter and Dart, Next.js and FastAPI on the web, through payments, maps and app store rollout
• Data science and machine learning — churn prediction, forecasting, segmentation, NLP at scale
Selected results from 19 portfolio projects:
• Dine — a two-sided restaurant discovery, ratings and booking platform for a paying client. Tools and languages used are Flutter, Node.js, Python, PostgreSQL, Google Maps, Safepay, AWS.
• Offline document OCR and semantic search — 300+ business documents processed with nothing leaving the machine.
• Customer churn prediction — 85.75% accuracy at 92.85% ROC AUC on imbalanced data.
• Sentiment analysis on 4M+ Amazon reviews — Apache Spark, tuned SVM at 86.01%.
• Real-estate price forecasting — 160,000+ listings, Prophet, R²=0.80.
Education and credentials: MS in Data Science, MSc Mathematics, MBA in Project Management, BSc Computer Science, and the IBM Data Science Professional Certificate. Alongside that, two decades running IT for a group business — I have been the client stuck with an unmaintainable handover, so I do not build things you cannot own and run yourself.
Stack: Python, Claude and the Anthropic API, MCP, LangChain, FastAPI, PostgreSQL, Flutter and Dart, Next.js, Node.js, Apache Spark, Docker, AWS
How I work: I scope before I quote, split the work into milestones so you can check the first piece before funding the rest, and hand over the source code. I am in GMT+5, which overlaps US mornings and the full European working day.
I am not the right fit for pure design work, or for a project that only needs to demo rather than keep running afterwards.
Tell me the outcome you need and I will tell you honestly whether I am the right fit — including when I am not.
Steps for completing your project
After purchasing the project, send requirements so Nisar can start the project.
Delivery time starts when Nisar receives requirements from you.
Nisar works on your project following the steps below.
Revisions may occur after the delivery date.
Map the process
I write your workflow out step by step and mark where the time actually goes. You approve that map before anything is built, and it is where I flag any step I think should be deleted rather than automated.
Connect the tools
Every system in the chain is connected and tested with credentials in your name, so access never depends on an account of mine. Anything with no API gets an honest workaround, agreed with you first.