You will get a custom AI pipeline that automates manual workflow in your business.
Project details
You will get a custom AI automation pipeline that turns your most time-consuming business process into a system that runs itself. I design and build intelligent workflows that connect your tools, apply AI where it matters, and deliver structured output, no manual work required. I have built production systems for real operational problems, and I bring that same precision to every project. The work I deliver is tested, documented, and built to scale with your business.
AI Algorithms
Self-Organizing MapAI Applications
AI Chatbot, AI Content Creation, AI Mobile App Development, AI Text-to-Image, AI Text-to-Speech, AI-Generated Art, AI-Generated Code, Conversational AI, Natural Language Generation, Natural Language Understanding, Text Recognition, Time Series AnalysisAI Development Language
PythonAI Tools
Bing AI, GitHub Copilot, ReplitAI Models
BLOOM, ChatGPT, DALL-E, GPT-3, GPT-4, GPT-J, GPT-Neo, LLaMA, Midjourney AI, OpenAI Codex, Stable DiffusionWhat's included
| Service Tiers |
Starter
$30
|
Standard
$50
|
Advanced
$100
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 14 days |
Number of Revisions | 1 | 2 | 3 |
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.
Fast Delivery
+$60 - $250About Patrick
AI Automation Specialist | AI Integration | N8N Automation | Claude AI
Port Harcourt, Nigeria - 10:06 am local time
My core work is designing and deploying end-to-end automation architecture that goes beyond simple tool connections. I think in terms of data flow, business logic, orchestration, reliability, and failure states; what needs to happen, in what order, under what conditions; and what the output must look like on the other side.
Whether the right solution is n8n, Make, Zapier, a custom Node.js backend, an LLM workflow, a RAG system, or a vector database pipeline, I choose tools to fit the business problem, not the other way around.
I specialize in building AI-powered automation systems where large language models do real work inside production workflows: extracting structure from unstructured data, reasoning across documents, generating grounded responses from proprietary knowledge bases, classifying information, routing decisions, and automating complex multi-step tasks that rule-based workflows can’t handle.
These are not prototypes. They are production-ready AI systems designed for reliability, scale, and business impact.
What I Build
✅ AI Automation & Workflow Automation
End-to-end automation pipelines
Business process automation
Workflow orchestration
CRM automation
API integrations & webhooks
No-code / low-code automation (n8n, Make, Zapier)
Custom backend automation
✅ AI Agents & LLM-Powered Workflows
AI agents
Agentic AI systems
Multi-step LLM workflows
Prompt orchestration
Tool-using agents
AI reasoning pipelines
Structured data extraction
Autonomous workflows
✅ RAG Systems (Retrieval-Augmented Generation)
Document ingestion pipelines
Vector database architecture
Semantic search systems
Embedding pipelines
Knowledge base chatbots
Enterprise search systems
Grounded LLM applications
✅ Custom AI Applications
AI-powered internal tools
Searchable knowledge platforms
AI dashboards
SaaS MVPs
Backend APIs
Production deployment
Selected Projects:
1. AI Virality Clipper (Video Intelligence Automation)
Built an AI-powered video automation system that identifies viral moments inside long-form video content and automatically clips them into short-form segments for social media distribution.
Capabilities:
video ingestion
AI scene analysis
highlight detection
automatic clip generation
content repurposing automation
Result: dramatically reduced manual editing time and accelerated content publishing.
2. Podcast AI Automation Pipeline (Transcript - Structured Knowledge System)
Built an end-to-end AI workflow for a podcast platform that automatically:
ingests transcripts.
processes text with LLMs
extracts structured insights
populates a Notion database
generates searchable metadata automatically
Result: replaced manual transcript review and turned raw podcast content into structured operational knowledge.
3. RAG-Powered Podcast Search Chatbot
Built a production RAG chatbot that allows users to query podcast transcript archives conversationally.
Architecture included:
transcript ingestion pipeline
vector embeddings using Nomic Embed v1.5
semantic retrieval with Supabase pgvector
LLM response generation with Groq
grounded answers with episode citations
deployed as a live web application
Result: Users can search across multiple podcast episodes instantly without manually browsing archives.
Tech Stack
Automation: n8n, Make, Zapier, webhooks, REST APIs
AI/LLMs: OpenAI, Claude, Groq, Prompt Engineering, AI Agents, RAG
Vector Search: Supabase pgvector, Nomic Embeddings, Semantic Search
Backend: Node.js, Express.js, JavaScript, API Development
Database: PostgreSQL, Supabase
Frontend: HTML, CSS, JavaScript
Architecture: Workflow Orchestration, Data Pipelines, Automation Design, System Design
I work with founders, startups, and businesses globally on projects where the brief is a real operational problem, not a feature checklist.
If you need an AI automation expert who understands both business workflows and AI systems architecture, I can help you build systems that save time, reduce operational overhead, and scale reliably.
You should send a message.
Steps for completing your project
After purchasing the project, send requirements so Patrick can start the project.
Delivery time starts when Patrick receives requirements from you.
Patrick works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & Scoping
I review your workflow requirements, identify integration points, and confirm the logic before touching any tools
Architecture Design
I map out the full pipeline—triggers, data flow, decision points, and output structure and share it with you for sign-off