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Building Intelligent AI Systems That Actually Work
Businesses don't just need another chatbot — they need AI systems that can understand data, use tools, automate workflows, and solve real problems.
I build custom AI Agents, RAG Chatbots, and AI Automation Systems designed around real business requirements.
What I can build:
🔹 AI Agents — tool calling, multi-step reasoning & automation
🔹 RAG Chatbots — chat with PDFs, documents, websites & knowledge bases
🔹 AI Customer Support — automated responses and knowledge-based assistance
🔹 AI Automation — n8n workflows, APIs, webhooks & business processes
🔹 AI SaaS Applications — complete AI-powered web applications
🔹 AI Integrations — OpenAI, Claude, Gemini and other LLMs
🔹 Backend Systems — Python, FastAPI & REST APIs
🔹 Vector Search — Pinecone, Qdrant, ChromaDB & FAISS
🛠️ Tech Stack
Python • FastAPI • LangChain • LangGraph • n8n • OpenAI • Claude • Gemini • React • RAG • Vector Databases
From a simple AI assistant to a complete production-ready AI platform, I can help turn your idea into a working product.
Have an AI idea or business workflow you'd like to automate? Let's build it. 🚀
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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Everyone loves to say AI makes creative work feel impersonal but I found the uses that actually save time without replacing my creative expertise.
Here's what's been working for me:
✍️ Copy and emails: I write the base, AI helps me polish and tighten it
🎨 Concept and moodboard copy: same approach, I write the core idea, AI helps shape the language for presentations
📦 Mockups: AI speeds up creation so I don't spend hours looking for the right .psd online
The key has been using it to speed up the boring parts so I can focus on the creative bits.
What's an AI use in your workflow that actually made things better, not just faster?
Starting with your own draft is close to how I use it on builds. I give AI the page and a short checklist, then use what it catches to tighten the work before I ship.
AgentFlow10 — AI Agent & Automation Platform
Lead Engineer · Self-hosted AI agent platform with a visual workflow builder
The problem
Automation tools are built for businesses that already have a card on file, reliable bandwidth, and a Slack workspace. That leaves most of the world out. In markets where WhatsApp is the customer channel and mobile money is the payment rail, the standard automation stack either doesn't connect or is priced in currencies that make it a non-starter.
There was a second problem. Every serious automation platform is someone else's cloud. If your data is sensitive, or your compliance rules keep it in-country, or you just don't want to hand your customer records to a vendor, your options run out fast.
What I built
AgentFlow10 is an AI agent and automation platform you can run on your own infrastructure. A visual builder lets you compose agents and multi-step workflows without writing code, connect them to the tools you already use, and deploy them where your data lives.
The design goal was to make AI agents something a small team can actually operate — not a research project requiring an ML engineer on staff.
What I did
I built and run it end to end: product direction, backend architecture, LLM orchestration, integration layer, the workflow engine, frontend, brand system, and go-to-market. Everything from the database schema to the landing page copy.
Notable pieces of engineering:
A workflow execution engine that handles branching, retries, and failure states — because an agent that dies silently mid-run is worse than no agent
LLM orchestration with prompt management and output validation, so results are structured and usable rather than free text
A self-hosting path that a non-specialist can actually deploy
Integration architecture designed for channels that matter outside Silicon Valley, including WhatsApp and mobile money flows
Why it matters for clients
This is the project I point to when someone asks whether I can build AI systems rather than just call an API. Running a platform means I've dealt with the parts that break in production — cost control, rate limits, non-deterministic model output, failure recovery. That experience is what I bring to client work.
Built with: Python, TypeScript, React, Next.js, Node.js, PostgreSQL, LLM APIs, Docker
🔥 TalentFilter AI — AI-Powered Candidate Screening
TalentFilter AI is an AI-powered hiring application designed to help recruiters and hiring teams evaluate candidates faster and focus on the people who best match each role.
In Phase 1, I built the core hiring workflow: users can create and manage job openings, add candidates and CVs, automatically analyze candidate profiles with AI, generate match scores and hiring recommendations, and quickly identify the strongest candidates through a ranked dashboard and AI hiring insights.
The goal is simple: turn a pile of resumes into a clear, actionable shortlist.
Phase 2 will expand TalentFilter AI into a more complete recruiting workspace, including deeper candidate profiles, improved AI-generated interview questions and hiring insights, candidate pipeline/status management, stronger analytics, workflow automation, and a more polished end-to-end recruiter experience.
Built with: Bubble, AI integrations, workflow automation, dynamic data, and responsive UI.
