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I performed end-to-end quality assurance testing on the client’s AI chatbot program. My role involved completing over 20 structured test sessions across Chrome, Safari, and Edge. Following a detailed feedback template, I documented bugs, usability issues, and performance inconsistencies. I also provided constructive feedback on the overall user experience, clarity of instructions, and the general flow of the course to help improve the final user experience.
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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AI can compress production time, but it does not remove the work between a convincing interface and a dependable web product. Start with one important user task, inspect the systems supporting it, then confirm the finished pages are accessible, measurable, and understandable to search engines. Which layer do you review least often after an AI-assisted build?
🚀 Using AI in your business? I’ll find where it can go wrong before your customers do.
I’m opening a few spots for an AI Workflow Quick Scan.
I’ll review one AI-assisted workflow and identify:
• Where AI is making or influencing important decisions
• Missing human approval points
• Weak or unclear operating rules
• Workflow failure and drift risks
• Where stronger controls could be added
You’ll receive a concise review with practical recommendations you can actually use.
No giant consulting engagement. No AI sales pitch. Just an independent look at the workflow you already have.
I design and test governed AI workflows, including controlled state transitions, adversarial testing, evidence lineage, and human-authority controls.
AI Workflow Quick Scan — $350
Message me on Contra if you have a workflow you want reviewed.
@Dustin Cummings The focus on missing human approval points and drift risks is practical. Do you also map evidence lineage when a workflow spans multiple AI tools?
I build production-ready AI systems that combine LLMs, RAG, reinforcement learning, advanced agentic architectures, automation to transform complex workflows into intelligent, scalable products. I have also built some MVP using agentic AI and open to share it with investors.
What I build:
→ AI Agents & complex multi-agent systems
→ Agentic AI architectures & autonomous workflows
→ RL-based LLM reasoning & reasoning optimization
→ RAG / knowledge assistants & enterprise search
→ LLM-powered applications
→ Document & data intelligence
→ AI automation & workflow orchestration
→ NLP & Generative AI solutions
→ AI-driven decision-making systems
→ Frontend and Backend using Nextjs, Reactjs and FastAPI
Core expertise:
LLMs • RL for LLM Reasoning • Agentic AI • Multi-Agent Systems • RAG • Generative AI • NLP • Reinforcement Learning • Knowledge Graphs • Vector Search • AI Automation • AI Architecture
Tech stack:
Python • PyTorch • LangGraph • LlamaIndex • OpenAI • AWS • Vector Databases • Docker • Kubernetes • React • TypeScript • fastAPI • Nextjs
I focus on the full AI lifecycle from problem formulation, AI architecture, and rapid prototyping to model development, agentic workflow design, deployment, evaluation, and production optimization.
My goal is to design AI systems that don't just generate content, but reason, retrieve knowledge and use tools for computations, coordinate agents, learn from feedback, and execute complex tasks autonomously with automatic validation and assessment.
I need more GPU and investments to develop more vertical AI products.