SafeClaim AI
Insurance claims from photo intake to decision in seconds: three AI agents handle damage review, fraud screening and coverage checks, and anything unusual goes to a human adjuster. Running on AWS.
- Python
- LangGraph
- AWS EKS
- RAG
Senior Auditor at Mercor · Founder, Sciensify
Hi, I'm Abdullah, a data scientist in New Jersey. I audit what AI models say, build the data and GenAI systems around them, and ship software that people actually use.
I've spent five-plus years across data analytics, generative AI and full-stack engineering. I've built RAG systems on insurance documents, scaled Hadoop pipelines to millions of daily records, cut AWS compute costs 40%, and shipped web apps from the database up.
Today I work on the layer most people skip: evaluation. At Mercor I review AI-generated code for 184+ developers worldwide, score what models produce against a rubric, and flag where it went wrong, because that's the step that decides whether AI is safe to put in front of a customer. I'm also finishing an MS in Data Science at NJIT.
Data tells the story. I just help it speak clearly.
Insurance claims from photo intake to decision in seconds: three AI agents handle damage review, fraud screening and coverage checks, and anything unusual goes to a human adjuster. Running on AWS.
One place for my projects, tasks, study plans, mail and AI agents. Claude drafts email replies and LinkedIn posts for my approval, and it runs on my PC and my phone.
A to-do app with a thinking partner: before a task is saved, AI helps clarify the scope, write a real description and propose subtasks. You approve every step.
Predicts which recipes will drive high traffic before they're published, so editors feature content on data instead of gut feel. 85% precision.
Lets an AI run the code it writes without endangering the system: extracts the code, runs it isolated with strict time limits, and returns clean, structured results.
My blog on AI, evaluation and data science, written in plain language for people who build with it.
Auditing AI model responses against rubrics and flagging where they go wrong, including AI-generated code for 184+ developers worldwide.
Building and evaluating self-correcting coding agents with LLM orchestration, and red-team evaluations to make models more reliable.
Coursework in deep learning, machine learning and big data.
A RAG-powered Q&A system on insurance documents, Hadoop ETL scaled to millions of daily records, a 40% cut in AWS compute costs, and GenAI on AWS Bedrock and Azure OpenAI.
A React and MongoDB web app with JWT auth, a Power BI dashboard for public health authorities, and analytics that shaped marketing decisions.
Statistical models for predictive insight on research data.
National University of Computer and Emerging Sciences.
Hiring, collaborating, or just curious about AI evaluation? My inbox is open.