Talha I.

Most AI projects I see are not failing at the model. Teams already have access to GPT, Claude, APIs, automation tools, or a chatbot pilot. The problem is that the layer underneath is unowned: whether the data feeding the model is current, whether the retrieval is actually accurate, whether the agent handled the edge case correctly, or whether the pipeline behind it even ran last night. That is the layer I build. I build the data and AI layer behind products and day-to-day operations: RAG chatbots, LLM integrations, AI agents, data pipelines, vector search, validation and data quality checks, monitoring, and reporting across the tools your team already uses. Recent results include: Built a real time ML pattern detection system for trading charts, delivered with a 5 star review on a $2,300 contract Shipped production ETL pipelines processing over 100K records per day Deployed ML models achieving over 90% accuracy Reduced a CRM reporting cycle from 3 days to 4 hours My background combines production data engineering at Starzplay and Dubizzle Labs with an MS in Big Data and Machine Learning from the University of Bonn, where my research covered transformer models, allowing me to work across both the data layer and the AI layer. Technologies I regularly work with include Python, SQL, OpenAI API, Claude, LangChain, RAG, Pinecone, Chroma, Snowflake, AWS, PySpark, n8n, REST APIs, webhooks, and Power BI. Whether the goal is a chatbot that answers correctly, an agent that runs unattended, cleaner data, or reporting your team can trust, I focus on building AI systems that are reliable, accurate, and easy for teams to operate. I work in English and German (ich betreue deutschsprachige Kunden gerne auch auf Deutsch). Send me your current data, AI, or reporting challenge, and I'll identify the bottlenecks and explain what I'd build first.