Agentic AI for Market Research

Research agents that gather, filter and summarise information are a recurring element in the projects of Dr. Alfred Ang. Dr. Ang is a full-stack AI developer, Agentic AI solution architect and corporate trainer, and serves as Chief Technology Officer of Tertiary Infotech Academy Pte Ltd, the Singapore training organisation behind Tertiary Courses, building complete solutions across front ends, back ends, databases, cloud deployment and native mobile apps. An automated YouTube production pipeline, triggered from a simple web form, sends topic requests to an agent that researches the subject, writes a script and assembles the video. Retrieval-augmented chatbots built on n8n answer questions from uploaded documents and brochures, and a stock analysis Telegram agent interprets live market data on demand. Dr. Ang built AI-powered content management systems on the Claude Agent SDK that generate research-backed articles, and a self-improving lead-generation chatbot that learns from the questions prospects actually ask, which doubles as a constant source of customer insight. A tutors marketplace runs a short Claude AI interview to assess applicants, and an AI coaching simulator explores how jobs can be redesigned around accountable AI agents. Dr. Ang also maintains Claude Code skills that automate multi-step knowledge work, and n8n workflows that manage email replies and subscriber lists for the Malaysia and Ghana sites. These projects, part of more than 300 public repositories on GitHub, put agentic research into everyday business operations rather than one-off experiments.

Agentic AI for Market Research applies that experience to the full research cycle using Claude Cowork. Dr. Ang starts with identifying data and sources: defining research objectives, turning them into focused questions and asking the agent to locate credible public information, industry reports and competitor material, while teaching learners to check sources rather than trust a confident summary. The second topic analyses market trends and industry developments, with learners directing Claude Cowork to compare competitors, track shifts in pricing and positioning and synthesise findings into a concise briefing. In the third topic, the focus turns to customer behaviour, market dynamics and simple forecasting, using survey responses, reviews and sales data to identify segments, needs and likely demand. The final topic evaluates marketing effectiveness with agentic AI models and indicators, connecting campaign data to measures such as reach, conversion and return on spend. Dr. Ang demonstrates how to structure an agent's task so its work is traceable, with notes on where each finding came from, and shows learners how to package a repeatable research routine they can rerun every quarter. Sessions mix live demonstration, guided practice on a market of the learner's choosing and group discussion of what the agent got right or wrong. Participants leave with a completed research report and a reusable workflow for future projects. The class also practises briefing the agent clearly so its output matches the decision at hand. Explore the research agents and automation projects behind this course at https://github.com/alfredang