Market Research Agentic Team
End-to-end AI marketing pipeline in Python/Jupyter — multi-agent market research, internal product matching, AI image generation, copywriting, and an executive markdown report. Includes a reflection loop that monitors execution and dynamically revises the remaining plan.
A practical demonstration of AI orchestration and prompt-driven workflow automation: a multi-agent marketing pipeline that takes a product brief and returns a packaged executive report — research, matched catalogue items, generated imagery, copywriting, the lot.
The pipeline
- Tool-enabled market research — agents call out for external context and pull it back into the plan.
- Internal product catalogue matching — finds the items in our own catalogue that best fit the brief.
- AI image generation — produces creative assets keyed off the research and the matches.
- Copywriting — drafts the marketing language for each asset.
- Executive packaging — assembles a clean markdown report ready for stakeholders.
Reflection-based planning loop
The thing that lifts this above a linear pipeline is the reflection loop: a planner monitors execution and dynamically rewrites the remaining plan when steps surface new information or fail. The agents don’t just execute — they re-plan.
Stack
Python + Jupyter notebooks, OpenAI for the language models and image generation, markdown for the final deliverable. Built to be read top-to-bottom in a notebook, not deployed as a service — the notebook is the artefact.
Status
A research prototype that lives in a private notebook rather than on this site — the write-up above is the public summary.