Turn repetitive data reports into polished slide decks via Python notebooks

Many analytics, finance, marketing and operations teams spend repeated hours assembling weekly or monthly slide decks from the outputs of Python notebooks and BI tools, a task that is error-prone and scales poorly as report cadence increases. With roughly 200 million knowledge workers and an estimated $12.0B addressable market (200M × $60/year for presentation/report automation), this is a widespread operational friction from SMBs to large enterprises. You could build a developer-focused platform that converts notebooks (Jupyter/Colab) into production-ready slide decks by mapping code cells, visuals and markdown into editable templates, re-executing notebooks for fresh data, and integrating LLMs to draft executive summaries and slide notes. The product would include scheduled exports, connectors to common data sources, PPT/Google Slides outputs and enterprise governance controls; technical challenges include robust rendering of interactive outputs and secure data access at scale. Timing is attractive because teams are standardizing on notebook-first analytics, businesses want to eliminate manual recurring-report assembly, and LLMs now make automatically generated narratives credible—factors that explain the high market score (95/100) and strong revenue potential (90/100) you provided. The $60/year per user estimate is conservative for organizations that realize weekly time savings, but expect longer enterprise sales cycles and integration effort. To stand out, prioritize a notebook-native UX (cell-level slide mapping and reproducible re-execution), enterprise-grade governance and prebuilt industry templates plus an open integration layer for customization; that approach leverages developer mindsets and reproducibility as strengths while acknowledging the real implementation and fidelity challenges.