Export database schemas and tables as Markdown for LLM workflows

Many teams — data engineers, analytics engineers, BI analysts, ML/LLM engineers and product owners — struggle with schema knowledge that is siloed, inconsistent and stale, yet LLMs and automated agents require concise, machine- and human-readable summaries to construct reliable prompts and workflows. The result is slow onboarding, error-prone prompt engineering and blocked automation across organizations that are part of an addressable base of roughly 1.5M companies spending about $8K per year on database and analytics tooling. You could build a product that exports database schemas and tables as Markdown optimized for LLM workflows: YAML front matter, token-efficient LLM summaries, column stats and sample rows, relationship graphs, SQL examples, and automated diffs that integrate with Git, dbt, CI pipelines, a CLI, API and optional UI. This market is attractive now because LLM adoption, documentation-as-code practices and the democratization of data are converging, producing a $12.0B opportunity (market score 90/100) and a solid revenue outlook (78/100) for tools that make schema context reliably consumable by both people and models. To stand out, prioritize exports tuned for token budgets and prompt consumption, provide embeddings and RAG-ready artifacts, and ship frictionless dbt/Git and enterprise security integrations to outcompete more generic documentation tools in a medium-competition landscape. Key challenges are building and maintaining reliable connectors across diverse databases, proving clear ROI to teams hesitant to add another tool, and competing with incumbents — overcoming those will require a product-led distribution, concrete time-to-value metrics, and exceptionally easy integration paths.