The highest leverage thing a company can do right now is build a data lakehouse and give AI agents access to it.

This requires a two-step process.

First, you set up ingestion and sync agents to pull all of your vendor data into one place. This means continuously syncing reviews, orders, subscriptions and all other data from disparate systems into your lakehouse via MCPs. The goal here is complete data availability. If the data is not centralized, the agents will not work.

Second, you give your analytics agents access to that data. You don't just point an LLM at a database and assume it will figure it out. You use models like Claude Fable 5.1 and equip them with specific, targeted skills. You teach them industry best practices for your specific business function and give them explicit instructions on exactly how to use the server to query the right data.

When you do this correctly, the result is a massive unlock.

An analytics agent can run through millions of rows of data and generate a 5-page cohort analysis in 17 minutes. That is work that would typically take a data team days to compile, clean, and format.

But doing this entirely from scratch takes a lot of time and engineering resources. You have to build the pipelines, manage the API syncs, and structure the architecture before the agents can even function.

Instead of spending months on custom infrastructure, you can deploy a pre-architected lakehouse like Gentic DB and pair it directly with Gentic MCP servers. The foundational architecture is already built.

Bora Celik
Founder, Gentic

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