Daniel Scott

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turning big-picture goals into clear, executable plans while aligning teams, data, and decisions to drive measurable growth.

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Why I Built a YC Database at 2AM

I was prepping my YC application. I wanted to know: has anyone already done this idea?

YC's search gave me nothing useful. So I spent a weekend scraping their Algolia API, built a local database, ran embeddings against my pitch... and found 3 companies that had tried it.

The enterprise question isn’t capture. It’s control.

On a Tuesday, the first enterprise question is usually not can you capture AI code? It s who can see the records, how long do they live, and what happens when a policy blocks a change?

That s the part LineageLens is built for. Base gives you local capture. Lite gives a shared team record. Plus and Max move the data into a backend where auth, permissions, retention, and policy live next to the provenance records instead of around them.

The useful thing here is not another dashboard. It s a self-hosted record of prompt, model, tool, file, and outcome that engineering, security, and platform teams can actually govern on their own infrastructure.

I keep seeing AI governance tools start with visibility, then discover that the real enterprise questions are identity, retention, and review. If the record cannot be scoped, retained, and exported on your side, it is not really governable.

When the same AI edit means different things in different places

One thing that surfaced while tightening LineageLens this week: capture is not the hard part. Agreement is.

If the extension, backend, and MCP server describe the same AI edit with slightly different field names or status values, you do not have provenance, you have three believable stories about the same event. That matters because reviewers and assistants start trusting whichever surface they looked at last.

The question I keep coming back to is simple: if a record can look applied in one place and accepted in another, is that still a single source of truth?

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