About DeltaVault
DeltaVault is the context layer for AI-built data: one governed place for what your data means, so AI can generate the right thing on any platform.
I have spent fifteen years building data automation solutions. For most of that time the shape of the work held steady: you wired the pipelines, you documented what you could, and the meaning of the data lived in someone's head or someone's spreadsheet. Then AI changed what a data team are expected to deliver in a week, and it made one thing obvious to me. The traditional way of building data automation was not going to keep up.
The tempting response is to bolt AI onto the systems we already have. I do not believe that works. A panel added to the side of an existing tool can answer questions about your metadata, but it cannot carry your standards and it cannot do the work. If AI is going to sit at the heart of how data gets built, it has to be the foundation, not an add-on. So I set out to build DeltaVault as a platform where AI is the core, not a feature stitched on at the edges.
That does not mean AI makes the decisions. It proposes, you review, and nothing lands until you approve it. Every change the AI writes arrives as a staged proposal, passes the same role checks as a human edit, and shows up in the same audit trail. You stay in control of what ships.
AI is here to stay, and it improves every few weeks: a new model, a new technique, another step up in the quality of what it can produce. A platform built for that pace has to treat AI as foundational from day one. Otherwise every leap in quality is something to catch up with instead of something you inherit.
This is why DeltaVault is model-driven. It starts from your business requirements and the outcomes you care about, and it uses AI to close the gap between what the business means and how the data is actually built. That, to me, is the only way forward.
Your meaning is the scarce part
DeltaVault is the place your business meaning lives: your requirements, your glossary, your governance rules, and the mappings from business concepts down to physical columns. Capture that context in one platform and generation stops being guesswork. Describe a table once, and the engine writes platform-correct code for Databricks, Snowflake, or Microsoft Fabric. Change the metadata, and the pipelines follow.
Deciding what the data should mean, and how it should be governed, is the work only your team can do, so it stays with you. The repetitive half, the plumbing rebuilt from scratch on every project, is the half we hand to AI that understands your intent.
Four things we will not trade away
AI at the core, not bolted on
AI is the foundation the platform is built on, not a feature layered onto legacy tooling. It works on the same governed metadata you edit, not on an export of it.
AI assists, people decide
Nothing the AI proposes reaches your catalog until a person approves it, item by item. You are always in control of what ships.
Model-driven, outcome-first
We start from your business requirements and the outcomes you care about, not the plumbing underneath them. The generated code is a derived artifact, like a compiled binary.
Built to ride the AI curve
Models improve every few weeks. DeltaVault is built to absorb each leap in quality rather than be outrun by it.
Bring us your ugliest source
The fastest way to judge any of this is against your own data, not a tidy demo schema. Email hello@deltavault.ai to see DeltaVault run on your sources, or start with the blog if you want the argument in full.