We built the layer your monitoring stack keeps promising.

Every observability vendor's homepage implies the same thing: install us and you'll know what's wrong with your systems. Then a database incident happens, and what you actually have is forty dashboards of symptoms and a senior engineer starting a 2–4 hour investigation at 3am. The what is beautifully rendered. The why is still manual labor.

dbexpertAI exists to close that gap for databases specifically. Databases are where the diagnosis problem is hardest — every engine fails in its own idiomatic ways — and where expert investigation is most encodable: a veteran PostgreSQL DBA checking for bloat-driven plan flips follows the same evidence trail every time. So we encoded the trails.

What we believe

• Diagnosis is a deliverable, not a dashboard. The unit of value is a root cause with evidence and resolution steps — something you can act on without further investigation.
• Deterministic beats probabilistic on production databases. Expert-curated detection paths give the same answer for the same evidence, and show their work. Operators can trust what they can verify.
• Safety is architectural. Read-only credentials, on-prem execution, data never leaving your network — these are structural properties, not policy promises.
• Tools should prove themselves. The demo is the pitch: connect a database, wait 90 seconds, read the diagnosis. That's also why the first database is free, forever.

Who's behind it

Three people, and an origin story that explains the product better than any feature list: co-founder Yoram Dan spent 11 years quietly automating his own root-cause investigations, it ran in production at a large energy company for a year, and it only worked if you were Yoram. Read how that became this.