PostgreSQL intelligence, as one product
Elevarq collects read-only signals from your databases, detects the problems developing inside them, analyzes each one with models trained for PostgreSQL, and hands your team implementation-ready fixes to review and ship — all in your own infrastructure.
Most PostgreSQL problems begin quietly and surface as incidents only once they are expensive. Elevarq is one product that watches for them continuously and turns what it finds into a fix your team can act on — not another dashboard to watch, and not a chatbot you have to drive.
It is four parts — Signals, Analyzer, Insight, and Workbench — that ship and run as a single deployment.
How it works
One flow, four stages: collect evidence, detect the problem, explain the fix, deliver it as a ticket. Each stage is independently testable.
Signals collects
A read-only collector gathers execution statistics, wait events, connection and configuration state into portable snapshots — in your infrastructure, with no schema changes.
Analyzer correlates
Deterministic detection rules turn each snapshot into evidence-grounded findings — the developing conditions the data actually supports, before they become incidents.
Insight explains
Purpose-trained, version-aware models explain each finding in plain language, validated against its cited evidence — what is wrong, why it matters, and how to fix it.
Workbench presents
Each recommendation becomes an implementation-ready ticket in the tracker you already use. Your team reviews it and ships it through your normal controls.
Built to be right about your database
Advice on a production database is only worth acting on if it is accurate. That is what Elevarq is built for.
Purpose-trained, not general-purpose
Our analysis runs on models trained specifically for PostgreSQL work. They run locally on your own infrastructure, so your data never leaves it, and every result is validated against the cited evidence before you see it. A bring-your-own general-purpose model is built for conversation, not for correctness on your database.
Version-correct by design
Recommendations match the PostgreSQL version you actually run. You will not get syntax, settings, or features that belong to a different release, the version mixup a generic model produces when it averages over everything it has seen.
Grounded and verifiable
Every finding is tied to an explicit rule set and checked against cited evidence, so you can trust what it says and confirm it yourself. Generic model output gives you prose with no evidence trail.
Recommendations, not automation
Elevarq analyzes and recommends. It cannot modify your database and never runs anything against it. Each finding arrives as an implementation-ready recommendation that feeds into your own change process; whether and when to apply it is entirely your team's decision.
The real cost is wrong advice, not the tool
On a production database, the expensive mistakes come from acting on wrong advice, not from the price of the analysis, which is modest. A free or general-purpose tool that gets it wrong pushes that cost onto your team; purpose-trained, version-correct results are built to keep you out of that trouble.
Runs entirely in your infrastructure
Elevarq ships as containers you run yourself, with the model baked into the image so analysis happens locally. Once the images and the offline activation artifact are provisioned, core operation needs no outbound connectivity — no telemetry, no phone-home, and no cloud inference. Installation and updates may pull images from a registry; any ticket integrations you enable are optional and connect only to the tracker you choose. For fully offline operation, Enterprise includes packaged air-gapped update artifacts.