Doram vs Veezoo

Checked on 17 October 2026. Every fact about Veezoo comes from its public website and docs. The links are at the end.

The short answer

Veezoo and Doram agree on the most important thing: the AI should not write SQL on its own. In both, the AI picks the business terms, and a compiler (a program that turns a request into SQL by fixed rules) writes the query.

They differ in where the business rules come from. Veezoo builds its model mostly from your warehouse schema, dbt and the files you give it, and your data team refines it as code. Doram learns the rules from how your company already works: your queries, dashboards, documents and tools. It keeps the evidence for each rule.

At a glance

What Veezoo does well

• The AI never writes SQL. It writes VQL, Veezoo’s own query language, over the concepts in the model. A rule-based compiler turns that into SQL. You can open any answer and see the VQL and the SQL.
• A real workflow for the model. The model is code in Git. You get branches, diffs, one-click revert and pull request review on your own Git host. Business users can test a branch before it is merged.
• A mature product. Veezoo has worked on this since 2016, with customers like AXA, BMW and Bayer. It offers chat, dashboards, a Canvas for reports and slides, scheduled agents, Slack, Teams and PowerPoint export.

Where they differ

Where the rules come from

Veezoo starts from your warehouse. Its agentic modeling scans the schema and creates classes, relationships and basic measures, and a person confirms each change. It can also pull in dbt descriptions, catalog definitions and files you attach.

As far as its docs show, Veezoo does not read how people use the data. We found no sign that it learns from query history, BI dashboards, Slack, tickets or the CRM. To teach it a correct answer, a person saves the question and its VQL with a Teach button.

Doram reads those places on purpose. Most business rules are not in the schema. They are in the filter every finance query adds, a line on a dashboard, or a decision in a Slack thread. Doram finds the rule, keeps the evidence, and sends it for review.

How changes are approved

Veezoo treats the model like software. Changes happen on a branch, and someone reviews the diff and merges it. This works well when a data team owns every change.

Doram routes each change by risk. A new word for an existing metric goes live on its own. A new join goes live, and one in ten is spot-checked. A change to how net sales is counted waits for its owner. Each suggestion shows its evidence and every metric it would change.

Access

In Veezoo, you write row and column rules inside the model, using user attributes and roles. Its docs do not say that it reads your warehouse permissions, and we found no masking feature.

Doram reads your warehouse grants at every sync, so a revoke reaches it straight away. Personal columns are masked or hidden. When two rules disagree, the stricter one wins.

Where it runs

Veezoo runs as a managed cloud in Europe. On the Enterprise plan, it can also run single tenant in your AWS, Azure or GCP region.

Doram runs in Doram Cloud, in the region you pick, or in your own cloud account. In Doram Cloud, an agent in your network connects out, so you open no inbound ports.

Who should pick which

Pick Veezoo if your data team wants to own the model as code in Git, your rules mostly live in the warehouse and dbt, and you want a mature product with many ways to share answers.

Pick Doram if your rules live in people’s queries, dashboards, documents and tools, and you want them found for you, kept with their reasons, and approved by the people who own them.

Sources

• Veezoo home page
• Knowledge Graph and agentic modeling
• Reliability: how VQL becomes SQL
• Git version control
• Row-level permissions
• MCP server and pricing