Doram vs Zenlytic
Zenlytic lets the AI write SQL and checks it against your model. Doram compiles the SQL from approved rules.
DoramChecked on 17 October 2026. Every fact about Zenlytic comes from its public website and docs. The links are at the end.
The short answer
Zenlytic is an AI analyst for business teams. Its analyst, Zoë, writes SQL against your warehouse, and a layer called the Clarity Engine then checks that SQL against your model.
Doram works the other way round. The AI does not write SQL. It picks approved facts, and a compiler (a program that turns a request into SQL by fixed rules) writes the query. Doram also lets other AI tools read the model itself, not only ask its analyst.
At a glance
| Question | Zenlytic | Doram |
|---|---|---|
| What it is | An AI analyst on a YAML model in Git | A context layer with its own AI analyst |
| Who builds the model | Your team, with Zoë drafting edits from chat | Doram drafts it. Your team approves what matters. |
| What it reads | Schema, query history, dbt, LookML, BI screenshots, files you share | Schema, query history, dashboards, docs, Slack, Jira, CRM |
| How the AI gets the number | The AI writes SQL. The Clarity Engine checks it. | The AI picks approved facts. A semantic compiler writes the SQL. |
| Review before changes go live | Pull requests in Git | Review by risk, with the evidence and impact of each change |
| Other AI tools | One MCP tool that asks Zoë | MCP and API that read the model itself |
| Where it runs | Zenlytic cloud in the US or EU, or in your VPC | Doram Cloud, or your own cloud account |
What Zenlytic does well
- A full product for business users. Chat, live dashboards, Excel, PowerPoint and Word files, Slack, Teams, and agents that send findings on a schedule.
- Fast setup. Zoë can build the model from LookML, dashboard screenshots, the schema and query history, and every change lands in Git.
- It shows what to trust. Each field in an answer is marked as verified or as one Zoë made up on the spot.
Where they differ
Who writes the SQL
Zoë writes SQL freely. The Clarity Engine reads that SQL and makes sure it does not break the rules in your model. Fields that are not in the model show up as “dynamic” fields, so the person reading the answer can see them.
In Doram, the AI never writes SQL. It picks approved facts, and the semantic compiler writes the query from them. The same question gives the same SQL every time, and any past answer can be replayed on the version that made it.
How changes reach production
Zenlytic keeps the model in Git, with branches and pull requests. But a setting called “Allow Edit Production” is on by default, so Zoë can save straight to production for Admin and Develop users.
Doram routes each change by risk. A new word for an existing metric goes live on its own. A change to how net sales is counted waits for its owner. Answers only use facts that are approved or certified.
What other AI tools get
Zenlytic’s MCP server has one tool, ask_zoe. Claude or ChatGPT can ask Zoë a question and get her answer. Its REST API covers admin tasks, not queries.
Doram serves the context layer itself. Claude, Cursor and ChatGPT read the same approved definitions through MCP, and your own agents read them through the API.
Access
Zenlytic sets row and column access from user attributes. A person with no matching attribute is not blocked by default. On Snowflake, queries can run as the real user. We found no masking feature.
Doram reads your warehouse grants at every sync, and masks or hides personal columns. When two rules disagree, the stricter one wins.
Who should pick which
Pick Zenlytic if you want a polished analyst for business teams today, your data team likes a YAML model in Git, and you are fine with SQL that the AI writes and a rules layer checks.
Pick Doram if you want SQL compiled only from approved rules, and you want every AI tool in the company to read the same rules directly.