Doram vs WisdomAI
WisdomAI guides an AI that writes SQL with reviewed context. Doram compiles the SQL from approved rules.
DoramChecked on 17 October 2026. Every fact about WisdomAI comes from its public website and docs, unless we say it is third party. The links are at the end.
The short answer
WisdomAI is an AI analyst for large companies. It builds “context” from what your teams already have, like dbt, LookML and past queries, and an admin accepts each piece. Then an AI model writes the SQL, guided by that context.
Doram also builds the model for you and asks people to approve it. The difference is the last step. In Doram, 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.
At a glance
| Question | WisdomAI | Doram |
|---|---|---|
| What it is | An AI analyst with a context engine | A context layer with its own AI analyst |
| Who builds the model | WisdomAI suggests it. An admin accepts each piece. | Doram drafts it. Your team approves what matters. |
| What it reads | Schema, dbt, LookML, Power BI and Tableau files, GitHub, query history, chats | Schema, query history, dashboards, docs, Slack, Jira, CRM |
| How the AI gets the number | The AI writes SQL, guided by context and reviewed examples | The AI picks approved facts. A semantic compiler writes the SQL. |
| Review before changes go live | An admin accepts suggestions. Accepted items go live at once. | Review by risk, with the evidence and impact of each change |
| Other AI tools | MCP, GraphQL API, React SDK | MCP and API |
| Where it runs | WisdomAI cloud, your cloud, or on premises | Doram Cloud, or your own cloud account |
What WisdomAI does well
- It starts from what you have. Upload dbt, LookML, Power BI or Tableau files, or connect GitHub, and WisdomAI pulls out metrics, joins and descriptions. Each one stays a suggestion until an admin accepts it.
- It tests itself. Reviewed queries, test sets and a health score re-run known questions and flag stale or clashing definitions.
- Many ways to deploy. It runs in its cloud, in your cloud account, or fully on premises, with your own AI model. Row rules and column masking apply in chat, dashboards and MCP.
Where they differ
How the number is made
WisdomAI says plainly that an AI model writes the query. Its context, its metric definitions and its reviewed example queries guide that AI. Row rules are added to every query after it is written.
In Doram, the AI never writes the SQL. It picks approved facts, and the semantic compiler writes the query from them. The same question gives the same answer, and any past answer can be replayed on the version that made it.
Where the rules come from
The two read many of the same things: schema, dbt, query history and past chats. WisdomAI also reads Power BI and Tableau files. Its marketing names Notion, Confluence, Slack and Jira as sources too, but its docs do not list them yet.
Doram reads documents and the tools where decisions are made, like Slack, Jira, the CRM and billing. It keeps the reason behind each rule and shows it with the answer.
How changes are approved
In WisdomAI, an admin accepts each suggestion, and its docs say an accepted item goes live at once, with no staging step. We found no way to roll back a context change.
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. A past answer can always be replayed on the version that made it.
Access
WisdomAI’s marketing says it inherits the source system’s permissions. Its docs describe row rules and masking that you set up inside WisdomAI.
Doram reads your warehouse grants at every sync, so a revoke reaches it straight away. When two rules disagree, the stricter one wins.
Accuracy claims
WisdomAI claims “95%+” accuracy in production. In its own test against Claude Code, on 30 questions, it scored 77% against 73% with the same documents, and 93% after learning on those same questions. Doram does not publish an accuracy number yet.
Who should pick which
Pick WisdomAI if you need on-premises deployment, your models already live in dbt, LookML, Power BI or Tableau, and you are fine with SQL that an AI writes under guidance.
Pick Doram if you want SQL compiled only from approved rules, changes routed by risk, and every AI in the company reading the same rules.