Data platform, analytics engineering and BI teams
DeepGarden for data and analytics teams
You are not short of tooling. You are short of a way to let people ask questions without either building them a dashboard or trusting them to define margin correctly on their own.
The dashboard estate is a liability you keep paying for
The estate grows because building a new view is easier than finding out whether an existing one already answers the question.
Now the queue is for access, for definitions and for explaining why two dashboards disagree.
Without a semantic layer it guesses at joins and definitions; without a permission model it either sees everything or nothing; and without provenance nobody in a regulated business can act on what it says.
Where the answer is currently spread
- Your warehouse or lakehouse
- Operational databases you allow read replicas of
- Existing dbt or semantic-layer definitions, as an input
- Identity provider, for the access model
- External datasets, including DeepGarden Data
Who is involved
- Head of data
- That self-service does not mean uncontrolled definitions.
- Analytics engineer
- That the semantic model is the one place logic lives.
- Data platform engineer
- Query load, read-only guarantees and what touches production.
- Security architect
- How access is enforced, and what is logged.
Questions this makes routine
These all cross a system boundary, which is why they currently get asked once a quarter rather than once a week.
- Which definitions are actually in dispute between departments?
- Which questions are people asking that our data cannot currently answer?
- Which datasets carry the load, and which are maintained for nobody?
- Where is the same analysis being re-run by hand every month?
- What would we have to instrument to answer the questions that came back unknown?
What DeepGarden does about it
What changes, specifically, and what lands in front of the person who asked.
A semantic layer that is enforced, not advisory
Metrics carry their definition, canonical calculation, dependencies and caveats. A metric is only offered to a question when every table it depends on is inside that person's scope, so a cross-dataset definition degrades gracefully rather than leaking.
Validation between generation and execution
Generated SQL is parsed and checked before it runs — read-only, inside the allowlisted tables, structurally sound — and executed under a role with no write grants. The guarantee is structural rather than a prompt instruction.
The unanswered questions, as a backlog
Every question that returned an unknown is captured with its context. That is a data roadmap written by the people who needed the data, which is a considerably better input than a planning workshop.
Why the output holds up
The parts of the governed path that matter most in this particular setting.
- Read-only enforcement is enforced at the database role, not only in the query checker.
- Every executed query is stored with its result, row count, duration and timestamp.
- Answers are verified against returned rows before display; unreconciled figures are flagged rather than published.
- Definitions are versioned, and a change is attributable to whoever made it.
How implementation would work
A first domain in roughly six to ten weeks
Try to break it.
Bring the questions you would use to catch a text-to-SQL tool out. That is a more useful hour than a scripted demo.