Text-to-SQL Consulting
Natural language to warehouse SQL that is evaluated, dialect-correct, and blocked before it can mutate or wander.
- Service
- Analytics AI
- Industry
- Enterprise
- Updated
- 2026-08-25
- Engagement
- 4 wks
Text-to-SQL consulting helps analytics teams put natural-language warehouse queries into production with a semantic layer, dialect-aware generation, a read-only database role, dry-run/EXPLAIN gates, and a golden-query eval that blocks prompt drift — typically one bounded metric domain in four weeks, with the client owning the metric definitions and eval suite.
Why teams pick this engagement
Analytics AI × EnterpriseRead-only by construction
The generated statement runs as a SELECT-only warehouse role. DDL, DML, and EXPORT are parser-blocked before they reach the engine.
Semantic layer in context
Metrics, joins, and certified tables are packed from your dbt/LookML/Cube definitions so “revenue” means the metric you already argue about in BI.
Explain the SQL, cite the metric
Every answer shows the statement, the tables used, and the metric definition. If the layer has no match, the system refuses instead of joining random facts.
Golden queries as CI
A held-out set of analyst questions with expected SQL or result hashes fails the release when a prompt change drifts a certified metric.
Analyst still owns writes to BI
Saved queries and dashboard publishes stay a human action. The agent proposes SQL; it does not silently rewrite a Looker explore.
One domain in four weeks
Finance, growth, or ops — one warehouse schema family, dry-run, evals, handover — not “chat with the entire lake.”
Key takeaways
- 01
Text-to-SQL fails in production when the model invents joins; it works when certified metrics and tables are the only things it is allowed to use.
- 02
The warehouse role must be SELECT-only, and the parser must still block DDL/DML before execution. Defense in depth is the point.
- 03
Dry-run or EXPLAIN on every statement catches fan-out joins and full scans before they hit the slot budget.
- 04
A golden set of real analyst questions is the release gate. Demo accuracy on five cherry-picked prompts is not evaluation.
- 05
One domain — finance, growth, or supply — in four weeks is the right first slice; “ask anything of the lake” is how you get a runaway query and a wrong number.
What the engagement covers
How we work
- 01
Discover
Certified metrics, warehouse dialect, BI tools, row-level security, and the single domain we will ship.
- 02
Design
Semantic packing, read-only role, dry-run policy, and golden-set plan reviewed with analytics engineering.
- 03
Build
Agent, catalog packing, and warehouse integration in a sandbox project with weekly query reviews.
- 04
Validate
Golden-query pass rate, fan-out join tests, injection tests, and bytes-scanned budgets.
- 05
Enable
Production for the bounded domain, runbooks, eval ownership, and 30 days on-call.
Take the playbook with you
The working documents from real engagements — free, in exchange for an email. They’re useful whether or not we ever talk.
Text-to-SQL Semantic Layer Packing Guide
How to expose dbt metrics, certified tables, and join paths to a generative SQL agent without dumping the whole catalog into the prompt.
Get the guide ·Golden Query Eval Worksheet
Template for 40 analyst questions with expected SQL, dialects, and result checks we use as a CI gate.
Get the worksheet ·