Talk to your data means a business user types "what was net revenue by region last quarter, excluding refunds?" and gets a correct answer, the chart, and the SQL that produced it. Under the hood it is text-to-SQL: an LLM translates the question into a query against your warehouse, lakehouse, or operational database, runs it, and explains the result. The outcome is analysts freed from ad-hoc request queues and decision makers who stop waiting on dashboards that never quite have the right filter.
Most conversational analytics pilots stall for the same reason. The demo works on ten hand-picked questions, then real users ask about a metric with three competing definitions, the model joins on the wrong key, and the answer looks plausible but is wrong. Production-grade means every generated query is grounded in your schema and metric definitions, checked before it runs, scoped to what the user may see, and measured against a growing golden question set.
What We Build
Schema-Grounded SQL Generation
The model never sees a raw information_schema dump. We build a retrieval layer over table and column documentation, metric definitions, and previously approved queries, and use it to ground each generation. Ambiguous terms resolve to a semantic layer, so "active customer" means one thing whether finance or sales asks. This is RAG applied to your data model rather than to documents.
Verification Before Execution
Generated SQL is parsed, dry-run against the engine, and rejected or repaired when it references unknown columns, produces cartesian joins, or violates cost limits. Queries get row limits, timeouts, and read-only credentials by default. The user sees the query alongside the answer, so any number can be traced back to how it was computed.
Permissions and Row-Level Security
Conversational access does not bypass governance. Queries run under the asking user's identity, with row-level security and column masking enforced by the database, not by a prompt. Audit logs capture the question, the SQL, and the result set size, which is what a compliance team will ask for first.
Evals, Cost Control, and Answer Presentation
A golden set of questions with known-correct SQL and results runs on every prompt, model, or schema change, so regressions surface before users find them. We track cost per question, cache repeat queries, and route simple questions to smaller models. Answers come back as a table, a chart where appropriate, and a short explanation citing the query and tables used.
How It Runs on Your Stack
We build against the engines you already run. On ClickHouse, Snowflake, Databricks, and Athena over Iceberg, the focus is dialect-correct SQL, partition-aware predicates, and predictable per-question cost. On PostgreSQL and other OLTP databases, the priority is protecting the production workload with read replicas, statement timeouts, and strict query budgets. The semantic layer can be an existing one (dbt metrics, a BI tool's model) or a lightweight one we define with you from the questions people actually ask.
On AWS, the typical build uses Amazon Bedrock for generation, Amazon OpenSearch for retrieval over schema docs and query history, and your existing Athena, Redshift, or Aurora endpoints for execution. Our GenAI and RAG fast track on AWS covers this pattern as an AWS Advanced Tier Services Partner with the AI Services Competency. If the underlying data platform itself needs work first, our data to analytics and AI practice handles the pipelines, modeling, and warehouse tuning that a reliable answer depends on.
Proof
Bank Poalim brought us in to turn AI initiatives into practical business value. Their team reported faster access to information, quicker ticket resolution, and a better understanding of logs across internal AI projects, which is exactly the outcome a talk-to-your-data system exists to produce: less time finding the number, more time acting on it.
From Idea to Production in Weeks
Talk-to-your-data projects run through the AI Launchpad Launch Sequence. Use-Case Fit picks the data domain and the user group with the clearest payoff, audits schema documentation and metric definitions, and sets accuracy targets on a first golden question set. Build on Your Data wires up retrieval over your data model, SQL generation, verification, and permissions against your real databases, in your cloud or on-prem. Harden for Production adds evals, cost and latency tuning, guardrails against runaway queries, and observability. Launch and Run puts it in front of users, hands over, and keeps improving accuracy as new questions arrive. If your users need answers from documents rather than tables, see the sibling AI knowledge base page.
Frequently Asked Questions
How accurate is text-to-SQL with an LLM?
Out of the box, accuracy on a real enterprise schema is not good enough to trust. Grounding generation in documented tables, metric definitions, and approved past queries, then verifying the SQL before it runs, is what moves it into a usable range. We measure accuracy on your own golden question set rather than quoting a benchmark number.
Can business users break production databases by asking questions?
Not in the systems we build. Queries run with read-only credentials, statement timeouts, row limits, and a cost budget, and on OLTP databases they run against replicas. Row-level security and column masking are enforced by the database under the user's identity, so the LLM cannot grant access it does not have.
Do we need a semantic layer before starting?
No, but you need agreed metric definitions for the questions that matter. If you already have dbt metrics or a BI semantic model we build on it. If not, we define a small semantic layer during the first stage from the questions people actually ask, which is usually a short list.