Text-to-SQL
Unlock complex analytics and business intelligence using natural language withReinforce Engine.
Query Success
Off-the-shelf LLMs struggle to query real-world databases.
To quickly access enterprise information in data lakes and warehouses, analysts use LLMs to convert natural language questions into structured queries.
However, models unfamiliar with your unique schema will deliver faulty queries, costing time and money.
Efficient text-to-SQL models specialized to your schema.
Tune models with reinforcement learning from execution feedback (RLEF) to outperform GPT-4o on text-to-SQL use cases, improving Llama 3.1 8B by 55% or more.
Not only are these models more accurate, they are up to 12x cheaper than closed API providers.
SQL Workflow
LEARNING FROM EXECUTION FEEDBACK
SUCCESS
QUERY PARSABILITY
Unparalleled accuracy
Reinforcement fine-tune LLMs with execution feedback on your schema for unparalleled query accuracy.
Accelerate to production
Alternatively, use a custom AI judge to evaluate query success and leverage feedback for training.
Reduce inference cost
Use small models specialized to your schema, instead of overloading API prompts with costly context tokens.
Ask your database in plain English.
Watch a model tuned on your schema turn real questions into queries that run.
Explore more
Use CasesQuestions teams ask about text to sql
What does text to sql actually involve?
We design the workflow, build the agent and the evaluation around it, run it in shadow mode against real traffic, then hand it over with a runbook. You end up owning a running system for text to sql plus the evidence it works, not a proof of concept that needs rebuilding.
How long before text to sql is live?
Four weeks is the standard implementation: discovery in week one, environments in week two, a shadow-mode pilot in week three, handover in week four. Most teams see their first agent running against real data by week three.
Do we need an ML team to run this?
No. Most clients start with strong software engineers and no ML specialists. The engagement is built so your existing team owns the system afterwards — we train them while we build rather than handing over documentation at the end.
How do you know it is working?
Every system ships with an evaluation suite: golden datasets built from your real cases, rubric-driven scoring, and regression gates in CI. Quality becomes a number you track per release rather than an opinion, and drift pages you the way a failing test would.
What happens when the agent gets it wrong?
Low-confidence and high-stakes cases route to a human queue by design. Every failure is captured with full trace context, and those traces become new evaluation cases, so the same mistake is caught automatically next time rather than recurring.
Does this run in our environment or yours?
Yours. Deployment happens inside your cloud perimeter, against your data stores and your identity provider. We integrate with the stack you already run rather than asking you to move anything into ours.
Who owns what we build?
You do. Fine-tuned weights, datasets, evaluation suites and runbooks are yours, handed over at the end of the engagement. There is no lock-in that requires us to keep the system running.
How is this priced?
A platform subscription plus a fixed-scope implementation fee, quoted in writing before work starts. Implementation is priced by engagement rather than by the hour, so a slower week costs you nothing extra.
We tried something like this before and it failed. Why would this be different?
Most failures are not model failures — they are missing evaluation, no human fallback, and no way to tell whether a change made things better. Those are the parts we build first. If we cannot define how success is measured for text to sql, we say so before taking the work.
What do you need from our team?
One process owner who knows the workflow end to end, one engineer with access to the systems being integrated, and a weekly 45-minute review. That is genuinely it — no standing project committee.