## Text-to-

## Unlock complex analytics and business intelligence using natural language with Adaptive Engine.

## Challenge

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.

## Solution

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.

## Workflow

### 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.

## Explore more

## Use Cases

### Use Case

**RAG Knowledge Retrieval**  
Enable access to enterprise knowledge at scale. Achieve best-in-class retrieval accuracy and eliminate hallucinations.

### Use Case

**Customer Support**  
Transform your customer experience with personalized AI agents. Improve CSAT and reduce compliance risks.