(1) Explain Your Data: Feed in dictionaries, dbt, schemas, Confluence docs - we'll understand the business context to your data.

(2) Train Your AI: Fine-tune an LLM (including GPT-4) specifically for your data, increasing accuracy and lowering latency

(3) Trust the Answer: See confidence scores with each AI-generated query, stay in control.

(4) Conduct complex SQL queries

Problem background - Developers struggle to build NL-to-SQL into products because LLMs do not work out-of-the-box; they lack metadata and business definitions. Existing NL-to-SQL tools struggle with context, complexity, and adapting to your data.

For example, given the question “what was the average rent in Los Angeles in May 2023?” a reasonable human would either assume the question is about Los Angeles, CA or would confirm the state with the question asker in a follow up. However, an LLM translates this to:

  select price from rent_prices where city=”Los Angeles” AND month=”05” AND year=”2023”

You can try it now free – no fees, no credit card, no sales pitches, just get the API key and get going. Let us know if it works for you, even your complex queries. (https://console.dataherald.ai/playground)

While the open source version works just fine (https://github.com/Dataherald/dataherald), the hosted API might be a better fit for those looking for: (1) someone else to take care of infrastructure setup, (2) access to an Admin UI console where you can configure and monitor performance, and (3) ability to invite team members to a project.

We're looking for feedback, particularly from anyone who can compare this performance to other NL-to-SQL products. Share your thoughts and join the conversation For more background on the release: https://www.dataherald.com/news/introducing-dhai