At Fractal, we recognize this disparity and have been working to unlock the emergent reasoning abilities of generative models specifically to work with structured data. By steering AI models through specialized tools and carefully crafted natural language prompts, we find that these models can be surprisingly successful in providing insights from structured data using only natural language conversation.

Avalok.ai has been made generally available to Fractal employees, and our business analysts, data scientists, and data engineers have been testing Avalok.ai. We have seen enough success from an alpha release to publish these findings.

The tool has three modules designed to enhance data analysis and drive invaluable insights.

Avalok is built using Amazon RDS, Amazon Dynamo DB, Amazon Neptune, AWS Lambda, and AWS S3.

Along with highlights, each module also supports:,

• Error recovery at the code generation step – if the code generation step fails, the agent automatically captures the stack trace and reattempts, knowing what failed the last time.
• Invoking LLMs to detect if user questions are toxic, inappropriate, or irrelevant based on the data.
• Figuring out whether it should respond with an inline text, a markdown table, or a file download depending on the output size generated.
• Retaining the context of the conversation and building on earlier queries (with mixed success in our current testing).
• Providing the users with a tracing of its thought process and tool use – often offering the exact hint necessary to improve a failing query to make it successful.
• Saving and resuming a conversation later.

We have integrated user feedback into our data collection process, including prompts, responses, and thought traces. This data can be used to fine-tune the LLMs in the future.