About Hyperparam

The workbench for LLM datasets

Production LLM systems generate massive, multi-gigabyte datasets at a scale teams can’t realistically inspect. As AI systems scale, they generate quadrillions of tokens of unstructured text every year as a byproduct of real-world use.

These datasets contain valuable information about failures, regressions, and output quality. LLM logs capture where conversations go off the rails, how updates affect outputs, and where inconsistencies emerge in production. But their size and structure make manual inspection impractical. No single person can read through millions of rows of text, and existing tools like Jupyter notebooks and DuckDB-Wasm are poorly suited to working with unstructured data at this scale.

Building the AI dataset workbench for unstructured text

Hyperparam is a lightning-fast, first-of-its-kind AI dataset workbench for multi-gigabyte LLM logs, chatbot transcripts, and model outputs. Because it runs entirely in the browser, AI teams can work interactively with large unstructured text datasets in real time. This is only possible thanks to Hyperparam’s mission to simplify the data engineering stack, moving as much as possible to the browser. By focusing on entire datasets, it enables teams to inspect LLM logs end-to-end, curate datasets based on real production outputs, and compare the impact of changes using the same inputs.

As AI systems continue to scale, the work of building and optimizing them depends increasingly on the datasets they produce. Logs, transcripts, and model outputs become the artifacts teams work from when deciding what to keep, what to change, and what to ship next. Hyperparam is built to support rapid dataset-level inspection, curation, and comparison of those artifacts at scale.