Understand what your AI is actually doing.

Most teams running AI can't answer basic questions about it: what their agents actually did, which prompts and tools work, what's really in the data they train and evaluate on. The data with the answers is huge, and most of it is never read. Hyperparam builds the tools that read it: the Hyperparam app to explore and transform AI data, and HypAware, which collects, stores, and analyzes every session so your agents can read the findings and act on them.

Where do you want to start?

Work with AI datasets

Explore and transform them in your browser. Open multi-gigabyte files, add AI-generated columns, export what you keep.

Work with AI agents

Collect, store, analyze, and act on every session your agents run. HypAware finds what's broken, hands your agent the fix, and checks that it held.

Try HypAware →

The answers are in the data. Your tools can't read it.

For decades the data we stored was rows and columns you analyze by aggregation, and a whole industry grew up around it: databases, warehouses, dashboards. AI data is not that. Open an agent log or an LLM training set and you find a couple of structured columns and then one enormous text column where every cell is a whole conversation, a reasoning trace, a source file, and there are millions of them.

SQL>find the sessions where the human was frustrated

ERROR: no such function

SQL can count the rows. It cannot read them. Understanding AI data needs infrastructure that both scales with the volume and reads the text the way a person would. That is what Hyperparam built, and both products run on it.

Search by keyword, by meaning, or with SQL, then run a language model over the rows that match. In Hyperparam it all runs in your browser, straight against the files in your bucket, reading only the bytes each query needs.

Agents and AI apps

Claude Code

Cursor / Copilot

Codex / agent SDKs

MCP servers

Production APIs

AI gateway

HypAware

Collect

Store

Iceberg tables

Analyze

Act

Hyperparam

Explore

Transform

Parquet · JSONL · CSV

S3 · Hugging Face · laptop · etc.

HypAware collects every session into open Iceberg tables, analyzes them, and hands your agents the fixes. Hyperparam opens those tables and any Parquet, JSONL, or CSV, wherever it lives. What you find flows back into both.

Explore. Surface. Improve.

2.3M traces, loaded in under a minute, then filtered, clustered, and annotated with AI assistance.

Used by

Hugging Face

Eurostat

Le Figaro

gridviz-parquet

Source Cooperative