About Hyperparam

Collect, store, and analyze your AI logs

Agents, coding tools, and chatbots generate quadrillions of tokens of conversational and tool-call data every year. Buried in those logs is everything teams need to understand what their AI is actually doing in production: where conversations go off the rails, where tool calls fail, which prompts burn tokens, and how each release shifts behavior.

But existing tools are not built for this. Observability dashboards aggregate, sample, and flatten. Jupyter notebooks and warehouse SQL choke on multi-gigabyte JSONL of nested conversations. No one can read through millions of rows of text, and the most important signal often lives in a long-tail 1% you would never spot with sampling.

Two products: collect, then analyze

HypAware is a lightweight, open-source collector that hooks into Claude Code, Codex, Cursor, and anything else that speaks OpenTelemetry, and writes every model call as Apache Iceberg tables. Point it at your own bucket, or use managed HypAware and we run the storage for you, segregated per organization.

Hyperparam is the analysis tool. It reads agent traces, coding-tool transcripts, and chatbot histories straight from where they land, and pairs them with an AI agent that analyzes the logs alongside you. Drill into nested traces, generate derived columns at scale, build SQL views that join across sources (so agent behavior can be correlated with the code, issues, or prompts that drove it), and save reusable analyses as skills. Because everything runs in the browser, teams can iterate on real production traces without moving the data.

As AI systems keep scaling, the work of improving them depends on what their logs reveal. Hyperparam exists to make that revealing fast, repeatable, and grounded in the actual data: explore, surface issues, improve the prompts, tools, and routing, then ship the fix and watch the next batch of logs.

A stack you own

The bigger picture: every company already has a data stack, and none of it was designed for AI. Datadog and Splunk are billed per GB ingested with short retention. Snowflake and Databricks are great at SQL but bad at nested LLM payloads, and the compute is rented. The usual answer is to bolt on yet another vendor (LLM observability, evals, agent tracing, prompt management), each one a copy of your data behind their API.

We think the answer is a stack you can own end to end: open collection with HypAware on top of OpenTelemetry, open storage in Apache Iceberg, and analysis with Hyperparam directly in the browser. The stack is dependency-free, so there is no supply chain to audit. Your prompts and traces stay in your bucket, in your IAM, in your region, with no vendor copy and no per-GB ingest fees. And if you would rather not run storage at all, managed HypAware keeps your organization's data in its own segregated tables in Hyperparam's cloud.

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