Trajectory data for frontier agents

The data engine driving frontier AI agents.

Agents fail at long, multi-step tasks because there’s too little data that captures human reasoning — not just what a person clicked. Nava is the system and workforce that turns frontier model failures into verified training data.

The recording environment

Every task is performed by a human, inside our sandbox.

A proprietary desktop environment captures screen state, DOM events, and live voice narration of intent — keeping every trajectory grounded in real reasoning, inside the real applications people use.

How it works

We remove the human bottleneck from data collection.

Data vendors are stuck because a person hand-designs every task and someone else manually writes an eval for it. Nava lets AI design the task — never the data — so a human’s real reasoning stays the ground truth.

  1. 01

    Ingest failures

    Customers upload failing evals and benchmark traces from their own models.

  2. 02

    Cluster & generate

    We cluster failure modes and use AI to generate specific tasks with detailed instructions, so workers ramp on unfamiliar work with minimal training.

  3. 03

    Human performs

    A person completes each task in the sandbox, capturing screen state, DOM events, and live voice narration of intent.

  4. 04

    Verify & ship

    An AI-written mini-eval checks each task, workers flag bad evals, and sandboxed tasks resolve to a clear end state before data feeds back into the customer’s models.

The core principle

AI designs the task, not the data. Synthetic data is hard to verify and risks contaminating what we sell — so a human performs every task, while AI handles tasking, instructions, and per-task grading. Quality is checked at the single-task level; expert humans grade only at the batch level. Higher throughput, without giving up ground truth.

Why Nava

The underbuilt layer is the engineering of data collection itself.

Quality and throughput

Every competitor trades QA against volume. AI-generated tasks and per-task mini-evals let us hold both at once.

Narration with telemetry

Capturing high-fidelity voice narration alongside visual telemetry is newly possible — and it is where the reasoning lives.

Targeted, not general

Tasks come directly from a customer’s model failures, so the data hits exactly what frontier models miss instead of aging as models improve.

A lower training bar

AI writes the instructions too, so headcount scales quickly across a broad, digitally literate workforce without sacrificing quality.

Turning model failures into the fuel for the next generation of agents.

We are building infrastructure for the frontier. Let’s talk.

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