WINDWAY DATA

DATA THAT CAN BE PUT TO THE TEST

Verified data
for agents that
have to work.

Windway Data builds verified environments, trajectories and evaluation datasets for game AI, autonomous agents, reinforcement learning and simulation teams. Replayable environments, observable failure and recovery, deterministic verifiers and provenance.

Windway Data metallic crested bird and W emblem
State, action, failure, recovery, verified: the product structureSTATEACTIONFAILURERECOVERYVERIFIED
STATE TO VERIFIED OUTCOMEFULL STRUCTURE
Illustrative environment, failed action, recovery and verificationStructural explanation, not a live agent, production episode or performance score. All narrative text remains available below.BLOCKEDSTATE AGOALSTATE Breplay · verifier_result · provenance
PASS · DIAGRAM FIXTUREfixture hash d3e71bd311b6
Illustrative replay using GameOps field structure. No production episode or gameplay is published.
How to read this scene

The layout is an original deterministic diagram fixture. The blocked move is rejected; a valid detour reaches its goal. PASS/FAIL here describe that fixture only. Production quality evidence is shown separately. Field names come from the admitted GameOps contract; no private task values or episode IDs are copied.

01 / state

Start with a state.

A controlled initial state makes the task answerable. Define the goal, the available actions and the constraints before an agent acts.

initial_state · objective · available_actions

02 / action

Observe what changes.

An action is a transition in the environment. Record the state it reaches, not only the explanation an agent gives.

reference_trajectory · observation

03 / failure

Keep the interruption.

A blocked action or an invalid assumption belongs in the record. The failure state tells the next step where recovery must begin.

failure_type · failure_step · failure_state

04 / recovery

Test the way back.

Recovery is another trajectory. Its actions must restore progress under the same environment constraints.

recovery_trajectory · final_state

05 / verified

Make the result checkable.

Replay, verify and retain provenance. A convincing answer becomes useful evidence only when the outcome can be reproduced.

verifier_result · environment_hash · provenance

CURRENT PRODUCTION
Windway GameOps

100unique verified GOLD episodes

5 / 5quality gates at 100/100

See the evidence

THE DIFFERENCE IS IN THE STATE

A convincing answer isn’t
always a correct outcome.

Agents act in worlds.
Their data should, too.

We build around what changes: the state before an action, the failure that interrupts it, the recovery that follows, and the evidence that the result holds.

Replay it. Reset it. Verify it. Then decide what you can trust.

How we work

A FOCUSED SET OF TOOLS

01

GameOps Pilot

Verified recovery trajectories and deterministic environments.

02

Custom Data Production

Private data built around an agreed task and acceptance gate.

03

Private Evaluation

Replayable protocols for the behavior you need to measure.

04

Continuous Data

Versioned additions, held to the same quality gates.

WHAT AN ENGAGEMENT LOOKS LIKE

GameOps Pilot

Problem
Game agents recover in ways a transcript alone cannot verify.
What we produce
Versioned state/action/failure/recovery episodes with executable checks.
What you provide
Your task family, engine interface, permitted inputs and acceptance criteria.
What you receive
An agreed episode package, replay interfaces, QA evidence and provenance.

Private Evaluation

Problem
A convincing answer may hide an incorrect environment outcome.
What we produce
Held-out tasks and a reproducible evaluation protocol with deterministic checks where applicable.
What you provide
The agent interface, evaluation question, constraints and authorized evaluation material.
What you receive
An executed evaluation report, per-task evidence and explicit limitations; no unrun scores.

Custom Data Production

Problem
Generic training examples miss your environment and failure modes.
What we produce
A scoped private dataset with agreed tools, states, failure taxonomy and quality gates.
What you provide
Your schema, environment, mechanics, action space, failure modes, evaluation criteria and delivery requirements.
What you receive
Versioned accepted records, schemas, provenance and acceptance evidence.

Continuous Data

Problem
A changing agent or environment needs consistent additions to its data.
What we produce
Recurring, versioned additions under an agreed acceptance protocol.
What you provide
A validated baseline, change priorities and a delivery cadence.
What you receive
Incremental releases, change summaries and QA for each delivery; volume is agreed, not promised.

FOR LABS, STUDIOS & TEAMS BUILDING AGENTS

What would you need
to prove next?

Let’s define the evidence