See into now. Build for tomorrow.

The shift

Your next user
isn't a person.

Agents now search, book, buy, and call APIs on someone's behalf. The interface is no longer your screen. It is how your system behaves when another machine is driving, and that raises two questions no dashboard answers today.

Assurance

Will it hold?

Your system, inside integrations you have never tested, driven by agents you do not control. Find the break before your users do.

Selection

Will you be chosen?

When an agent picks between you and a competitor for the same request, know how often you win, where you lose, and what changes it.

Two questions. One way to answer both before the real world does: simulation grounded in your own data.

The difference

Evals score answers. Monitoring watches production. O4VE runs worlds.

Model evalsProduction monitoringO4VE
What is testedModel evalsA prompt and a responseProduction monitoringLive traffic, after releaseO4VEYour deployment, in a world built from your own data
When you learnModel evalsBefore shipping, in isolationProduction monitoringAfter users feel itO4VEBefore shipping, under your real conditions
What it answersModel evalsIs this output good?Production monitoringWhat just broke?O4VEWhat will happen, and what should change?

The system

Your data builds the world. Your agents run in it as they ship.

A world is not a mock picked off a shelf. O4VE generates it from your traces, logs, and deployments, proves it reproduces your baseline, then mirrors how your agents are actually deployed, over MCP, A2A, or plain APIs, across every system they touch.

  1. Your dataTracesProduction traces · Logs and configs · Baseline outcomes
  2. World builderWorldsGenerated from data · Fit to your baseline · Versioned per run
  3. HarnessDeploymentAgents as deployed · MCP · A2A · APIs · Across systems
  4. EngineSweepsEvery variable · Thousands of seeds · Replayed, not rerun
  5. IntelligenceResearchPaired comparisons · Root causes · Attribution
  6. OutcomeActionCI gate · Alerts · Decisions

Start from your baseline, not a blank page.

O4VE reads the traces, logs, and deployment configs you already have and generates worlds that behave like your production. Before anything is varied, each world has to reproduce your own baseline outcomes.

What you take forwardA world built from your data that matches what really happened

Ownership

Hold your data. Own your system. Run it wherever it makes sense.

Every edge of O4VE is an adapter: where data comes from, how agents are deployed, which models answer, where results land. Your data stays in your stores and the worlds are grown from it. Run the whole system yourself with our SDKs, or let us run the scale.

Deploy through
MCPA2AHTTPQueues
Data in
TracesLogsWarehouseObject store
Results out
CIAlertsYour BIDecision log
Models from
Any providerSelf-hostedFine-tunes
O4VEOpen spec · adapters on every edge

Keep your data where it lives. Hand us the scale.

  • Elastic workers sized to each sweep, back to zero between runs
  • A replay cache across all your runs, so no trajectory is paid for twice
  • Simulated counterparts on right-sized models; your agents keep theirs

Either way, the same spec, the same adapters, and the same results. Start on one side and move to the other without rebuilding a thing.

Setup

Start from what you already have. Your data, your agents, one question.

  1. 01

    Connect your data

    Point O4VE at the traces, logs, and deployment configs you already keep. That history is the raw material for every world.

  2. 02

    Grow the world

    O4VE generates worlds from that data and calibrates them until they reproduce your baseline. Nothing is varied until the world agrees with what really happened.

  3. 03

    Mirror the deployment

    Your agents run unchanged, wired the way they ship: MCP servers, A2A handoffs, plain APIs, across every system they touch.

  4. 04

    Ask one question

    Name the change you are weighing, what must hold, and the outcome you care about. O4VE plans the sweep and researches the result.

experiment.yamlIllustrative
question: Should we ship the candidate model?
baseline: production/last-30d  # your traces
world: 
  from: [traces, logs, deploy.yaml]  # generated, not picked
  calibrate: baseline  # must reproduce it first
harness: 
  agents: ./agents  # as deployed
  via: [mcp, a2a, http]
vary: 
  model: [current, candidate]
measure: [task_success, cost_per_task]
guard: [refund_rate]  # must not regress
✓ world matches baseline · 2 configurations · guardrail set

Aggregation

Thousands of runs. One answer you can defend.

Every scenario runs on both configurations under identical conditions. Pairing outcomes one to one cancels the noise that sinks ordinary A/B tests, so a real effect shows up with far fewer runs, and arrives with an interval.

BaselineCandidate192 scenarios eachBaseline134/192Candidate149/192
+7.8 pts95% interval +3.0 to +12.619 pairs won · 4 lost
  1. 01Run every scenario on both configurations, same seed, same world.
  2. 02Pair the outcomes. Only the scenarios that disagree carry signal.
  3. 03Aggregate into an effect with an interval, not an anecdote.

Illustrative data, generated from a fixed seed. The interval is computed from the dots shown.

Scale

Scale to the question. Pay only for what is new.

A real question explodes into a sweep: every model, tool, scenario, and seed. Most of that space has been run before or does not matter. O4VE scales out for the part that does, and removes the rest before it costs anything.

  1. The naive sweep

    Agents × models × tools × scenarios × seeds. The full space, if you ran all of it.

  2. Replay cache

    Trajectories are stored by content. Any step already computed, by any experiment, resolves from storage instead.

  3. Importance sampling

    Compute goes where behavior diverges, not to re-confirming what already holds.

  4. Right-sized worlds

    The simulated services run on small, distilled models. Only your system keeps the model it ships with.

computed computed cheaply replayed not neededProportions illustrative

Elastic

Sweeps fan out across as many workers as the question needs, then scale back to zero.

Cost follows the question

You pay for the trajectories that are new, at the model tier each part of the world needs.

Deterministic replay

Any result can be rerun exactly: same world, same seeds, same trace.

Isolated per tenant

Your agent logic and data never share a data plane with anyone else.

Proof

Every claim carries its evidence, and says how strong it is.

A simulated effect is a hypothesis. O4VE tags each one with the rung of evidence behind it, follows it into production, and keeps score of its own accuracy.

L0

Before and after

Nothing. A direction, not a proof.

L1

Interrupted time series

Rules out trend and seasonality.

L2

Randomized rollout

Rules out everything but relevance.

L3

Paired counterfactual replay

The same real transactions, replayed through both configurations. Every confound, at a fraction of the sample.

Attribution certificateIllustrative
  1. ExperimentWorld version, baseline fit, seeds, the full sweep
  2. DecisionThe recommendation and its predicted effect, with an interval
  3. DeploymentConfig diff, rollout share, anything shipped alongside
  4. OutcomeMeasured effect on your metric, and the rung it was measured at
  5. VerdictConfirmedInconclusiveRefuted
Refuted is a valid verdict. It is shown to you, not buried.
predictedrealized

The calibration ledger

Every decision pairs a predicted effect with the realized one. The ledger learns how each world's simulation transfers to production, and corrects the next prediction.

See into now.
Build for tomorrow.

O4VE is taking shape: simulation and intelligence for systems that machines drive.