Your assets.
Your evidence.
Your next decision.

A useful twin connects an identified asset to evidence, a declared model or state, a recurring decision and the outcome of an authorized action. The 3D view helps people locate and understand that relationship. Operational readiness comes from validated connections, ownership and feedback, not from visual fidelity alone.

EarthScan Twin is the standalone exploration and engineering investigation experience, with public visual exhibits and invited access to detailed workflows. ES360° AI is a separate EarthScan application. Shared authentication, project transfer and consolidation will be designed later, after the architecture is agreed. A Twin invitation does not grant ES360 access.

No. EarthScan Twin includes authored assets, synthetic operating cases and separately identified public recorded evidence. Your live records, asset identities, permissions and operating procedures require a customer implementation and acceptance. A public demonstration cannot issue commands to your installation.

AI-assisted engineering, robotic inspection evidence and operational data workflows are our initial focus. Start with a recurring task such as preparing an engineering review, reconciling inspection findings or checking incoming data. Wider process-control, reservoir and plant capabilities need their own qualified models, integrations and domain partners.

No. Start at the detail level that helps the decision: a well, inspection corridor, equipment assembly or asset graph. Existing CAD, scans or geospatial data can be assessed for rights, accuracy and web delivery. Use more visual detail where it improves identification, context or training.

Yes, the engagement can focus on the missing evidence-to-decision workflow rather than replacing your existing environment. We first identify systems of record, model interfaces and ownership. The agreed integration should preserve your asset identities and make changes and outcomes traceable.

A scoped agent can retrieve authorized evidence, invoke approved analytical tools, compare saved results and draft an explanation for review. Tool permissions and execution limits are explicit. The operational design also specifies refusal, missing evidence, failure recovery and when work returns to an engineer.

Require claims to reference the exact evidence and calculation revision. Check values, units, candidate identities and source support separately. Numerical checks can reject a wrong value; they cannot by themselves establish that a narrative conclusion is scientifically sound. Qualified review remains part of the acceptance plan.

We can assess your approved provider or model endpoint against the required tools, security, latency and evaluation criteria. EarthScan Twin has multi-provider adapter groundwork, but adapter availability is not a claim that every model is qualified. The deployed model and its budget are agreed explicitly.

The integration can submit approved jobs to a customer-selected execution service and retain inputs, versions, status and outputs. That needs scheduler access, job isolation, cancellation, storage and cost controls. The current EarthScan Twin reservoir example executes bounded local calculations; animated racks do not represent a connected remote HPC installation.

Its synthetic examples let users explore declared trade-offs. They do not establish field uplift or net environmental benefit. A customer claim needs suitable field data, model validation and uncertainty, plus an agreed accounting boundary covering the relevant water, energy, chemical and emissions consequences.

The workflow should show what is missing, preserve the failed attempt and prevent an unsupported conclusion from becoming an approved action. Define escalation and abstention rules, test difficult cases before rollout and monitor performance after deployment. A fluent answer is not an acceptance criterion.

Yes. Recorded evidence is a useful first scope: verify source files, align observations and navigation, review proposed findings and follow them through to a maintenance decision. This can deliver value before a live robot connection is justified.

We can scope integration with your robotics team or OEM using documented interfaces, mission records and supported sensors. Begin with evidence ingestion and supervised planning. Vehicle control, communications loss, emergency behavior and site authorization require separate validation; the EarthScan Twin mission is a simulation.

Accuracy is established on representative, held-out customer evidence with agreed defect definitions. Report false alarms, missed defects and performance by condition, not just one headline score. The recorded EarthScan Twin example illustrates evidence review and a bounded baseline; it is not a certified detector.

That is a practical integration target. A reviewed finding should retain its evidence, location, severity basis and owner when proposed to your maintenance system. Creation rights, duplicate prevention, status synchronization and closure evidence must be tested against the actual system before enabling the action.

Choose one accessible source: approved files, a historian extract, inspection media or a documented API. Discovery checks formats, identity, timestamps, units, spatial references, permissions and update rates. A source category on this page is not a promise of an already installed connector.

Start by making those limits visible. Preserve original records, separate missing values from zeros, quarantine incompatible inputs and show freshness. Data remediation can be the first deliverable. Unresolved gaps should narrow the conclusions and permitted actions rather than being silently filled.

No. Use an update cadence that matches the decision: an inspection campaign, daily review or lower-latency monitoring task. Agree freshness and failure behavior for each. Never present a historical replay as a live feed, or imply a browser view is a safety-critical control channel.

Our initial offer focuses on decision support, reviewed work and bounded software actions in AI, robotics and data workflows. Direct equipment actuation is a separate scope requiring the customer’s operating authority, control-system engineering and independently accepted safeguards. Public EarthScan Twin controls operate illustrative or synthetic cases.

These are architectures to assess against your security, connectivity, hardware and support requirements. ES360 describes private deployment options; each twin engagement still needs a concrete design and acceptance. An air-gapped deployment cannot depend on a public model API or a cloud-streamed renderer.

Agree ownership, permitted processing, retention, deletion, export and model-provider terms in the engagement. Design access around tenant, project and role, and test denied and expired access. Do not upload sensitive operational files to a public demonstration or send them in the initial enquiry.

Define a stale-data indication, recovery path, retry policy and safe stopping point for each workflow. Preserve the last accepted evidence and distinguish it from fresh state. An interrupted job should not appear completed, and a retry should not silently duplicate a work order or paid model call.

The implementation should retain original evidence references, transformations, model and tool versions, inputs, outputs and the human decision. Agree the export format and replay requirements early. A checksum helps detect changed content; it does not prove that a source or engineering interpretation is correct.

Yes. Define a bounded interface, data rights, units, identity mapping, versioning and a joint acceptance test. Partners can supply domain models, survey assets, robot interfaces or operating-system integrations while EarthScan connects the AI and evidence workflow. Commercial and support responsibilities are agreed explicitly.

Bring one recurring decision, the team that owns it, the current process and an inventory of available data and systems. A redacted sample can follow through an agreed channel. You do not need to prepare a complete facility model or share credentials to begin.

Agree a bounded working loop, representative evidence, an integration map, evaluation results, a user workflow and a decision on production readiness. The handover should include operating instructions, known limits, ownership and the unresolved work required for rollout. Deliverables are written into the engagement.

Scope depends on source access, integration maturity, model requirements, visual detail, security and the acceptance process. We estimate after discovery and separate implementation from model usage, compute, storage, rendering infrastructure and support. This guide makes no fixed delivery or savings promise.

Measure the current process first. Compare time to reviewed outcome, rework, evidence coverage, error rates and operating cost on comparable tasks. Agree who validates the result. Faster unreviewed output is not a substitute for a better engineering or operational outcome.

Run in parallel with the established process, resolve failures and train the actual users. Set owners for data, models, actions and support. Release only the accepted functions, with monitoring, rollback and a review cadence. Expansion follows evidence from the first working loop.

It is an option for demanding training, review or immersive environments. Its lighting and geometry tools can increase visual fidelity with suitable assets and hardware. Browser delivery through GPU streaming introduces a different operating model. We select the renderer for the task while retaining the same governed evidence and workflow underneath.

Choose an AI, robotics or data starting point below, copy the discussion brief and book a conversation or open an email draft. Tell us the decision you want to improve. We will use that to scope an EarthScan Twin engagement and the customer-specific operational loop. The guide itself submits no project data.