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MetraSlate

Equipment and business capabilities · Metrology examples

AI is not required. It is optional.

Run the operation through controlled records, calculations and workflows. Add model assistance where it helps, with a supported model and deployment approach suited to your data-control requirements.

Pilot / Early Access Asset by MetraSlate, AssetLight by MetraSlate and Standard by MetraSlate: Metrology Edition. How Evaluation Works

Optional and configuration-dependent. Confirm the model, information sent and hosting boundary before enabling assistance.

  1. 01Use the working foundation
  2. 02Choose the assistance
  3. 03Define the data boundary
  4. 04Inspect the result
  5. 05Keep the source context

Optional by design

Run without AI.

Your people use the workflows, records, calculations and permissions directly. An AI subscription or model is not required to operate the platform.

Organization policyPermitted data and actionsAccountable review
Model location is one part of a deployment’s data controls. The organization’s security, retention and operating policies govern the complete arrangement.

Standard's working foundation is its records, deterministic workflows, calculations, approvals and business controls. Equipment identity, customer requests, work progress, uncertainty, technical review and document release do not depend on a model answer. Organizations can adopt the platform on the strength of those capabilities and choose where optional assistance is useful.

Conversational assistance can help people work through a question in the application. The configured model returns a streamed answer, and the completed exchange retains which model answered and its reported usage. Attached record references identify the records named in a discussion; they do not give the model unrestricted access to the database or automatically authorize a business action.

A separate optional workflow can identify commitments in selected communication. It requests a defined result, checks the response against the extraction schema and retains its relationship to the source message. An unresolved person or a result below the configured confidence threshold waits for review. Eligible results can enter the commitment workflow with their source retained.

Supported OpenAI-compatible endpoints allow an appropriate local or privately operated model to participate in the architecture. Model and endpoint selection are configuration choices, with different arrangements for shared hosting and standalone deployments that can reach private-network services. This gives organizations with strict data-control requirements a concrete deployment question to resolve before enabling assistance.

The data boundary must include the text actually sent to the selected endpoint. Commitment extraction uses participant tokens, but the selected message body remains part of the request. Conversational assistance sends the exchange needed to answer. Organizations should select the model, endpoint and use cases according to the information they permit that service to process.

Private model choice addresses the model connection. Identity, mail, maps, calling, video, monitoring and backups each have their own configuration and service boundaries. No model automatically guarantees compliance, and a private model does not make every connected service private.

Choose assistance for a useful job.

The clearest starting points are bounded: help a person think through a question, or identify a commitment in communication they have selected for that purpose. The record and workflow supply the business context; the model supplies an answer or structured extraction that can be inspected.

Technical calculations and release authority retain their own deterministic and human responsibilities. Optional assistance does not replace the approved uncertainty method, a technician's signing responsibility or the laboratory's release decision.

Put the deployment boundary before the model name.

For an organization with strict data-control requirements, the first question is where the selected text may be processed. A private-network model needs a deployment that can reach it, while a shared hosted application has a different endpoint policy. A supported model protocol alone does not settle the hosting arrangement.

In everyday use

What your team can do.

Follow the record, the decision and the person responsible for the next step.

A complete deterministic foundation
Use controlled records, calculation logic and workflow decisions without requiring AI. Optional assistance adds to the operation rather than becoming a prerequisite for it.
Conversational assistance
Ask a question in context and retain the completed exchange with its model identity and reported usage. Referenced record names do not imply unrestricted record-content access.
Commitments with source context
Identify candidate commitments through a defined extraction workflow. Schema checks, source spans and configured review thresholds preserve an inspectable relationship to the original message.
Supported local and private models
Use an appropriate OpenAI-compatible endpoint, including supported locally operated runtimes. Reachability, hosting posture and the organization's approved network boundary determine the deployment.
Bounded provider access
Configured extraction calls use connection budgets, response limits, concurrency controls and endpoint-address policy. An unavailable or invalid provider result remains a visible outcome.
Strict data-control deployments
Select the model endpoint and permitted information, then examine the other connected services separately. Model selection supports a deployment design; it does not confer automatic compliance.

Related capabilities

Keep Following the Work.

A conversation about your work

See it with your own workflow in mind.

Start with your items, your business, and the processes you need to manage. We’ll help you identify the right product.

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