Confirm supplier master-data fields before AI changes a construction record

AI can turn a supplier document into structured fields, but extraction confidence is not business approval. A contractor still needs to decide which legal entity, address, contact or purchasing value belongs in trusted master data. Enfin separates those concerns: models can help interpret information, while Alfie provides the governed construction execution layer with organisation context, permissions, a proposed action and confirmation.

Keep extracted evidence separate from trusted values

Store the source document according to the organisation’s document process and show each extracted value beside its source. A value such as a company number, email address or supplier reference should remain a proposal until a person verifies it. Unreadable or absent fields must stay unknown rather than being completed from a plausible pattern.

Do not let a newly uploaded document silently overwrite the existing supplier record. First compare the proposed value with the trusted value and identify whether the document is newer, authoritative for that field and intended for this supplier.

Resolve identity before individual fields

Match the supplier using more than its name. Compare company or VAT number, address, known contacts and existing documents. Similar trading names and group companies are common sources of wrong-record changes. If the document could belong to more than one entity, stop the update and ask for confirmation.

The current application supports governed supplier creation and updating as well as supplier-document review and import action families. That does not mean every document type or field can be accepted automatically.

Apply stronger checks to sensitive changes

Treat bank details, legal identity and tax identifiers differently from a telephone number or operational contact. Use an independent verification route defined by the contractor, and separate request, verification and approval where practical. Do not copy sensitive payment data into an unrestricted note merely to preserve the decision trail.

For purchasing fields, record unit, currency, supplier option, source date and commercial conditions. A quoted project price should not become a durable catalogue value without an explicit decision.

Review the proposed action in context

The reviewer should see the supplier, source, old value, proposed value and fields that remain unchanged. Check the active Enfin organisation and the user’s permission before confirmation. If one field is uncertain, reject or remove that field rather than approving the complete update for convenience.

Alfie executes the confirmed change; it does not independently establish supplier identity, contractual authority or bank-account ownership. This boundary is what makes AI assistance useful without allowing uncertain extraction to become silent master-data corruption.

Assess provider terms for the actual configuration.

Data handling depends on the product, account and contract in use. OpenAI states that data submitted to its business offerings and API is not used to train models by default. Anthropic likewise describes its role and commercial-data handling in its privacy documentation for commercial customers. These general statements do not replace a review of the selected service, retention settings, agreements and permitted data.

Enfin does not need to claim a particular model provider for this workflow. The operational control remains the same: minimise the supplied data, use an approved configuration, show proposed field changes and require an authorised person to confirm them.

Verify the record after execution.

Open the supplier record and compare the stored values with the approved proposal. Check that unrelated fields were preserved and no duplicate supplier was created. Monitor the first relevant use, especially a purchase or payment process, and stop if the result does not match the verified source.

See how Enfin connects construction administration, review pricing, or discuss your workflow with us.