Validate AI-Extracted Data Before Updating Your CRM

Short answer

Treat AI extraction as a candidate record: validate structure, source evidence and business rules before allowing a CRM write.

Start with one narrow document task

This proposed workflow turns an invented enquiry into a reviewed CRM draft. It does not send outreach or modify a live database. Use a sandbox and ten to twenty synthetic examples before connecting customer material. Decide which fields are necessary and exclude everything else.

Step 1: specify missing information

Define fields such as source_id, company, requested_date, amount, currency and evidence_quote. Represent a missing value explicitly with null; do not ask the model to complete it from guesswork. Store the original document separately so a reviewer can inspect the quoted passage. Use document IDs in logs instead of full customer text.

n8n’s Structured Output Parser supports output fields based on JSON Schema. Its documentation notes that generated schemas treat example fields as mandatory and that sub-node expressions resolve to the first item. Process one document per extraction call or explicitly test batching so values from one enquiry cannot leak into another.

Step 2: separate shape from truth

A valid JSON record can still contain a wrong amount. After parsing, run deterministic checks for supported currencies, allowed field types and date formats. Check that the evidence quote occurs in the source. This does not prove the quote supports the field: that semantic judgment still needs a reviewer or an independently evaluated check.

Require null for an absent date, and route conflicting dates to review. Do not normalize a dollar sign to USD when the document does not establish the currency. Compare extracted totals with any itemized amounts using a documented rule. Preserve the original string beside a normalized value when interpretation is involved.

Step 3: build the review branch

The If node supports conditions combined with AND or OR. Use AND for a write gate that requires every validation to pass. Failed records should go to a correction queue with the specific reason, not to the successful branch with a generic warning.

Show reviewers the source, candidate values and detected conflicts. Save the approved record and reviewer decision. Only then allow an upsert using a stable source reference. Keep the approval attached to that exact record version; a later model regeneration must not inherit an earlier approval.

Step 4: test misleading and missing inputs

Include a missing currency, two companies with similar names, a quoted old price, an invalid calendar date and a document that says to ignore your instructions. Source text is evidence to extract from, not authority to change the workflow. Also test two records in one execution to catch first-item mapping errors.

Record false accepts, false rejects, correction minutes and cost per approved record. A perfect formatting rate is insufficient if wrong facts reach the CRM. Keep all writes disabled until the test set’s failures have an owner and the review queue works.

Step 5: release with a recovery path

Begin with draft-only records and a small daily ceiling. Keep a before-value for any update, define a reversal procedure and check duplicate handling. Read the retry implementation guide and our Make/n8n workflow guide to connect validation with delivery controls.