An AI supplier master data agent assembles each record from onboarding forms, documents and contracts, mapping every value to a defined field and noting where it came from.
Approved supplier details arrive from onboarding.
Agent checks mandatory fields and formats.
Agent compares with existing records.
Master data owner approves the creation or change.
The vendor master is created or updated.
Agent reports on incomplete records.
It checks completeness and consistency against your field rules, such as required fields by supplier type, valid country and currency codes and formats for numbers.
When a supplier or a user asks for a change, it prepares the proposed edit with before and after values and routes it for approval before the record is updated.
Master data errors travel into every downstream process, from purchase orders to payments, and are expensive to correct once spread.
The same supplier detail is held in contracts, forms, the finance system and emails, and they disagree.
Required fields are left blank to get an onboarding through.
Anyone can change a record, and nobody knows who altered what.
Dates, names and addresses are entered in many formats.
Fixing a record means finding every system that copied the old value.
Values are extracted from forms and documents and mapped to defined fields with their source.
Each field is checked for presence, format and consistency with related fields.
Where sources disagree, the agent shows both values and the evidence for each.
A complete record, or a change to one, is prepared with before and after values.
A data owner approves, and the change, approver and date are recorded.
Bring a small sample and watch Flo work through it.
| Aspect | By hand | With the agent |
|---|---|---|
| Entry | Records are keyed from emails. | Agent prepares the record from the onboarding data for approval. |
| Standards | Formats differ by who entered. | Agent applies agreed formats and flags exceptions. |
| Duplicates | Duplicates are found after payment. | Agent searches for likely matches before creation. |
| Changes | Edits are made without a trail. | Each change request shows old and new values and the approver. |
| Quality | Gaps are discovered by accident. | Agent reports missing or inconsistent fields. |
| If | Then | Who decides |
|---|---|---|
| a mandatory field is empty | hold the record and request it | Master data owner |
| a likely duplicate exists | stop creation and show the match | Master data owner |
| a bank detail changes | route to finance verification | AP manager |
| a format is outside the standard | propose the corrected value | Master data owner |
| the record is approved | prepare the write with all values shown | Master data owner |
| a field has not changed | leave it unchanged | Master data owner |
The agent lists the conflict by system. The data owner decides which is correct.
The agent notes one-off status and suggests an expiry. The owner decides whether to keep it.
The agent previews every change for approval as a group and flags outliers. The owner approves.
The agent asks for supporting evidence and routes it. The owner updates only after review.
Talk it through with the team. We will map it to your steps and show where a person stays in control.
Define fields and formats.
Have the agent prepare records for human entry.
Add duplicate and format checks.
Publish quality reports monthly.
It shows how much of the supplier base is ready for use without fixes. Track it by field to see which fields are weakest.
Quick answers to what people ask most about supplier master data.
Book a demoIt is the authoritative record of each supplier, including legal name, addresses, tax details, contacts and bank details. An AI agent can build and maintain it from approved sources while people approve the changes.
No. It proposes edits with before and after values and a data owner approves them before the record is updated.
It shows each value with its source and lets a person choose, then records the decision.
Master data is the record you want to maintain. Cleansing is the work of correcting and standardising existing records that have fallen short.
Supplier classification is the step of labelling each supplier by what it provides and what it is, such as spend category, industry code, legal type, diversity status or goods versus services.
→Supplier data cleansing is the work of finding and correcting errors, gaps and inconsistent formats in existing supplier records, such as misspelt names, outdated addresses and mixed date formats.
→Supplier tiering is the practice of grouping suppliers into levels, such as strategic, preferred or transactional, so that oversight matches importance and risk.
→Duplicate supplier detection is the step of finding supplier records that refer to the same business under different names, numbers or spellings.
→Bring a small sample of real items. We will show what Flo handles on its own and where it hands over to your team.
