An AI contract metadata extraction agent reads each contract and fills a defined set of fields, including parties, dates, term, renewal, notice, value, currency and governing law.
A contract lands in the repository intake.
The agent identifies the agreement type and parties.
Dates, term, value basis and notice period are read.
Each field gets a confidence level and a source clause.
A contract administrator confirms low-confidence fields.
Verified metadata populates the contract record.
It records where in the document each value was found, so any field can be checked.
It marks fields as found, inferred or missing and routes uncertain ones to a person.
A contract record is only as useful as its fields. Entering them by hand is slow and error-prone, so many repositories hold documents with blank or wrong data.
Backlogs mean records sit empty for months.
Effective date, signature date and start date get confused.
Fees sit across order forms, schedules and amendments.
Inconsistent entry breaks reporting and alerts.
A mistaken notice period misses a deadline.
Agree which fields matter and what each means, such as the difference between effective and signature dates.
Pull each field from the body, schedules and amendments, and record the source location.
Where documents disagree, apply the order of precedence and flag the conflict.
Mark each value as found, inferred or missing, with a reason for the doubtful ones.
Send uncertain fields to a reviewer and write confirmed values to the contract record.
Bring a small sample and watch Flo work through it.
| Aspect | By hand | With the agent |
|---|---|---|
| Data entry | Administrators key dates and names by hand. | Fields are extracted with the source clause shown. |
| Errors | Typed dates and names contain mistakes. | Extracted values are checked against the document text. |
| Backlog | Old contracts are never entered. | Bulk extraction works through the backlog in batches. |
| Trust | Nobody knows which fields were verified. | Each field shows confidence and who verified it. |
| Consistency | Field formats vary by person. | Dates and names follow one field standard. |
| If | Then | Who decides |
|---|---|---|
| a field value appears more than once with different values | flag the conflict and show both sources | Contract administrator |
| the effective date is not stated | mark as not found and suggest the signature date as a candidate | Contract administrator |
| the counterparty name differs from the vendor record | flag for matching | Procurement lead |
| extraction confidence is low | route to manual verification | Contract administrator |
| the term is described as evergreen | record it as ongoing and note the notice terms | Contract owner |
| notice period is defined in another document | note the reference and request that document | Contract administrator |
Day-month order is ambiguous. The agent flags uncertain dates and the administrator confirms.
The counterparty appears under a trading name. The agent proposes a match and procurement confirms.
Price sits in an attachment. The agent looks across attachments and notes where it found the value.
Edits on a scan are unreadable. The agent marks the area and an administrator reads it directly.
Talk it through with the team. We will map it to your steps and show where a person stays in control.
Agree the short list of mandatory fields and formats.
Compare agent output to hand-keyed records.
Set the confidence level that routes to a person.
Process older contracts in batches with sampling.
This shows how usable the repository is for alerts and reports. Low values mean decisions rely on missing data.
Quick answers to what people ask most about contract metadata extraction.
Book a demoIt is the process of reading contracts and capturing key facts, such as dates, parties, value and renewal terms, as structured data for search, alerts and reporting.
Renewal type, term end date, notice period, value and payment terms usually drive the most action. Your team can add others.
Each value carries a source and a confidence grade. Uncertain or conflicting fields go to a person to confirm.
It can read scanned documents once they are converted to text, though poor scans lead to more uncertain fields for review.
Contract summarization is the condensing of an agreement into a short statement of what it covers, what it costs, how long it lasts and what the main obligations and risks are.
→Contract classification is the labelling of each agreement by type, such as NDA, MSA, SOW, order form or amendment, and by business category and risk tier.
→Contract version comparison is the identification of changes between two drafts of the same agreement, such as the supplier's draft and your redline.
→Contract hierarchy mapping is the linking of related documents into a parent and child structure, such as an MSA with its order forms, SOWs, schedules and amendments.
→Bring a small sample of real items. We will show what Flo handles on its own and where it hands over to your team.
