An AI intake form completion agent reads whatever the requester provides, such as a quote PDF, a pasted email or a vendor name, and proposes values for the form fields it can support.
A requester has filled only some form fields.
The agent reads the wording, attachments and vendor record.
Missing fields get proposed values with sources.
Anything inferred is shown as a suggestion.
The requester accepts or edits each proposed value.
The confirmed form moves to the next step.
It looks up known facts, like a vendor's legal name, payment terms on file and the requester's cost centre, rather than asking the requester to supply them.
Every pre-filled field is shown as a suggestion with its source, and the requester confirms or edits before submitting.
Long forms discourage honest use, and short forms leave procurement with too little to act on.
Requesters copy vendor names and amounts from a quote by hand and make small errors.
People do not know the right category, cost centre or legal entity, so they guess or leave blanks.
The same company appears under three spellings, which breaks later matching.
Requesters give up halfway and buy by credit card or ask a colleague instead.
Incomplete forms bounce back, adding days before any real review.
It reads the attached quote, the email text and any vendor name the requester has typed.
Vendor legal name, existing contract references and the requester's department are pulled from vendor records and the directory.
Each field gets a suggested value with a note of where it came from.
Fields it could not support are left empty and marked, with a hint on what to enter.
The requester reviews the form, edits anything wrong and submits it.
Bring a small sample and watch Flo work through it.
| Aspect | By hand | With the agent |
|---|---|---|
| Form effort | Requesters work through every field themselves. | Known fields are drafted from the request and attachment for them to check. |
| Wrong entries | Fields are filled with guesses to get past validation. | Proposed values show their source so guesses are visible. |
| Reuse of data | Vendor details are retyped each time. | Vendor details are read from the existing vendor record. |
| Abandoned forms | Long forms get left half done. | The requester confirms drafted fields instead of starting from blank. |
| Audit trail | Nobody knows which values the requester typed. | Each value is tagged as entered, extracted or suggested. |
| If | Then | Who decides |
|---|---|---|
| Quote attached shows a price and term | Propose amount and term from the quote | Requester confirms |
| Vendor already exists in the vendor record | Propose stored contact and payment details | Requester confirms they still apply |
| Field needs a cost centre the agent cannot determine | Leave it blank and ask | Requester or their manager supplies it |
| Requester entry conflicts with the attachment | Show both values side by side | Requester chooses the correct one |
| Field is a required declaration or attestation | Do not fill it | Requester completes it personally |
| Business reason is missing | Draft one from the request text and mark as a draft | Requester rewrites or accepts it |
Fields where a person confirms something, such as a conflict-of-interest statement, are never pre-filled. The requester completes them.
The form says one amount and the quote another. The agent shows both, with the quote page, and the requester picks.
Stored bank or contact data may be out of date. The agent marks vendor data with its last-update date for the requester to check.
Unconfirmed suggestions are not submitted as fact. The record stays in draft and the follow-up step reminds the requester.
Talk it through with the team. We will map it to your steps and show where a person stays in control.
List fields safe to draft, such as vendor name and term, and fields that never are.
Show drafted values to requesters and measure how often they edit them.
Add more fields as edit rates on existing ones stay low.
Sample completed forms to compare drafted values with source documents.
A low rate suggests the suggestions are accurate. A high rate on a single field points to a bad source or a field that people interpret differently.
Quick answers to what people ask most about intake form completion.
Book a demoIt is the work of filling in a purchase request form with the details procurement needs. An AI agent can pre-fill much of it from quotes and records, leaving the requester to confirm.
From what the requester provides, such as quotes and emails, and from records you already hold, like vendor details and the staff directory. Each suggestion shows its source.
No. The requester reviews and submits. The agent only prepares suggestions, so the person remains accountable for the content.
They are left empty and marked, with a prompt on what is needed. The agent does not invent a value to make the form look complete.
Request routing is the step that sends a purchase request to the right queue, team or owner once it has been captured.
→Duplicate request detection is the step that spots when the same purchase has been asked for more than once, by one person or by several teams.
→Request classification is the step that labels an incoming purchase request by what it is: software, services, hardware, a renewal or a one-off buy.
→Missing information follow-up is the step where gaps in a purchase request are identified and the requester is asked to fill them.
→Bring a small sample of real items. We will show what Flo handles on its own and where it hands over to your team.
