An AI request category assignment agent reads the request description, vendor and quote, and chooses the best-fitting category and subcategory from the list your finance and procurement teams maintain.
A request needs a spend category.
The agent reads the description and vendor.
It finds the best category in the company's list.
Close categories are shown with reasons.
The buyer confirms or corrects the category.
The category feeds budgets and reports.
It records the choice with a one-line reason and a confidence level, and uses what it learns from past requests from the same vendor.
Low-confidence picks are marked for a person to confirm. The category list itself is never changed by the agent.
Category drives routing, approval rules and spend reporting, so a wrong label quietly distorts decisions that come later.
They select the first plausible option from a long dropdown.
A supplier of both hardware and services fits several categories.
Hundreds of categories are hard to remember, so people default to an 'other' bucket.
Spend in the wrong category makes category totals and savings targets unreliable.
When the taxonomy is updated, older requests keep outdated labels.
The agent looks at the description, quote lines, vendor and requester team.
It reviews how past purchases from the same vendor were categorised.
It chooses the category and subcategory that best fit the purchase itself, not only the vendor.
A short note and a confidence level are recorded with the choice.
Low-confidence assignments are marked so an analyst confirms them.
Bring a small sample and watch Flo work through it.
| Aspect | By hand | With the agent |
|---|---|---|
| Lookup | Requesters scroll a long category list and pick something close. | The agent proposes the best-fitting category from the request wording. |
| Consistency | Similar purchases land in different categories. | The same taxonomy and logic apply to every request. |
| Reports | Category spend reports are unreliable. | Cleaner categories at intake give cleaner reports later. |
| Taxonomy limits | Gaps in the list go unnoticed. | Requests that fit nowhere are logged as taxonomy gaps. |
| Review | Categories are corrected after the invoice. | Corrections happen at intake, with the reason noted. |
| If | Then | Who decides |
|---|---|---|
| Description matches one category clearly | Assign it with the matching words cited | Buyer sees it in the confirmed list |
| Two categories fit equally | Show both and explain the difference | Buyer chooses |
| Vendor's usual category differs from this purchase | Weigh the request text above the vendor history | Buyer confirms |
| No category fits | Mark as uncategorised and record the wording | Category manager decides whether to add one |
| Request has several items in different categories | Propose a split by line | Buyer decides on splitting |
| Category affects the approval path | Show the effect next to the category | Buyer confirms before routing |
A label like 'Services' is too broad. The agent proposes the narrowest category that fits and the buyer reviews it.
Categories are merged or renamed. The agent applies the new list from its start date and the category manager decides about history.
The project may span several kinds of spend. The agent proposes categories per line and the buyer confirms.
They know the purpose but not the taxonomy. The agent records their view and the buyer decides.
Talk it through with the team. We will map it to your steps and show where a person stays in control.
Agree one current taxonomy before assigning categories.
Compare the agent's choices with buyer choices for a set of requests.
Let strong matches pass and send ambiguous ones to a buyer.
Use the gap log and corrections to refine categories each quarter.
It shows how well the agent's choices match your team's view. A high rate for a particular category suggests its definition is unclear or overlaps another.
Quick answers to what people ask most about request category assignment.
Book a demoIt is putting a purchase request into the right spend category from your taxonomy. An AI agent can propose the category with a reason, and a person confirms any uncertain cases.
No. It chooses from the list your team maintains. If nothing fits it flags the request, and a person decides whether the list needs to change.
The agent flags it and suggests a split or a primary category. A person decides how it should be recorded.
It affects routing, approval rules and spend reporting. A wrong category can send a request to the wrong team and distort category totals.
Request cost estimation is the step of working out what a requested purchase will really cost over its term, not only the headline price.
→Approval routing is the step that decides who must approve a purchase and sends the request to them in the right order.
→Request escalation is the step that raises a purchase request to a more senior or different person when it stalls, breaches a rule or carries unusual risk.
→Approval chain configuration is the work of defining who approves which purchases, in what order and under what conditions.
→Bring a small sample of real items. We will show what Flo handles on its own and where it hands over to your team.
