An AI natural language spend query agent turns a question such as which vendors in a category grew most this quarter into a structured query over the spend data it can access.
A user types a spend question in everyday words.
Works out the measure, period, vendor or team asked about.
Asks a short clarifying question if terms are ambiguous.
Runs the question against approved spend records.
Returns figures with the filters and sources used.
The asker checks it and escalates odd results to finance.
It returns the answer as a short summary and a table, and states what it assumed, for example the period, currency and whether tax was included.
If the question is ambiguous, it asks a clarifying question first. Each result can be opened down to the invoices or POs behind it.
Spend questions normally go to an analyst, who builds a report, and by the time it arrives the meeting has moved on.
Business users cannot filter data themselves, so simple questions queue behind other work.
Two people asking about spend by team may use different date logic or exclusions.
Invoices, POs and contracts sit in separate places, so even a simple total needs joins.
A number in a slide without its source invites doubt and rework.
Identify the measure, grouping, filters and time period implied by the wording.
Ask a short follow-up where terms such as team, vendor group or year could mean more than one thing.
Retrieve the matching records from the spend data the user may see.
Return a summary and table with the filters and assumptions displayed.
Let the user drill into the underlying invoices or orders.
Bring a small sample and watch Flo work through it.
| Aspect | By hand | With the agent |
|---|---|---|
| Access | Spend answers wait on an analyst to build a report. | Users ask directly and get an answer with the filters used. |
| Definitions | Each report defines vendor, category and period differently. | Terms map to one agreed definition and the mapping is shown. |
| Follow-ups | A new angle means a new report request. | Follow-up questions refine the same query in the same session. |
| Trust | Numbers arrive with no trail of how they were built. | Every answer lists data source, filters and date range. |
| Analyst time | Analysts answer repeat questions all week. | Analysts handle only questions that need judgement. |
| If | Then | Who decides |
|---|---|---|
| The question names a term with several meanings | Ask which meaning is intended before answering | The asker |
| The period is not stated | Offer the default period and state it in the answer | The asker |
| The user lacks access to a data area | Decline that part and say who can grant access | Data owner |
| The data for the period is incomplete | Answer with a note on what is missing | Finance analyst |
| The question asks for a forecast or advice | Say it is outside the data and point to an analyst | Finance partner |
| The result looks unusual | Show the underlying lines and suggest a check | Finance analyst |
A vendor may appear under several names in the records. The agent groups known variants and shows the grouping so the asker can correct it.
Two teams may classify the same item differently. The agent uses the approved category and flags the other as an exception for the data owner.
Some spend may be restricted to certain roles. The agent applies the asker's access and never reveals hidden rows.
Spend may be held in several currencies or fiscal calendars. The agent states the conversion and calendar used so finance can verify.
Talk it through with the team. We will map it to your steps and show where a person stays in control.
Open the agent on one clean dataset and one group of users.
Analysts compare answers with their own reports and fix mappings.
Write the glossary the agent uses and share it with users.
Add data areas and users while keeping access rules in force.
It shows how many answers can be checked by the user. A figure below full coverage points to data that cannot be traced and should be fixed.
Quick answers to what people ask most about natural language spend query.
Book a demoIt is asking a question about spending in plain language and getting an answer from the underlying data. The user does not need to build a report or write a database query.
No. The agent respects the access rules of the person asking, so they see only what their role allows.
The agent shows the filters and assumptions it used and lets you open the records behind the figure. A person should still check anything that will inform a decision.
The answers will reflect it. Uncategorised or miscoded spend appears as such, and the agent can point out where gaps affect the result.
Procurement policy Q&A is answering employees' questions about purchasing rules, such as who must approve a purchase or when a quote is needed.
→A procurement chatbot is a conversational assistant that handles everyday purchasing questions and requests through chat.
→Procurement document search is finding the right contract, quote, invoice or approval record across the places they are stored.
→Procurement operations is the routine work that keeps purchasing running, including data upkeep, status chasing, document checks and reporting.
→Bring a small sample of real items. We will show what Flo handles on its own and where it hands over to your team.
